{"id":51525,"date":"2026-05-05T08:35:42","date_gmt":"2026-05-05T13:35:42","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/?page_id=51525"},"modified":"2026-05-05T13:24:27","modified_gmt":"2026-05-05T18:24:27","slug":"tcia-data-explorer","status":"publish","type":"page","link":"https:\/\/stage.cancerimagingarchive.net\/tcia-data-explorer\/","title":{"rendered":"TCIA Data Explorer"},"content":{"rendered":"<!-- Data Explorer CSS -->\r\n<style class=\"wpcode-css-snippet\">\/* ============================================================\r\n   TCIA Dataset Browser  \u00b7  Stylesheet\r\n   Multi-select pill dropdowns matching the Streamlit prototype\r\n   ============================================================ *\/\r\n\r\n@import url('https:\/\/fonts.googleapis.com\/css2?family=Inter:wght@400;500;600;700&display=swap');\r\n\r\n\/* \u2500\u2500 Variables \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-root {\r\n  --tcb-blue:        #1a5a8a;\r\n  --tcb-red:         #c0392b;\r\n  --tcb-blue-light:  rgba(26,90,138,.1);\r\n  --tcb-red-light:   rgba(192,57,43,.1);\r\n\r\n  --tcb-bg:          #f0f2f5;\r\n  --tcb-surface:     #ffffff;\r\n  --tcb-surface2:    #f7f9fb;\r\n  --tcb-border:      #dce2eb;\r\n  --tcb-border2:     #c3cdd9;\r\n  --tcb-text:        #182030;\r\n  --tcb-text2:       #445566;\r\n  --tcb-text3:       #8899aa;\r\n\r\n  --tcb-sidebar-w:   300px;\r\n  --tcb-radius:      8px;\r\n  --tcb-radius-sm:   5px;\r\n  --tcb-font:        'Inter', system-ui, sans-serif;\r\n  --tcb-t:           0.14s;\r\n  --tcb-ease:        cubic-bezier(.25,.46,.45,.94);\r\n\r\n  font-family: var(--tcb-font);\r\n  color: var(--tcb-text);\r\n  background: var(--tcb-bg);\r\n  border: none;\r\n  border-radius: 0;\r\n  overflow: visible;\r\n  box-shadow: none;\r\n}\r\n\r\n\/* \u2500\u2500 Layout \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-layout {\r\n  display: block; \/* was grid *\/\r\n  grid-template-columns: var(--tcb-sidebar-w) 1fr;\r\n  min-height: 600px;\r\n  align-items: start;  \/* critical: allows sidebar to be shorter than main *\/\r\n}\r\n\r\n\r\n\/* \u2500\u2500 Mobile bar \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-mobile-bar {\r\n  display: none;\r\n  align-items: center;\r\n  justify-content: space-between;\r\n  padding: 11px 16px;\r\n  background: var(--tcb-surface);\r\n  border-bottom: 1px solid var(--tcb-border);\r\n}\r\n.tcb-mobile-count { font-size: 12.5px; color: var(--tcb-text3); }\r\n\r\n.tcb-mobile-toggle {\r\n  display: inline-flex;\r\n  align-items: center;\r\n  gap: 7px;\r\n  padding: 7px 14px;\r\n  background: var(--tcb-surface);\r\n  border: 1.5px solid var(--tcb-border2);\r\n  border-radius: var(--tcb-radius-sm);\r\n  font: 500 13.5px var(--tcb-font);\r\n  color: var(--tcb-text2);\r\n  cursor: pointer;\r\n  transition: all var(--tcb-t);\r\n}\r\n.tcb-mobile-toggle:hover,\r\n.tcb-mobile-toggle[aria-expanded=\"true\"] {\r\n  border-color: var(--tcb-blue);\r\n  color: var(--tcb-blue);\r\n  background: var(--tcb-blue-light);\r\n}\r\n.tcb-mobile-badge {\r\n  background: var(--tcb-blue);\r\n  color: #fff;\r\n  border-radius: 50px;\r\n  font-size: 10px;\r\n  font-weight: 700;\r\n  min-width: 17px;\r\n  height: 17px;\r\n  padding: 0 5px;\r\n  display: inline-flex;\r\n  align-items: center;\r\n  justify-content: center;\r\n}\r\n.tcb-mobile-badge[hidden] { display: none; }\r\n\r\n\/* \u2500\u2500 Sidebar \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n\r\n.tcb-sidebar {\r\n  background: var(--tcb-surface);\r\n  border-right: 1px solid var(--tcb-border);\r\n  display: flex;\r\n  flex-direction: column;\r\n  position: fixed;\r\n  width: var(--tcb-sidebar-w);\r\n  height: 100vh;\r\n  overflow: visible;\r\n  align-self: flex-start;\r\n}\r\n\r\n.tcb-sidebar-head {\r\n  display: flex;\r\n  align-items: center;\r\n  justify-content: space-between;\r\n  padding: 20px 20px 16px;\r\n  flex-shrink: 0;\r\n}\r\n.tcb-sidebar-title {\r\n  font-size: 17px;\r\n  font-weight: 700;\r\n  color: var(--tcb-text);\r\n  margin: 0;\r\n}\r\n\r\n\/* \u2500\u2500 Clear all \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-clear-all {\r\n  background: none;\r\n  border: none;\r\n  cursor: pointer;\r\n  font: 500 13px var(--tcb-font);\r\n  color: var(--tcb-blue);\r\n  padding: 0;\r\n  transition: color var(--tcb-t);\r\n}\r\n.tcb-clear-all:hover { color: var(--tcb-red); text-decoration: underline; }\r\n.tcb-clear-all:disabled { color: var(--tcb-text3); cursor: default; text-decoration: none; }\r\n\r\n\/* \u2500\u2500 Search block \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-search-block {\r\n  padding: 0 16px 16px;\r\n  flex-shrink: 0;\r\n  border-bottom: 1px solid var(--tcb-border);\r\n}\r\n.tcb-field-label {\r\n  display: flex;\r\n  align-items: center;\r\n  gap: 6px;\r\n  font-size: 13px;\r\n  font-weight: 600;\r\n  color: var(--tcb-text2);\r\n  margin-bottom: 6px;\r\n}\r\n.tcb-help {\r\n  display: inline-flex;\r\n  align-items: center;\r\n  justify-content: center;\r\n  width: 16px;\r\n  height: 16px;\r\n  border-radius: 50%;\r\n  border: 1.5px solid var(--tcb-text3);\r\n  font-size: 10px;\r\n  color: var(--tcb-text3);\r\n  cursor: help;\r\n  flex-shrink: 0;\r\n}\r\n.tcb-search {\r\n  width: 100%;\r\n  box-sizing: border-box;\r\n  padding: 9px 12px;\r\n  border: 1.5px solid var(--tcb-border2);\r\n  border-radius: var(--tcb-radius-sm);\r\n  background: var(--tcb-surface);\r\n  font: 14px var(--tcb-font);\r\n  color: var(--tcb-text);\r\n  outline: none;\r\n  transition: border-color var(--tcb-t), box-shadow var(--tcb-t);\r\n}\r\n.tcb-search:focus {\r\n  border-color: var(--tcb-blue);\r\n  box-shadow: 0 0 0 3px rgba(26,90,138,.1);\r\n}\r\n\r\n\/* \u2500\u2500 Filters scrollable area \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-filters {\r\n  flex: 1;\r\n  overflow-y: auto;\r\n  min-height: 0;  \/* \u2190 this is the critical fix for flex scroll children *\/\r\n  scrollbar-width: thin;\r\n  scrollbar-color: var(--tcb-border) transparent;\r\n  padding-bottom: 20px;\r\n}\r\n.tcb-filters::-webkit-scrollbar { width: 4px; }\r\n.tcb-filters::-webkit-scrollbar-thumb { background: var(--tcb-border); border-radius: 4px; }\r\n\r\n\/* \u2500\u2500 Filter block \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-fblock {\r\n  padding: 14px 16px;\r\n  border-bottom: 1px solid var(--tcb-border);\r\n}\r\n.tcb-fblock:last-child { border-bottom: none; }\r\n\r\n.tcb-fblock-label {\r\n  font-size: 13px;\r\n  font-weight: 600;\r\n  color: var(--tcb-text2);\r\n  margin-bottom: 8px;\r\n  display: block;\r\n}\r\n\r\n\/* \u2500\u2500 Multi-select pill dropdown \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n\/* Matches the Streamlit screenshot: selected pills inside the box,\r\n   chevron + clear on the right, dropdown list below *\/\r\n.tcb-ms-wrap {\r\n  position: relative;\r\n}\r\n\r\n.tcb-ms-box {\r\n  display: flex;\r\n  align-items: flex-start;\r\n  flex-wrap: wrap;\r\n  gap: 5px;\r\n  min-height: 40px;\r\n  padding: 6px 64px 6px 8px;  \/* right padding for the icons *\/\r\n  border: 1.5px solid var(--tcb-border2);\r\n  border-radius: var(--tcb-radius-sm);\r\n  background: var(--tcb-surface);\r\n  cursor: pointer;\r\n  transition: border-color var(--tcb-t), box-shadow var(--tcb-t);\r\n  position: relative;\r\n  box-sizing: border-box;\r\n}\r\n.tcb-ms-box:focus-within,\r\n.tcb-ms-wrap.open .tcb-ms-box {\r\n  border-color: var(--tcb-blue);\r\n  box-shadow: 0 0 0 3px rgba(26,90,138,.1);\r\n}\r\n\r\n\/* Selected pills inside box *\/\r\n.tcb-ms-pill {\r\n  display: inline-flex;\r\n  align-items: center;\r\n  gap: 5px;\r\n  background: var(--tcb-blue);\r\n  color: #fff;\r\n  border-radius: 4px;\r\n  padding: 3px 8px;\r\n  font-size: 12.5px;\r\n  font-weight: 500;\r\n  white-space: nowrap;\r\n  max-width: 160px;\r\n  overflow: hidden;\r\n}\r\n.tcb-ms-pill span {\r\n  overflow: hidden;\r\n  text-overflow: ellipsis;\r\n  white-space: nowrap;\r\n}\r\n.tcb-ms-pill-x {\r\n  background: none;\r\n  border: none;\r\n  color: rgba(255,255,255,.8);\r\n  cursor: pointer;\r\n  font-size: 14px;\r\n  line-height: 1;\r\n  padding: 0;\r\n  flex-shrink: 0;\r\n  transition: color var(--tcb-t);\r\n}\r\n.tcb-ms-pill-x:hover { color: #fff; }\r\n\r\n\/* Placeholder text when nothing selected *\/\r\n.tcb-ms-placeholder {\r\n  font-size: 13.5px;\r\n  color: var(--tcb-text3);\r\n  align-self: center;\r\n  pointer-events: none;\r\n  user-select: none;\r\n}\r\n\r\n\/* Right-side icons (clear + chevron) *\/\r\n.tcb-ms-icons {\r\n  position: absolute;\r\n  right: 8px;\r\n  top: 50%;\r\n  transform: translateY(-50%);\r\n  display: flex;\r\n  align-items: center;\r\n  gap: 4px;\r\n}\r\n.tcb-ms-clear-btn {\r\n  background: none;\r\n  border: none;\r\n  cursor: pointer;\r\n  color: var(--tcb-text3);\r\n  font-size: 17px;\r\n  line-height: 1;\r\n  padding: 2px;\r\n  border-radius: 50%;\r\n  display: flex;\r\n  align-items: center;\r\n  justify-content: center;\r\n  transition: color var(--tcb-t), background var(--tcb-t);\r\n}\r\n.tcb-ms-clear-btn:hover { color: var(--tcb-text); background: var(--tcb-surface2); }\r\n.tcb-ms-clear-btn[hidden] { display: none; }\r\n.tcb-ms-chevron {\r\n  color: var(--tcb-text3);\r\n  transition: transform var(--tcb-t) var(--tcb-ease);\r\n  pointer-events: none;\r\n}\r\n.tcb-ms-wrap.open .tcb-ms-chevron { transform: rotate(180deg); }\r\n\r\n\/* Dropdown list *\/\r\n.tcb-ms-dropdown {\r\n  position: absolute;\r\n  top: calc(100% + 4px);\r\n  left: 0;\r\n  right: 0;\r\n  background: var(--tcb-surface);\r\n  border: 1.5px solid var(--tcb-border2);\r\n  border-radius: var(--tcb-radius-sm);\r\n  box-shadow: 0 6px 24px rgba(0,0,0,.12);\r\n  z-index: 200;\r\n  overflow: hidden;\r\n  display: none;\r\n  flex-direction: column;\r\n}\r\n.tcb-ms-wrap.open .tcb-ms-dropdown { display: flex; }\r\n\r\n\/* Search within dropdown *\/\r\n.tcb-ms-dsearch {\r\n  padding: 8px 10px;\r\n  border: none;\r\n  border-bottom: 1px solid var(--tcb-border);\r\n  font: 13px var(--tcb-font);\r\n  color: var(--tcb-text);\r\n  background: var(--tcb-surface2);\r\n  outline: none;\r\n  width: 100%;\r\n  box-sizing: border-box;\r\n}\r\n.tcb-ms-dsearch:focus { background: var(--tcb-surface); }\r\n\r\n\/* Option list *\/\r\n.tcb-ms-options {\r\n  list-style: none;\r\n  margin: 0;\r\n  padding: 4px 0;\r\n  max-height: 220px;\r\n  overflow-y: auto;\r\n  scrollbar-width: thin;\r\n  scrollbar-color: var(--tcb-border) transparent;\r\n}\r\n.tcb-ms-options::-webkit-scrollbar { width: 4px; }\r\n.tcb-ms-options::-webkit-scrollbar-thumb { background: var(--tcb-border); }\r\n\r\n.tcb-ms-option {\r\n  display: flex;\r\n  align-items: center;\r\n  gap: 9px;\r\n  padding: 7px 12px;\r\n  cursor: pointer;\r\n  font-size: 13.5px;\r\n  color: var(--tcb-text2);\r\n  transition: background var(--tcb-t);\r\n}\r\n.tcb-ms-option:hover { background: var(--tcb-surface2); }\r\n.tcb-ms-option.selected { background: rgba(26,90,138,.06); color: var(--tcb-text); }\r\n\r\n\/* Custom checkbox inside option *\/\r\n.tcb-ms-cb {\r\n  width: 15px;\r\n  height: 15px;\r\n  min-width: 15px;\r\n  border: 1.5px solid var(--tcb-border2);\r\n  border-radius: 3px;\r\n  background: var(--tcb-surface);\r\n  position: relative;\r\n  transition: background var(--tcb-t), border-color var(--tcb-t);\r\n  flex-shrink: 0;\r\n}\r\n.tcb-ms-option.selected .tcb-ms-cb {\r\n  background: var(--tcb-blue);\r\n  border-color: var(--tcb-blue);\r\n}\r\n.tcb-ms-option.selected .tcb-ms-cb::after {\r\n  content: '';\r\n  position: absolute;\r\n  left: 3px;\r\n  top: 0px;\r\n  width: 5px;\r\n  height: 9px;\r\n  border: 2px solid #fff;\r\n  border-top: none;\r\n  border-left: none;\r\n  transform: rotate(45deg);\r\n}\r\n.tcb-ms-opt-label { flex: 1; }\r\n.tcb-ms-opt-count {\r\n  font-size: 11px;\r\n  color: var(--tcb-text3);\r\n  flex-shrink: 0;\r\n}\r\n.tcb-ms-no-results {\r\n  padding: 10px 12px;\r\n  font-size: 13px;\r\n  color: var(--tcb-text3);\r\n  text-align: center;\r\n}\r\n\r\n\/* \u2500\u2500 Subjects number field \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-subjects-wrap {\r\n  display: flex;\r\n  gap: 8px;\r\n  align-items: center;\r\n}\r\n.tcb-subjects-input {\r\n  width: 100%;\r\n  box-sizing: border-box;\r\n  padding: 8px 10px;\r\n  border: 1.5px solid var(--tcb-border2);\r\n  border-radius: var(--tcb-radius-sm);\r\n  font: 14px var(--tcb-font);\r\n  color: var(--tcb-text);\r\n  background: var(--tcb-surface);\r\n  outline: none;\r\n  transition: border-color var(--tcb-t);\r\n}\r\n.tcb-subjects-input:focus { border-color: var(--tcb-blue); }\r\n.tcb-subjects-sep {\r\n  font-size: 13px;\r\n  color: var(--tcb-text3);\r\n  flex-shrink: 0;\r\n}\r\n\r\n\/* \u2500\u2500 Main content \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n\r\n\r\n.tcb-main {\r\n  padding: 20px 22px 32px;\r\n  min-height: 600px;\r\n  background: var(--tcb-bg);\r\n  margin-left: var(--tcb-sidebar-w);\r\n}\r\n\r\n\/* \u2500\u2500 Toolbar \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-toolbar {\r\n  display: flex;\r\n  align-items: center;\r\n  justify-content: space-between;\r\n  gap: 12px;\r\n  margin-bottom: 18px;\r\n  flex-wrap: wrap;\r\n}\r\n.tcb-count {\r\n  font-size: 13px;\r\n  color: var(--tcb-text3);\r\n  margin: 0;\r\n}\r\n.tcb-count strong { color: var(--tcb-text); font-weight: 600; }\r\n.tcb-sort-wrap { display: flex; align-items: center; gap: 8px; }\r\n.tcb-sort-label { font-size: 12.5px; color: var(--tcb-text3); }\r\n.tcb-sort {\r\n  padding: 6px 26px 6px 9px;\r\n  border: 1.5px solid var(--tcb-border);\r\n  border-radius: var(--tcb-radius-sm);\r\n  background: var(--tcb-surface)\r\n    url(\"data:image\/svg+xml,%3Csvg xmlns='http:\/\/www.w3.org\/2000\/svg' width='11' height='11' viewBox='0 0 24 24' fill='none' stroke='%238899aa' stroke-width='2.5'%3E%3Cpolyline points='6 9 12 15 18 9'\/%3E%3C\/svg%3E\")\r\n    no-repeat right 8px center;\r\n  appearance: none;\r\n  -webkit-appearance: none;\r\n  font: 13px var(--tcb-font);\r\n  color: var(--tcb-text);\r\n  cursor: pointer;\r\n  outline: none;\r\n  transition: border-color var(--tcb-t);\r\n}\r\n.tcb-sort:focus { border-color: var(--tcb-blue); }\r\n\r\n\/* \u2500\u2500 Grid: single column list (matches collection page style) \u2500 *\/\r\n.tcb-grid {\r\n  display: flex;\r\n  flex-direction: column;\r\n  gap: 16px;\r\n}\r\n\r\n\/* \u2500\u2500 Card \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-card {\r\n  background: var(--tcb-surface);\r\n  border: 1px solid var(--tcb-border);\r\n  border-radius: var(--tcb-radius);\r\n  overflow: hidden;\r\n  display: flex;\r\n  flex-direction: column;\r\n  transition: box-shadow var(--tcb-t), border-color var(--tcb-t);\r\n  animation: tcb-in .2s var(--tcb-ease) backwards;\r\n}\r\n@keyframes tcb-in {\r\n  from { opacity: 0; transform: translateY(5px); }\r\n  to   { opacity: 1; transform: translateY(0); }\r\n}\r\n.tcb-card:hover {\r\n  box-shadow: 0 4px 20px rgba(0,0,0,.09);\r\n  border-color: var(--tcb-border2);\r\n}\r\n\r\n\/* \u2500\u2500 Card header \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-card-header {\r\n  padding: 14px 16px 12px;\r\n}\r\n\r\n\/* \u2500\u2500 Type pill \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-type-pill {\r\n  display: inline-flex;\r\n  align-items: center;\r\n  gap: 6px;\r\n  font-size: 11px;\r\n  font-weight: 700;\r\n  letter-spacing: .07em;\r\n  text-transform: uppercase;\r\n  border-radius: 6px;\r\n  padding: 5px 12px;\r\n  color: #fff;\r\n  line-height: 1;\r\n  flex-shrink: 0;\r\n  float: right;\r\n}\r\n.tcb-type-pill.collection { background: #42b4e6; }\r\n.tcb-type-pill.analysis   { background: #d4531a; }\r\n\r\n\/* \u2500\u2500 Title \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-card-title {\r\n  font-size: 15px;\r\n  font-weight: 400;\r\n  color: var(--tcb-text);\r\n  margin: 0 0 9px;\r\n  line-height: 1.35;\r\n}\r\n.tcb-card-title a {\r\n  color: inherit;\r\n  text-decoration: none;\r\n  transition: color var(--tcb-t);\r\n}\r\n.tcb-card-title a:hover { color: var(--tcb-blue); }\r\n.tcb-card-title strong  { font-weight: 700; }\r\n.tcb-card-title-sep     { color: var(--tcb-text3); font-weight: 400; margin: 0 4px; }\r\n.tcb-card-title-full    { font-weight: 400; }\r\n\r\n\/* \u2500\u2500 Pill + DOI row \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-card-meta-row {\r\n  display: flex;\r\n  align-items: center;\r\n  gap: 12px;\r\n  flex-wrap: wrap;\r\n}\r\n.tcb-card-doi {\r\n\/*  display: inline-flex;*\/\r\n  align-items: center;\r\n\/*  gap: 5px;*\/\r\n  font-size: 12px;\r\n  color: var(--tcb-blue);\r\n  text-decoration: none;\r\n  transition: color var(--tcb-t);\r\n}\r\n.tcb-card-doi:hover { color: var(--tcb-red); text-decoration: underline; }\r\n.tcb-card-doi svg   { flex-shrink: 0; color: var(--tcb-text3); }\r\n\r\n\/* \u2500\u2500 Summary wrap \u2014 contains floated image + text \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-card-summary-wrap {\r\n  border-top: 1px solid var(--tcb-border);\r\n  padding: 12px 16px;\r\n  overflow: hidden; \/* contains the float *\/\r\n}\r\n\r\n\/* Featured image \u2014 floated right inside summary *\/\r\n.tcb-card-thumb {\r\n  float: right;\r\n  width: 110px;\r\n  height: 80px;\r\n  object-fit: cover;\r\n  border-radius: 5px;\r\n  margin: 0 0 10px 14px;\r\n  border: 1px solid var(--tcb-border);\r\n}\r\n\r\n\/* \u2500\u2500 Metadata table \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-card-table-wrap {\r\n  overflow-x: auto;\r\n  border-top: 1px solid var(--tcb-border);\r\n  -webkit-overflow-scrolling: touch;\r\n}\r\n.tcb-card-table {\r\n  width: 100%;\r\n  border-collapse: collapse;\r\n  font-size: 13px;\r\n}\r\n.tcb-card-table th {\r\n  background: var(--tcb-surface2);\r\n  font-weight: 600;\r\n  color: var(--tcb-text2);\r\n  padding: 8px 14px;\r\n  text-align: left;\r\n  white-space: nowrap;\r\n  border-right: 1px solid var(--tcb-border);\r\n  border-bottom: 1px solid var(--tcb-border);\r\n  font-size: 12.5px;\r\n}\r\n.tcb-card-table th:last-child { border-right: none; }\r\n.tcb-card-table td {\r\n  padding: 10px 14px;\r\n  color: var(--tcb-text2);\r\n  border-right: 1px solid var(--tcb-border);\r\n  vertical-align: top;\r\n  font-size: 13px;\r\n  line-height: 1.5;\r\n}\r\n.tcb-card-table td:last-child { border-right: none; }\r\n\r\n\/* \u2500\u2500 Summary text \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-card-summary {\r\n  font-size: 13.5px;\r\n  color: var(--tcb-text2);\r\n  line-height: 1.6;\r\n  margin: 0;\r\n  display: -webkit-box;\r\n  -webkit-line-clamp: 3;\r\n  -webkit-box-orient: vertical;\r\n  overflow: hidden;\r\n  max-height: calc(13.5px * 1.6 * 3);\r\n}\r\n\r\n\/* \u2500\u2500 Card footer \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-card-footer {\r\n  padding: 10px 18px 12px;\r\n  border-top: 1px solid var(--tcb-border);\r\n  display: flex;\r\n  align-items: center;\r\n  justify-content: space-between;\r\n  gap: 10px;\r\n  flex-wrap: wrap;\r\n  background: var(--tcb-surface2);\r\n}\r\n.tcb-footer-left {\r\n  display: flex;\r\n  align-items: center;\r\n  gap: 10px;\r\n  flex-wrap: wrap;\r\n}\r\n.tcb-footer-left .fas {\r\n  font-size: 12px;\r\n  vertical-align: middle;\r\n  position: relative;\r\n  top: -1px;\r\n  opacity: .7;\r\n}\r\n.tcb-footer-program {\r\n  \r\n  font-size: 12px;\r\n  font-weight: 500;\r\n  color: var(--tcb-text3);\r\n}\r\n.tcb-footer-license {\r\n  \r\n  font-size: 12px;\r\n  color: var(--tcb-text3);\r\n}\r\n.tcb-access-badge {\r\n  \r\n  font-size: 12px;\r\n  font-weight: 600;\r\n  border-radius: 3px;\r\n  padding: 3px 8px;\r\n}\r\n\r\n.tcb-footer-doi {\r\n  font-size: 12px;\r\n}\r\n\r\n.tcb-footer-doi a.tcb-card-doi {\r\n  color: var(--tcb-blue);\r\n  text-decoration: none;\r\n}\r\n.tcb-footer-doi a.tcb-card-doi:hover {\r\n  text-decoration: underline;\r\n}\r\n\r\n\r\n\r\n.tcb-access-badge.public  { background: rgba(13,158,118,.12); color: #0a7a5c; }\r\n.tcb-access-badge.limited { background: rgba(230,140,0,.12);  color: #a06000; }\r\n\r\n.tcb-card-links { display: flex; gap: 8px; flex-shrink: 0; }\r\n.tcb-btn {\r\n  display: inline-flex;\r\n  align-items: center;\r\n  gap: 4px;\r\n  padding: 6px 14px;\r\n  border-radius: 5px;\r\n  font: 500 12.5px var(--tcb-font);\r\n  text-decoration: none;\r\n  border: 1.5px solid;\r\n  cursor: pointer;\r\n  background: none;\r\n  transition: all var(--tcb-t);\r\n  white-space: nowrap;\r\n}\r\n.tcb-btn-primary {\r\n  background: #222;\r\n  border-color: #222;\r\n  color: #fff;\r\n}\r\n.tcb-btn-primary:hover { background: #444; border-color: #444; color: #fff; }\r\n.tcb-btn-primary.analysis { background: #222; border-color: #222; }\r\n.tcb-btn-outline {\r\n  border-color: var(--tcb-border2);\r\n  color: var(--tcb-text2);\r\n}\r\n.tcb-btn-outline:hover { border-color: var(--tcb-blue); color: var(--tcb-blue); }\r\n\r\n\/* \u2500\u2500 Empty state \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-empty {\r\n  display: flex;\r\n  flex-direction: column;\r\n  align-items: center;\r\n  gap: 12px;\r\n  padding: 80px 0;\r\n  text-align: center;\r\n  color: var(--tcb-text3);\r\n}\r\n.tcb-empty[hidden] { display: none; }\r\n.tcb-empty p { font-size: 14px; margin: 0; }\r\n.tcb-empty-clear {\r\n  padding: 9px 22px;\r\n  border: 1.5px solid var(--tcb-blue);\r\n  border-radius: var(--tcb-radius-sm);\r\n  font: 500 13px var(--tcb-font);\r\n  color: var(--tcb-blue);\r\n  cursor: pointer;\r\n  transition: background var(--tcb-t);\r\n  background: none;\r\n}\r\n.tcb-empty-clear:hover { background: var(--tcb-blue-light); }\r\n\r\n\/* \u2500\u2500 Pagination \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n.tcb-pagination {\r\n  display: flex;\r\n  justify-content: center;\r\n  align-items: center;\r\n  gap: 4px;\r\n  margin-top: 28px;\r\n  flex-wrap: wrap;\r\n}\r\n.tcb-pg-btn {\r\n  min-width: 34px;\r\n  height: 34px;\r\n  padding: 0 9px;\r\n  border: 1.5px solid var(--tcb-border);\r\n  border-radius: var(--tcb-radius-sm);\r\n  background: var(--tcb-surface);\r\n  font: 13px var(--tcb-font);\r\n  color: var(--tcb-text2);\r\n  cursor: pointer;\r\n  transition: all var(--tcb-t);\r\n}\r\n.tcb-pg-btn:hover:not(:disabled):not(.active) {\r\n  border-color: var(--tcb-blue);\r\n  color: var(--tcb-blue);\r\n}\r\n.tcb-pg-btn.active {\r\n  background: var(--tcb-blue);\r\n  border-color: var(--tcb-blue);\r\n  color: #fff;\r\n  font-weight: 600;\r\n}\r\n.tcb-pg-btn:disabled { opacity: .35; cursor: default; }\r\n.tcb-pg-ellipsis { color: var(--tcb-text3); padding: 0 3px; line-height: 34px; }\r\n\r\n\/* \u2500\u2500 Dark mode \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n@media (prefers-color-scheme: dark) {\r\n  .tcb-root {\r\n    --tcb-bg:       #0c1219;\r\n    --tcb-surface:  #141c28;\r\n    --tcb-surface2: #1a2436;\r\n    --tcb-border:   #222f42;\r\n    --tcb-border2:  #354a63;\r\n    --tcb-text:     #dce8f5;\r\n    --tcb-text2:    #8899bb;\r\n    --tcb-text3:    #445566;\r\n    --tcb-blue-light: rgba(26,90,138,.22);\r\n    --tcb-red-light:  rgba(192,57,43,.22);\r\n  }\r\n}\r\n\r\n\/* \u2500\u2500 Tablet \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n@media (max-width: 960px) {\r\n  .tcb-root { --tcb-sidebar-w: 260px; }\r\n}\r\n\r\n\/* \u2500\u2500 Mobile \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n@media (max-width: 680px) {\r\n  .tcb-mobile-bar { display: flex; }\r\n  .tcb-layout { display: block; }\r\n\r\n\r\n  .tcb-sidebar {\r\n    position: static;\r\n    max-height: 0;\r\n    overflow: hidden;\r\n    border-right: none;\r\n    border-bottom: 1px solid var(--tcb-border);\r\n    height: auto;\r\n    transition: max-height .35s var(--tcb-ease);\r\n  }\r\n\r\n    \r\n\/*\r\n  .tcb-sidebar {\r\n      background: var(--tcb-surface);\r\n      border-right: 1px solid var(--tcb-border);\r\n      display: flex;\r\n      flex-direction: column;\r\n      position: fixed;\r\n      top: 0;\r\n      width: var(--tcb-sidebar-w);\r\n      height: 100vh;\r\n      overflow: visible;\r\n      z-index: 100;\r\n    }\r\n*\/\r\n    \r\n  .tcb-sidebar.open {\r\n    max-height: 75vh;\r\n    overflow-y: auto;\r\n  }\r\n\r\n  .tcb-main {\r\n    padding: 14px 14px 24px;\r\n    margin-left: 0;\r\n  }\r\n  .tcb-card-thumb { width: 80px; height: 64px; }\r\n  .tcb-card-title { font-size: 14px; }\r\n  .tcb-toolbar { flex-direction: column; align-items: flex-start; }\r\n}\r\n<\/style>\r\n\r\n<!-- Data Explorer JS -->\r\n<script>\/**\r\n * TCIA Dataset Browser \u2014 Frontend JS\r\n *\r\n * Filter controls:\r\n *   - Multi-select pill dropdowns\r\n *   - Min\/max number inputs for Subjects\r\n *   - Full-text search across blob field\r\n *\r\n *\/\r\n(function () {\r\n  'use strict';\r\n\r\n  \/* \u2500\u2500 Boot \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n  function boot() { \r\n      document.querySelectorAll('.tcb-root').forEach(mount);\r\n      syncSidebarTop();\r\n  }\r\n    \r\n    \r\n\/\/function syncSidebarTop() {\r\n\/\/  const header  = document.querySelector('header#top .container');\r\n\/\/  const sidebar = document.querySelector('.tcb-sidebar');\r\n\/\/  if (!header || !sidebar) return;\r\n\/\/\r\n\/\/  const update = () => {\r\n\/\/    const h = header.getBoundingClientRect().bottom;\r\n\/\/    sidebar.style.top    = h + 'px';\r\n\/\/    sidebar.style.height = `calc(100vh - ${h}px)`;\r\n\/\/  };\r\n\/\/\r\n\/\/  update();\r\n\/\/  window.addEventListener('scroll', update, { passive: true });\r\n\/\/  window.addEventListener('resize', update, { passive: true });\r\n\/\/}\r\n    \r\n    function syncSidebarTop() {\r\n  const header  = document.querySelector('header#top .container');\r\n  const sidebar = document.querySelector('.tcb-sidebar');\r\n  if (!header || !sidebar) return;\r\n\r\n  const update = () => {\r\n    const h = header.getBoundingClientRect().bottom;\r\n    sidebar.style.top    = h + 'px';\r\n    sidebar.style.height = `calc(100vh - ${h}px)`;\r\n  };\r\n\r\n  \/\/ Run immediately\r\n  update();\r\n\r\n  \/\/ Watch for header resize (catches expand\/collapse transitions)\r\n  const ro = new ResizeObserver(update);\r\n  ro.observe(header);\r\n\r\n  \/\/ Also keep scroll + resize as fallbacks\r\n  window.addEventListener('scroll', update, { passive: true });\r\n  window.addEventListener('resize', update, { passive: true });\r\n}\r\n    \r\n  window.addEventListener('load', boot);\r\n\r\n  \/* \u2500\u2500 Mount \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n  function mount(root) {\r\n    const iid    = root.id;\r\n    const data   = readJSON(iid + '-data');\r\n    const config = readJSON(iid + '-cfg');\r\n    if (!data || !config) return;\r\n\r\n    const schema  = config.schema;\r\n    const perPage = config.perPage || 24;\r\n\r\n    \/* Active filter state *\/\r\n    const active = {\r\n      search:   '',\r\n      facets:   {},   \/\/ key \u2192 Set<string>\r\n      subjects: { min: '', max: '' },\r\n    };\r\n    schema.filters.forEach(f => { active.facets[f.key] = new Set(); });\r\n\r\n    \/* DOM refs *\/\r\n    const el = {\r\n      sidebar:      root.querySelector('.tcb-sidebar'),\r\n      mobileToggle: root.querySelector('.tcb-mobile-toggle'),\r\n      mobileBadge:  root.querySelector('.tcb-mobile-badge'),\r\n      mobileCount:  root.querySelector('.tcb-mobile-count'),\r\n      clearBtns:    root.querySelectorAll('.tcb-clear-all'),\r\n      search:       root.querySelector('.tcb-search'),\r\n      filtersEl:    root.querySelector('.tcb-filters'),\r\n      count:        root.querySelector('.tcb-count'),\r\n      sort:         root.querySelector('.tcb-sort'),\r\n      grid:         root.querySelector('.tcb-grid'),\r\n      empty:        root.querySelector('.tcb-empty'),\r\n      pagination:   root.querySelector('.tcb-pagination'),\r\n    };\r\n\r\n    let page    = 1;\r\n    let sortKey = 'title-asc';\r\n    let results = [];\r\n\r\n    \/* \u2500\u2500 Build sidebar \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n    const filterWidgets = {}; \/\/ key \u2192 { reset() }\r\n\r\n    schema.filters.forEach(f => {\r\n      const block = mk('div', 'tcb-fblock');\r\n      const label = mk('span', 'tcb-field-label');\r\n      label.textContent = f.label;\r\n      block.appendChild(label);\r\n\r\n      const widget = buildMultiSelect(f, active.facets[f.key], refresh);\r\n      block.appendChild(widget.el);\r\n      filterWidgets[f.key] = widget;\r\n      el.filtersEl.appendChild(block);\r\n    });\r\n\r\n    \/\/ Subjects min\/max inputs\r\n    {\r\n      const block = mk('div', 'tcb-fblock');\r\n      const label = mk('span', 'tcb-field-label');\r\n      label.textContent = 'Number of Subjects';\r\n      block.appendChild(label);\r\n\r\n      const wrap = mk('div', 'tcb-subjects-wrap');\r\n      const minIn = mk('input', 'tcb-subjects-input');\r\n      minIn.type = 'number'; minIn.min = '0'; minIn.placeholder = 'Min';\r\n      minIn.setAttribute('aria-label', 'Minimum subjects');\r\n      const sep = mk('span', 'tcb-subjects-sep');\r\n      sep.textContent = '\u2013';\r\n      const maxIn = mk('input', 'tcb-subjects-input');\r\n      maxIn.type = 'number'; maxIn.min = '0'; maxIn.placeholder = 'Max';\r\n      maxIn.setAttribute('aria-label', 'Maximum subjects');\r\n\r\n      let debounce;\r\n      [minIn, maxIn].forEach(inp => {\r\n        inp.addEventListener('input', () => {\r\n          clearTimeout(debounce);\r\n          debounce = setTimeout(() => {\r\n            active.subjects.min = minIn.value.trim();\r\n            active.subjects.max = maxIn.value.trim();\r\n            page = 1;\r\n            refresh();\r\n          }, 250);\r\n        });\r\n      });\r\n\r\n      wrap.appendChild(minIn); wrap.appendChild(sep); wrap.appendChild(maxIn);\r\n      block.appendChild(wrap);\r\n      el.filtersEl.appendChild(block);\r\n\r\n      filterWidgets['_subjects'] = {\r\n        reset() {\r\n          minIn.value = ''; maxIn.value = '';\r\n          active.subjects.min = ''; active.subjects.max = '';\r\n        }\r\n      };\r\n    }\r\n\r\n    \/* \u2500\u2500 Events \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n    el.mobileToggle?.addEventListener('click', () => {\r\n      const open = el.sidebar.classList.toggle('open');\r\n      el.mobileToggle.setAttribute('aria-expanded', open);\r\n    });\r\n\r\n    let searchTimer;\r\n    el.search.addEventListener('input', () => {\r\n      clearTimeout(searchTimer);\r\n      searchTimer = setTimeout(() => {\r\n        active.search = el.search.value.trim().toLowerCase();\r\n        page = 1;\r\n        refresh();\r\n      }, 160);\r\n    });\r\n\r\n    el.sort.addEventListener('change', () => { sortKey = el.sort.value; page = 1; refresh(); });\r\n\r\n    el.clearBtns.forEach(btn => btn.addEventListener('click', () => {\r\n      active.search = '';\r\n      el.search.value = '';\r\n      schema.filters.forEach(f => { active.facets[f.key] = new Set(); });\r\n      Object.values(filterWidgets).forEach(w => w.reset());\r\n      page = 1;\r\n      refresh();\r\n    }));\r\n\r\n    \/* Close dropdowns on outside click *\/\r\n    document.addEventListener('click', e => {\r\n      if (!root.contains(e.target)) {\r\n        root.querySelectorAll('.tcb-ms-wrap.open').forEach(w => w.classList.remove('open'));\r\n      }\r\n    });\r\n\r\n    \/* \u2500\u2500 Initial render \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n    refresh();\r\n\r\n    \/* \u2500\u2500 Core refresh \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n    function refresh() {\r\n      results = filterData(data, active);\r\n      sortData(results, sortKey);\r\n      renderGrid(el, results, page, perPage);\r\n      renderPagination(el, results, page, perPage, p => { page = p; refresh(); }, root);\r\n      renderCount(el, results, el.mobileCount);\r\n      updateClearBtns(el, active);\r\n      updateMobileBadge(el, active, root);\r\n    }\r\n  }\r\n\r\n  \/* \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\r\n     Multi-select pill dropdown widget\r\n  \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550 *\/\r\n  function buildMultiSelect(facet, selectedSet, onChange) {\r\n    const wrap = mk('div', 'tcb-ms-wrap');\r\n    wrap.dataset.key = facet.key;\r\n\r\n    \/* Box (shows selected pills + placeholder) *\/\r\n    const box = mk('div', 'tcb-ms-box');\r\n    box.setAttribute('role', 'combobox');\r\n    box.setAttribute('aria-haspopup', 'listbox');\r\n    box.setAttribute('aria-expanded', 'false');\r\n    box.setAttribute('tabindex', '0');\r\n\r\n    const placeholder = mk('span', 'tcb-ms-placeholder');\r\n    placeholder.textContent = 'Select\u2026';\r\n\r\n    const icons = mk('div', 'tcb-ms-icons');\r\n    const clearBtn = mk('button', 'tcb-ms-clear-btn');\r\n    clearBtn.type = 'button';\r\n    clearBtn.innerHTML = '&#x2715;';\r\n    clearBtn.setAttribute('aria-label', `Clear ${facet.label}`);\r\n    clearBtn.hidden = true;\r\n    const chevron = mk('span', 'tcb-ms-chevron');\r\n    chevron.innerHTML = '<svg width=\"12\" height=\"12\" viewBox=\"0 0 24 24\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2.5\"><polyline points=\"6 9 12 15 18 9\"\/><\/svg>';\r\n    icons.appendChild(clearBtn);\r\n    icons.appendChild(chevron);\r\n\r\n    box.appendChild(placeholder);\r\n    box.appendChild(icons);\r\n\r\n    \/* Dropdown *\/\r\n    const dropdown = mk('div', 'tcb-ms-dropdown');\r\n    dropdown.setAttribute('role', 'listbox');\r\n    dropdown.setAttribute('aria-multiselectable', 'true');\r\n    dropdown.setAttribute('aria-label', facet.label);\r\n\r\n    \/* Search within dropdown (only for lists > 8) *\/\r\n    let dsearch = null;\r\n    if (facet.options.length > 8) {\r\n      dsearch = mk('input', 'tcb-ms-dsearch');\r\n      dsearch.type = 'search';\r\n      dsearch.placeholder = `Search ${facet.label.toLowerCase()}\u2026`;\r\n      dsearch.setAttribute('aria-label', `Search ${facet.label} options`);\r\n      dropdown.appendChild(dsearch);\r\n    }\r\n\r\n    const optList = mk('ul', 'tcb-ms-options');\r\n    dropdown.appendChild(optList);\r\n\r\n    wrap.appendChild(box);\r\n    wrap.appendChild(dropdown);\r\n\r\n    \/* Render options *\/\r\n    let visibleOptions = facet.options.slice();\r\n\r\n    function renderOptions() {\r\n      optList.innerHTML = '';\r\n      if (!visibleOptions.length) {\r\n        const li = mk('li', 'tcb-ms-no-results');\r\n        li.textContent = 'No matches';\r\n        optList.appendChild(li);\r\n        return;\r\n      }\r\n      visibleOptions.forEach(opt => {\r\n        const li = mk('li', 'tcb-ms-option' + (selectedSet.has(opt.value) ? ' selected' : ''));\r\n        li.setAttribute('role', 'option');\r\n        li.setAttribute('aria-selected', selectedSet.has(opt.value));\r\n        li.dataset.value = opt.value;\r\n        li.innerHTML = `\r\n          <span class=\"tcb-ms-cb\"><\/span>\r\n          <span class=\"tcb-ms-opt-label\">${esc(opt.label)}<\/span>\r\n          <span class=\"tcb-ms-opt-count\">${opt.count ?? ''}<\/span>`;\r\n        li.addEventListener('mousedown', e => {\r\n          e.preventDefault(); \/\/ prevent box blur\r\n          toggleOption(opt.value, li);\r\n        });\r\n        optList.appendChild(li);\r\n      });\r\n    }\r\n\r\n    function toggleOption(value, li) {\r\n      if (selectedSet.has(value)) {\r\n        selectedSet.delete(value);\r\n        li.classList.remove('selected');\r\n        li.setAttribute('aria-selected', 'false');\r\n      } else {\r\n        selectedSet.add(value);\r\n        li.classList.add('selected');\r\n        li.setAttribute('aria-selected', 'true');\r\n      }\r\n      syncPills();\r\n      onChange();\r\n    }\r\n\r\n    \/* Sync pill display inside the box *\/\r\n    function syncPills() {\r\n      \/\/ Remove existing pills (not placeholder\/icons)\r\n      box.querySelectorAll('.tcb-ms-pill').forEach(p => p.remove());\r\n\r\n      if (selectedSet.size === 0) {\r\n        placeholder.style.display = '';\r\n        clearBtn.hidden = true;\r\n      } else {\r\n        placeholder.style.display = 'none';\r\n        clearBtn.hidden = false;\r\n        selectedSet.forEach(val => {\r\n          const pill = mk('span', 'tcb-ms-pill');\r\n          pill.innerHTML = `<span>${esc(val)}<\/span><button class=\"tcb-ms-pill-x\" type=\"button\" aria-label=\"Remove ${esc(val)}\">&#x2715;<\/button>`;\r\n          pill.querySelector('.tcb-ms-pill-x').addEventListener('mousedown', e => {\r\n            e.stopPropagation();\r\n            e.preventDefault();\r\n            selectedSet.delete(val);\r\n            syncPills();\r\n            renderOptions();\r\n            onChange();\r\n          });\r\n          \/\/ Insert before icons\r\n          box.insertBefore(pill, icons);\r\n        });\r\n      }\r\n    }\r\n\r\n    \/* Toggle dropdown open\/close *\/\r\n    function openDropdown() {\r\n      wrap.classList.add('open');\r\n      box.setAttribute('aria-expanded', 'true');\r\n      if (dsearch) { dsearch.focus(); dsearch.value = ''; visibleOptions = facet.options.slice(); renderOptions(); }\r\n    }\r\n    function closeDropdown() {\r\n      wrap.classList.remove('open');\r\n      box.setAttribute('aria-expanded', 'false');\r\n    }\r\n\r\n    box.addEventListener('click', e => {\r\n      if (e.target.closest('.tcb-ms-pill-x')) return;\r\n      wrap.classList.contains('open') ? closeDropdown() : openDropdown();\r\n    });\r\n    box.addEventListener('keydown', e => {\r\n      if (e.key === 'Enter' || e.key === ' ') { e.preventDefault(); wrap.classList.contains('open') ? closeDropdown() : openDropdown(); }\r\n      if (e.key === 'Escape') closeDropdown();\r\n    });\r\n\r\n    clearBtn.addEventListener('mousedown', e => {\r\n      e.stopPropagation();\r\n      e.preventDefault();\r\n      selectedSet.clear();\r\n      syncPills();\r\n      renderOptions();\r\n      onChange();\r\n    });\r\n\r\n    \/* Dropdown inner search *\/\r\n    if (dsearch) {\r\n      dsearch.addEventListener('input', () => {\r\n        const q = dsearch.value.trim().toLowerCase();\r\n        visibleOptions = q ? facet.options.filter(o => o.label.toLowerCase().includes(q)) : facet.options.slice();\r\n        renderOptions();\r\n      });\r\n      dsearch.addEventListener('keydown', e => { if (e.key === 'Escape') closeDropdown(); });\r\n    }\r\n\r\n    renderOptions();\r\n\r\n    return {\r\n      el: wrap,\r\n      reset() {\r\n        selectedSet.clear();\r\n        visibleOptions = facet.options.slice();\r\n        syncPills();\r\n        renderOptions();\r\n      }\r\n    };\r\n  }\r\n\r\n  \/* \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\r\n     Filter engine\r\n  \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550 *\/\r\n  function filterData(data, active) {\r\n    return data.filter(d => {\r\n      \/* Text search *\/\r\n      if (active.search && !d.blob.includes(active.search)) return false;\r\n\r\n      \/* Facets *\/\r\n      for (const [key, set] of Object.entries(active.facets)) {\r\n        if (!set.size) continue;\r\n        const val = d[key];\r\n        let hit = false;\r\n        if (Array.isArray(val)) {\r\n          hit = [...set].some(s => val.some(v => v.toLowerCase() === s.toLowerCase()));\r\n        } else if (typeof val === 'string') {\r\n          hit = [...set].some(s => val.toLowerCase() === s.toLowerCase());\r\n        }\r\n        if (!hit) return false;\r\n      }\r\n\r\n      \/* Subjects range *\/\r\n      const subj = d.subjects ?? 0;\r\n      if (active.subjects.min !== '' && subj < parseInt(active.subjects.min, 10)) return false;\r\n      if (active.subjects.max !== '' && subj > parseInt(active.subjects.max, 10)) return false;\r\n\r\n      return true;\r\n    });\r\n  }\r\n\r\n  function sortData(arr, key) {\r\n    \/\/ Use short_title for alphabetical sort (matches what users see on cards)\r\n    const lbl = d => (d.short_title || d.title || '').toLowerCase();\r\n    arr.sort((a, b) => {\r\n      switch (key) {\r\n        case 'title-asc':     return lbl(a).localeCompare(lbl(b));\r\n        case 'title-desc':    return lbl(b).localeCompare(lbl(a));\r\n        case 'subjects-desc': return (b.subjects || 0) - (a.subjects || 0);\r\n        case 'date-desc':\r\n          return new Date(b.date_updated || '1970-01-01') - new Date(a.date_updated || '1970-01-01');\r\n        case 'type-col': return a.type === 'collection' ? -1 : 1;\r\n        case 'type-ana': return a.type === 'analysis'   ? -1 : 1;\r\n        default: return 0;\r\n      }\r\n    });\r\n  }\r\n\r\n  \/* \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\r\n     Render\r\n  \u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550\u2550 *\/\r\n  function renderGrid(el, results, page, perPage) {\r\n    const start = (page - 1) * perPage;\r\n    const slice = results.slice(start, start + perPage);\r\n    el.empty.hidden = results.length > 0;\r\n    el.grid.innerHTML = '';\r\n    slice.forEach((d, i) => el.grid.appendChild(buildCard(d, i)));\r\n  }\r\n\r\n  function buildCard(d, idx) {\r\n    const isCollection = d.type === 'collection';\r\n    const card = mk('div', 'tcb-card');\r\n    card.setAttribute('role', 'listitem');\r\n    card.style.animationDelay = Math.min(idx * 22, 280) + 'ms';\r\n\r\n    \/\/ \u2500\u2500 SVG icons for the type pills \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\r\n    \/\/ Collection: grid of 4 squares (\u229e)\r\n    const gridIcon = `<svg width=\"13\" height=\"13\" viewBox=\"0 0 16 16\" fill=\"currentColor\" aria-hidden=\"true\">\r\n      <rect x=\"1\" y=\"1\" width=\"6\" height=\"6\" rx=\"1\"\/><rect x=\"9\" y=\"1\" width=\"6\" height=\"6\" rx=\"1\"\/>\r\n      <rect x=\"1\" y=\"9\" width=\"6\" height=\"6\" rx=\"1\"\/><rect x=\"9\" y=\"9\" width=\"6\" height=\"6\" rx=\"1\"\/>\r\n    <\/svg>`;\r\n    \/\/ Analysis result: tag\/price-tag shape\r\n    const tagIcon = `<svg width=\"13\" height=\"13\" viewBox=\"0 0 24 24\" fill=\"currentColor\" aria-hidden=\"true\">\r\n      <path d=\"M21.41 11.58l-9-9A2 2 0 0011 2H4a2 2 0 00-2 2v7a2 2 0 00.59 1.42l9 9a2 2 0 002.82 0l7-7a2 2 0 000-2.84zM6.5 8a1.5 1.5 0 110-3 1.5 1.5 0 010 3z\"\/>\r\n    <\/svg>`;\r\n\r\n    let html = '';\r\n\r\n    \/\/ \u2500\u2500 Card header: title \u2192 pill + DOI row \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\r\n    html += `<div class=\"tcb-card-header\">`;\r\n    \r\n    html += `<div>\r\n      <span class=\"tcb-type-pill ${isCollection ? 'collection' : 'analysis'}\">\r\n        ${isCollection ? gridIcon : tagIcon}\r\n        ${isCollection ? 'IMAGE COLLECTION' : 'ANALYSIS RESULT'}\r\n      <\/span>\r\n    <\/div>`;\r\n\r\n    \/\/ Short title bold | Full title normal weight\r\n    const titleHtml = (d.short_title && d.short_title !== d.title)\r\n      ? `<strong>${esc(d.short_title)}<\/strong> <span class=\"tcb-card-title-sep\">|<\/span> <span class=\"tcb-card-title-full\">${esc(d.title)}<\/span>`\r\n      : `${esc(d.title)}`;\r\n    html += `<h3 class=\"tcb-card-title\"><a href=\"${esc(d.url || '#')}\">${titleHtml}<\/a><\/h3>`;\r\n\r\n    \r\n\r\n\r\n    html += `<\/div>`; \/\/ .tcb-card-header\r\n\r\n    \/\/ \u2500\u2500 Metadata table (matching the collection page layout) \u2500\u2500\r\n    \/\/ Build rows only for fields that have values\r\n    const tableRows = [];\r\n\r\n    if (d.cancer_types?.length)\r\n      tableRows.push({ label: 'Cancer Types',        val: d.cancer_types.join(', ') });\r\n    if (d.cancer_locations?.length)\r\n      tableRows.push({ label: 'Location',           val: d.cancer_locations.join(', ') });\r\n    \/\/if (d.species?.length)\r\n      \/\/tableRows.push({ label: 'Species',             val: d.species.join(', ') });\r\n    if (d.subjects > 0)\r\n      tableRows.push({ label: 'Subjects',            val: d.subjects.toLocaleString() });\r\n    if (d.data_types?.length)\r\n      tableRows.push({ label: 'Data Types',          val: d.data_types.join(', ') });\r\n    if (d.supporting_data?.length)\r\n      tableRows.push({ label: 'External Resources',  val: d.supporting_data.join(', ') });\r\n    if (d.status)\r\n      tableRows.push({ label: 'Status',              val: d.status });\r\n    if (d.date_updated)\r\n      tableRows.push({ label: 'Updated',             val: d.date_updated });\r\n\r\n    if (tableRows.length) {\r\n      html += `<div class=\"tcb-card-table-wrap\"><table class=\"tcb-card-table\">\r\n        <thead><tr>${tableRows.map(r => `<th>${esc(r.label)}<\/th>`).join('')}<\/tr><\/thead>\r\n        <tbody><tr>${tableRows.map(r => `<td>${esc(r.val)}<\/td>`).join('')}<\/tr><\/tbody>\r\n      <\/table><\/div>`;\r\n    }\r\n\r\n    \/\/ \u2500\u2500 Summary text with featured image floated right \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\r\n    if (d.summary || (d.image && d.image !== false)) {\r\n      html += `<div class=\"tcb-card-summary-wrap\">`;\r\n      if (d.image && d.image !== false) {\r\n        html += `<img decoding=\"async\" class=\"tcb-card-thumb\" src=\"${esc(d.image)}\" alt=\"${esc(d.short_title || d.title)}\" loading=\"lazy\">`;\r\n      }\r\n      if (d.summary) {\r\n        html += `<p class=\"tcb-card-summary\">${esc(d.summary)}<\/p>`;\r\n      }\r\n      html += `<\/div>`;\r\n    }\r\n\r\n    \/\/ \u2500\u2500 Footer: program \u00b7 access \u00b7 action buttons \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\r\n    html += `<div class=\"tcb-card-footer\">`;\r\n\r\n    const doiUrl = d.doi ? `https:\/\/doi.org\/${d.doi}` : '';\r\n    const footerLeft = [];\r\n    if (doiUrl) footerLeft.push(`<span class=\"tcb-footer-doi\"><i class=\"fas fa-compass\" aria-hidden=\"true\"><\/i> <a class=\"tcb-card-doi\" href=\"${esc(doiUrl)}\" target=\"_blank\" rel=\"noopener noreferrer\">DOI: ${esc(d.doi)}<\/a><\/span>`);\r\n    if (d.program)  footerLeft.push(`<span class=\"tcb-footer-program\"><i class=\"fas fa-sitemap\" aria-hidden=\"true\"><\/i> ${esc(d.program)}<\/span>`);\r\n    if (d.access) {\r\n      const isLimited = (d.access||'').toLowerCase() === 'limited';\r\n      footerLeft.push(`<span class=\"tcb-access-badge ${isLimited ? 'limited' : 'public'}\"><i class=\"fas fa-lock\" aria-hidden=\"true\"><\/i> ${esc(d.access)}<\/span>`);\r\n    }\r\n    if (d.license)  footerLeft.push(`<span class=\"tcb-footer-license\"><i class=\"fas fa-certificate\" aria-hidden=\"true\"><\/i> ${esc(d.license)}<\/span>`);\r\n\r\n\r\n    html += `<div class=\"tcb-footer-left\">${footerLeft.join('')}<\/div>`;\r\n    html += `<div class=\"tcb-card-links\">`;\r\n\r\n    \/\/ Browse Images button \u2014 commented out for now, revisit later\r\n    \/\/ if (d.search_url) {\r\n    \/\/   html += `<a class=\"tcb-btn tcb-btn-outline\" href=\"${esc(d.search_url)}\"\r\n    \/\/               target=\"_blank\" rel=\"noopener noreferrer\">Browse Images<\/a>`;\r\n    \/\/ }\r\n    if (d.url) {\r\n      html += `<a class=\"tcb-btn tcb-btn-primary ${isCollection ? '' : 'analysis'}\"\r\n                  href=\"${esc(d.url)}\">View<\/a>`;\r\n    }\r\n\r\n    html += `<\/div><\/div>`; \/\/ .tcb-card-links .tcb-card-footer\r\n\r\n    card.innerHTML = html;\r\n    return card;\r\n  }\r\n\r\n  \/* \u2500\u2500 Count \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n  function renderCount(el, results, mobileCount) {\r\n    const n = results.length;\r\n    el.count.innerHTML = `<strong>${n.toLocaleString()}<\/strong> dataset${n !== 1 ? 's' : ''}`;\r\n    if (mobileCount) mobileCount.textContent = `${n.toLocaleString()} datasets`;\r\n  }\r\n\r\n  \/* \u2500\u2500 Clear buttons \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n  function updateClearBtns(el, active) {\r\n    const hasAny = active.search\r\n      || Object.values(active.facets).some(s => s.size > 0)\r\n      || active.subjects.min !== ''\r\n      || active.subjects.max !== '';\r\n    el.clearBtns.forEach(b => { b.disabled = !hasAny; });\r\n  }\r\n\r\n  \/* \u2500\u2500 Mobile badge \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n  function updateMobileBadge(el, active, root) {\r\n    let n = active.search ? 1 : 0;\r\n    Object.values(active.facets).forEach(s => { n += s.size; });\r\n    if (active.subjects.min || active.subjects.max) n++;\r\n    if (el.mobileBadge) {\r\n      el.mobileBadge.textContent = n || '';\r\n      el.mobileBadge.hidden = n === 0;\r\n    }\r\n  }\r\n\r\n  \/* \u2500\u2500 Pagination \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n  function renderPagination(el, results, page, perPage, onPage, root) {\r\n    const total = Math.ceil(results.length \/ perPage);\r\n    el.pagination.innerHTML = '';\r\n    if (total <= 1) return;\r\n\r\n    const btn = (label, target, disabled, active) => {\r\n      const b = mk('button', 'tcb-pg-btn' + (active ? ' active' : ''));\r\n      b.textContent = label;\r\n      b.disabled    = disabled;\r\n      b.setAttribute('aria-label', `Page ${target}`);\r\n      b.addEventListener('click', () => {\r\n        onPage(target);\r\n        root.scrollIntoView({ behavior: 'smooth', block: 'start' });\r\n      });\r\n      return b;\r\n    };\r\n\r\n    const ellipsis = () => {\r\n      const s = mk('span', 'tcb-pg-ellipsis'); s.textContent = '\u2026'; return s;\r\n    };\r\n\r\n    el.pagination.appendChild(btn('\u2039', page - 1, page === 1, false));\r\n\r\n    const pages = pageRange(page, total);\r\n    let prev = null;\r\n    pages.forEach(p => {\r\n      if (prev !== null && p - prev > 1) el.pagination.appendChild(ellipsis());\r\n      el.pagination.appendChild(btn(p, p, false, p === page));\r\n      prev = p;\r\n    });\r\n\r\n    el.pagination.appendChild(btn('\u203a', page + 1, page === total, false));\r\n  }\r\n\r\n  function pageRange(cur, total) {\r\n    if (total <= 7) return Array.from({ length: total }, (_, i) => i + 1);\r\n    const l = Math.max(2, cur - 1), r = Math.min(total - 1, cur + 1);\r\n    const mid = [];\r\n    for (let i = l; i <= r; i++) mid.push(i);\r\n    return [1, ...mid, total];\r\n  }\r\n\r\n  \/* \u2500\u2500 Utilities \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500 *\/\r\n  function readJSON(id) {\r\n    const el = document.getElementById(id);\r\n    if (!el) return null;\r\n    try { return JSON.parse(el.textContent); } catch { return null; }\r\n  }\r\n  function mk(tag, cls) {\r\n    const e = document.createElement(tag);\r\n    if (cls) e.className = cls;\r\n    return e;\r\n  }\r\n  function esc(s) {\r\n    return String(s ?? '').replace(\/&\/g,'&amp;').replace(\/<\/g,'&lt;')\r\n      .replace(\/>\/g,'&gt;').replace(\/\"\/g,'&quot;').replace(\/'\/g,'&#39;');\r\n  }\r\n\r\n})();\r\n<\/script>\r\n\r\n<!-- Data Explorer PHP -->\r\n<div id=\"tcb-6a1bf86e65251\" class=\"tcb-root\"\r\n     style=\"--tcb-blue:#1a5a8a;--tcb-red:#d4531a\">\r\n\r\n    <script type=\"application\/json\" id=\"tcb-6a1bf86e65251-data\">[{\"id\":42113,\"type\":\"collection\",\"slug\":\"4d-lung\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/4d-lung\\\/\",\"title\":\"Data from 4D Lung Imaging of NSCLC Patients\",\"short_title\":\"4D-Lung\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.ELN8YGLE\",\"summary\":\"This data collection consists of images acquired during chemoradiotherapy of 20 locally-advanced, non-small cell lung cancer patients. The images include four-dimensional (4D) fan beam (4D-FBCT) and 4D cone beam CT (4D-CBCT). All patients underwent concurrent radiochemotherapy to a total dose of 64.8-70\\u00a0Gy using daily 1.8 or 2\\u00a0Gy fractions.\\u00a04D-FBCT images were acquired\\u00a0on a 16-slice helical CT scanner (Brilliance Big Bore, Philips Medical Systems, Andover, MA) as respiration-correlated CTs with 10 breathing phases (0 to 90%, phase-based binning) and 3 mm slice thickness. 4D-FBCT images were acquired during simulation, prior to therapy, and used for therapy planning. In 14 of the 20 subjects, 4D-FBCTs were also acquired on the same scanner weekly during therapy.\\u00a04D-CBCT images were acquired\\u00a0on a commercial CBCT scanner (On-Board Imager\\u2122, Varian Medical Systems, Inc.). An external surrogate (Real-time Position Management, Varian Medical Systems, Inc.) was integrated into the CBCT acquisition system to stamp each CBCT projection with the surrogate respiratory signal through in-house software and hardware tools. Approximately 2500 projections were acquired over a period of 8-10 minutes in half-fan mode with half bow-tie filter. The technique was 125 kVp, 20 mA, and 20 ms in a single 360\\u00b0 slow gantry arc. Using the external surrogate, the CBCT projections were sorted into 10 breathing phases (0 to 90%, phase-based binning) and reconstructed with an in-house FDK reconstruction algorithm.Audio-visual biofeedback was performed for all 4D-FBCT and 4D-CBCT acquisitions in all subjects.\\u00a0A single Radiation Oncologist delineated targets and organs at risk in all 4D-FBCT and a limited number of 4D-CBCT images, on all 10 phases per scan.\\u00a0Seven of the subjects had gold coils implanted as fiducial markers in or near the tumor.\\u00a0The dataset is most fully described in detail in Balik et al.1\\u00a0 Seven of the subjects had gold coils implanted as fiducial markers in or near the tumor. The implantation procedure and details of marker location are described in detail in Roman et al.2References1 S. Balik et al., \\u201cEvaluation of 4-Dimensional Computed Tomography to 4-Dimensional Cone-Beam Computed Tomography Deformable Image Registration for Lung Cancer Adaptive Radiation Therapy.\\u201d Int. J. Radiat. Oncol. Biol. Phys.\\u00a086, 372\\u20139 (2013) PMCID: PMC3647023.2 N.O. Roman, W. Shepherd, N. Mukhopadhyay, G.D. Hugo, and E. Weiss, \\u201cInterfractional Positional Variability of Fiducial Markers and Primary Tumors in Locally Advanced Non-Small-Cell Lung Cancer during Audiovisual Biofeedback Radiotherapy.\\u201d Int. J. Radiat. Oncol. Biol. Phys.\\u00a083, 1566\\u201372 (2012).\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Non-small Cell Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"RTSTRUCT\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":20,\"date_updated\":\"2016-10-19\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=4D-Lung\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/doiJNLP-Y71kLpdQ.tcia\",\"blob\":\"data from 4d lung imaging of nsclc patients 4d-lung this data collection consists of images acquired during chemoradiotherapy of 20 locally-advanced, non-small cell lung cancer patients. the images include four-dimensional (4d) fan beam (4d-fbct) and 4d cone beam ct (4d-cbct). all patients underwent concurrent radiochemotherapy to a total dose of 64.8-70\\u00a0gy using daily 1.8 or 2\\u00a0gy fractions.\\u00a04d-fbct images were acquired\\u00a0on a 16-slice helical ct scanner (brilliance big bore, philips medical systems, andover, ma) as respiration-correlated cts with 10 breathing phases (0 to 90%, phase-based binning) and 3 mm slice thickness. 4d-fbct images were acquired during simulation, prior to therapy, and used for therapy planning. in 14 of the 20 subjects, 4d-fbcts were also acquired on the same scanner weekly during therapy.\\u00a04d-cbct images were acquired\\u00a0on a commercial cbct scanner (on-board imager\\u2122, varian medical systems, inc.). an external surrogate (real-time position management, varian medical systems, inc.) was integrated into the cbct acquisition system to stamp each cbct projection with the surrogate respiratory signal through in-house software and hardware tools. approximately 2500 projections were acquired over a period of 8-10 minutes in half-fan mode with half bow-tie filter. the technique was 125 kvp, 20 ma, and 20 ms in a single 360\\u00b0 slow gantry arc. using the external surrogate, the cbct projections were sorted into 10 breathing phases (0 to 90%, phase-based binning) and reconstructed with an in-house fdk reconstruction algorithm.audio-visual biofeedback was performed for all 4d-fbct and 4d-cbct acquisitions in all subjects.\\u00a0a single radiation oncologist delineated targets and organs at risk in all 4d-fbct and a limited number of 4d-cbct images, on all 10 phases per scan.\\u00a0seven of the subjects had gold coils implanted as fiducial markers in or near the tumor.\\u00a0the dataset is most fully described in detail in balik et al.1\\u00a0 seven of the subjects had gold coils implanted as fiducial markers in or near the tumor. the implantation procedure and details of marker location are described in detail in roman et al.2references1 s. balik et al., \\u201cevaluation of 4-dimensional computed tomography to 4-dimensional cone-beam computed tomography deformable image registration for lung cancer adaptive radiation therapy.\\u201d int. j. radiat. oncol. biol. phys.\\u00a086, 372\\u20139 (2013) pmcid: pmc3647023.2 n.o. roman, w. shepherd, n. mukhopadhyay, g.d. hugo, and e. weiss, \\u201cinterfractional positional variability of fiducial markers and primary tumors in locally advanced non-small-cell lung cancer during audiovisual biofeedback radiotherapy.\\u201d int. j. radiat. oncol. biol. phys.\\u00a083, 1566\\u201372 (2012). non-small cell lung cancer lung ct rtstruct image analyses community\"},{\"id\":43621,\"type\":\"collection\",\"slug\":\"a091105\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/a091105\\\/\",\"title\":\"Sorafenib Tosylate in Treating Patients With Desmoid Tumors or Aggressive Fibromatosis\",\"short_title\":\"A091105\",\"doi\":\"10.7937\\\/0WF5-SJ50\",\"summary\":\"This collection contains data from the Alliance for Clinical Trials in Oncology Clinical Trial NCT02066181\\u00a0 \\u201cSorafenib Tosylate in Treating Patients With Desmoid Tumors or Aggressive Fibromatosis\\\". Principal Investigator: Mrinal M Gounder. It was sponsored by NCI and performed by the Alliance for Clinical Trials in Oncology under study number A091105. This randomized phase III trial compares the effects, good and\\\/or bad, of sorafenib tosylate in treating patients with desmoid tumors or aggressive fibromatosis. Sorafenib tosylate may stop the growth of tumor cells by blocking some of the proteins needed for cell growth. Select patient-level data from this trial is available via the following link: \\u00a0https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/1266.Trial Outcomes\\u00a0\\u00a0Results of the trial have been reported in the following publications:Mazza GL, Petersen MM, Ginos B, Langlais BT, Heon N, Gounder MM, Mahoney MR, Zoroufy AJ, Schwartz GK, Rogak LJ, Thanarajasingam G, Basch E, Dueck AC. Missing data strategies for the Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE) in Alliance A091105 and COMET-2. Qual Life Res. 2022 Apr;31(4):1069-1080. doi: 10.1007\\\/s11136-021-02968-1. Epub 2021 Aug 21. Erratum in: Qual Life Res. 2021 Oct 11.Basch E, Becker C, Rogak LJ, Schrag D, Reeve BB, Spears P, Smith ML, Gounder MM, Mahoney MR, Schwartz GK, Bennett AV, Mendoza TR, Cleeland CS, Sloan JA, Bruner DW, Schwab G, Atkinson TM, Thanarajasingam G, Bertagnolli MM, Dueck AC. Composite grading algorithm for the National Cancer Institute's Patient-Reported Outcomes version of the Common Terminology Criteria for Adverse Events (PRO-CTCAE). Clin Trials. 2021 Feb;18(1):104-114. doi: 10.1177\\\/1740774520975120. Epub 2020 Dec 1.Gounder MM, Mahoney MR, Van Tine BA, Ravi V, Attia S, Deshpande HA, Gupta AA, Milhem MM, Conry RM, Movva S, Pishvaian MJ, Riedel RF, Sabagh T, Tap WD, Horvat N, Basch E, Schwartz LH, Maki RG, Agaram NP, Lefkowitz RA, Mazaheri Y, Yamashita R, Wright JJ, Dueck AC, Schwartz GK. Sorafenib for Advanced and Refractory Desmoid Tumors. N Engl J Med. 2018 Dec 20;379(25):2417-2428. doi: 10.1056\\\/NEJMoa1805052.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/nctn-logo.png\",\"program\":\"NCTN\",\"cancer_types\":[\"Non-Cancer\"],\"cancer_locations\":[\"Various\"],\"data_types\":[\"MR\",\"CT\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":83,\"date_updated\":\"2023-02-10\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=A091105\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/A091105-February-2023-manifest.tcia\",\"blob\":\"sorafenib tosylate in treating patients with desmoid tumors or aggressive fibromatosis a091105 this collection contains data from the alliance for clinical trials in oncology clinical trial nct02066181\\u00a0 \\u201csorafenib tosylate in treating patients with desmoid tumors or aggressive fibromatosis\\\". principal investigator: mrinal m gounder. it was sponsored by nci and performed by the alliance for clinical trials in oncology under study number a091105. this randomized phase iii trial compares the effects, good and\\\/or bad, of sorafenib tosylate in treating patients with desmoid tumors or aggressive fibromatosis. sorafenib tosylate may stop the growth of tumor cells by blocking some of the proteins needed for cell growth. select patient-level data from this trial is available via the following link: \\u00a0https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/1266.trial outcomes\\u00a0\\u00a0results of the trial have been reported in the following publications:mazza gl, petersen mm, ginos b, langlais bt, heon n, gounder mm, mahoney mr, zoroufy aj, schwartz gk, rogak lj, thanarajasingam g, basch e, dueck ac. missing data strategies for the patient-reported outcomes version of the common terminology criteria for adverse events (pro-ctcae) in alliance a091105 and comet-2. qual life res. 2022 apr;31(4):1069-1080. doi: 10.1007\\\/s11136-021-02968-1. epub 2021 aug 21. erratum in: qual life res. 2021 oct 11.basch e, becker c, rogak lj, schrag d, reeve bb, spears p, smith ml, gounder mm, mahoney mr, schwartz gk, bennett av, mendoza tr, cleeland cs, sloan ja, bruner dw, schwab g, atkinson tm, thanarajasingam g, bertagnolli mm, dueck ac. composite grading algorithm for the national cancer institute's patient-reported outcomes version of the common terminology criteria for adverse events (pro-ctcae). clin trials. 2021 feb;18(1):104-114. doi: 10.1177\\\/1740774520975120. epub 2020 dec 1.gounder mm, mahoney mr, van tine ba, ravi v, attia s, deshpande ha, gupta aa, milhem mm, conry rm, movva s, pishvaian mj, riedel rf, sabagh t, tap wd, horvat n, basch e, schwartz lh, maki rg, agaram np, lefkowitz ra, mazaheri y, yamashita r, wright jj, dueck ac, schwartz gk. sorafenib for advanced and refractory desmoid tumors. n engl j med. 2018 dec 20;379(25):2417-2428. doi: 10.1056\\\/nejmoa1805052.\\u00a0 non-cancer various mr ct clinical nctn\"},{\"id\":41141,\"type\":\"collection\",\"slug\":\"aapm-rt-mac\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/aapm-rt-mac\\\/\",\"title\":\"AAPM RT-MAC Grand Challenge 2019\",\"short_title\":\"AAPM-RT-MAC\",\"doi\":\"10.7937\\\/tcia.2019.bcfjqfqb\",\"summary\":\"This data set was provided in association with a\\u00a0challenge competition\\u00a0and\\u00a0related conference session\\u00a0conducted at the AAPM 2019 Annual Meeting.\\u00a0MRI is popular in radiation oncology because of its excellent imaging quality of soft tissue and tumor. With the advent of MR-Linac and MR-guided radiation therapy, there is a trend toward a MR-based radiation treatment planning. Contouring is an important task in modern radiation treatment planning and frequently introduces uncertainties in radiation therapy due to observer variabilities. Auto-segmentation has been demonstrated as an effective approach to reduce this uncertainty. The overall objective of this grand challenge is to provide a platform for comparison of various auto-segmentation algorithms when they are used to delineate organs at risk (OARs) or tumors from MR images for head and neck patients for radiation treatment planning. The results will provide an indication of the performances achieved by various auto-segmentation algorithms and can be used to guide the selection of these algorithms for clinic use if desirable.The data for this challenge contains a total of 55 MRI cases, each from a single examination from a distinct patient, with each case consisting of a T2-weighted MRI images in DICOM format. The MRI scanning protocol was designed for radiation treatment simulation. Thirty-one of these will be provided as training cases, with the parotid glands, submandibular glands, level 2 and level 3 lymph nodes contoured. The images and contours were acquired from MD Anderson Cancer Center.More details on accessing the various challenge subsets (training, off-site test, and live test) can be found on the Detailed Description tab below.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/RTMACImage.png\",\"program\":\"Community\",\"cancer_types\":[\"Head and Neck Cancer\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"MR\",\"RTSTRUCT\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":55,\"date_updated\":\"2020-07-20\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=AAPM-RT-MAC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/AAPM-RT-MAC-July-20-2020-NBIA-manifest.tcia\",\"blob\":\"aapm rt-mac grand challenge 2019 aapm-rt-mac this data set was provided in association with a\\u00a0challenge competition\\u00a0and\\u00a0related conference session\\u00a0conducted at the aapm 2019 annual meeting.\\u00a0mri is popular in radiation oncology because of its excellent imaging quality of soft tissue and tumor. with the advent of mr-linac and mr-guided radiation therapy, there is a trend toward a mr-based radiation treatment planning. contouring is an important task in modern radiation treatment planning and frequently introduces uncertainties in radiation therapy due to observer variabilities. auto-segmentation has been demonstrated as an effective approach to reduce this uncertainty. the overall objective of this grand challenge is to provide a platform for comparison of various auto-segmentation algorithms when they are used to delineate organs at risk (oars) or tumors from mr images for head and neck patients for radiation treatment planning. the results will provide an indication of the performances achieved by various auto-segmentation algorithms and can be used to guide the selection of these algorithms for clinic use if desirable.the data for this challenge contains a total of 55 mri cases, each from a single examination from a distinct patient, with each case consisting of a t2-weighted mri images in dicom format. the mri scanning protocol was designed for radiation treatment simulation. thirty-one of these will be provided as training cases, with the parotid glands, submandibular glands, level 2 and level 3 lymph nodes contoured. the images and contours were acquired from md anderson cancer center.more details on accessing the various challenge subsets (training, off-site test, and live test) can be found on the detailed description tab below.\\u00a0 head and neck cancer head-neck mr rtstruct community\"},{\"id\":41821,\"type\":\"collection\",\"slug\":\"acns0332\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/acns0332\\\/\",\"title\":\"Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed, Previously Untreated, High-Risk Medulloblastoma\\\/PNET\",\"short_title\":\"ACNS0332\",\"doi\":\"10.7937\\\/TCIA.582B-XZ89\",\"summary\":\"This collection contains data from the Children\\u2019s Oncology Group (COG) Clinical Trial NCT00392327, \\\"Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed, Previously Untreated, High-Risk Medulloblastoma\\\/PNET\\\". The\\u00a0Study Chair was James M. Olson, M.D., Ph.D. It was sponsored by NCI and performed by the Children's Oncology Group under study number ACNS0332. This randomized phase III trial studies different chemotherapy and radiation therapy regimens to compare how well they work in treating young patients with newly diagnosed, previously untreated, high-risk medulloblastoma. \\u00a0Select patient-level clinical data from this trial is available via the following link:\\u00a0https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/838.Trial DescriptionChildren with histologically diagnosed high-risk medulloblastoma, supratentorial primitive neuro-ectodermal tumor of the CNS (CNS-PNET), and pineoblastoma (PBL) have had poor survival despite intensive treatment.\\u00a0 The Children\\u2019s Oncology Group (COG) study ACNS0332 was designed to test two approaches for treatment intensification for these patients: addition of carboplatin during irradiation and\\\/or addition of isotretinoin to the adjuvant regimen. Carboplatin has demonstrated preclinical and clinical efficacy in these tumors and is well tolerated in children, whereas isotretinoin crosses the blood-brain barrier efficiently and is effective against preclinical models of medulloblastoma (MB).\\u00a0 Molecular profiling later revealed tumor heterogeneity that was not detectable at protocol inception. Enrollment of patients with CNS-PNET\\\/PBL was subsequently discontinued, and outcomes for this part of the study reported.\\u00a0 Eighty-five participants with institutionally diagnosed CNS-PNETs\\\/PBLs were enrolled.\\u00a0Of 60 patients with sufficient tissue, 31\\u00a0had tumors that\\u00a0were non-pineal in location. Twenty-two patients\\u00a0(71%) of those 31 were diagnosed with\\u00a0tumor types\\u00a0not intended for trial inclusion, including 18 high-grade (HGGs), two atypical teratoid rhbdoid tumors, and 2 ependymomas. Outcomes across tumor types were strikingly different. Neither carboplatin, nor isotretinoin significantly altered outcomes for all patients. Survival for patients with HGG was similar to that of historic studies that avoid craniospinal irradiation and intensive chemotherapy.\\u00a0 For patients with CNS-PNET\\\/PBL, prognosis is considerably better than previously assumed when molecularly confirmed HGGs are removed. Identification of molecular HGGs may spare affected children from unhelpful intensive treatment. This trial highlights the challenges of a histology-based diagnosis for pediatric brain tumors and indicates that molecular profiling should become a standard component of initial diagnosis.Pre-operative and post-operative MRI scans of the brain with and without contrast and spinal MRI with and without contrast were required.\\u00a0 All scans underwent central review.Trial Outcomes Results of the trial have been reported in the following publication:Hwang, E. I., Kool, M., Burger, P. C., Capper, D., Chavez, L., Brabetz, S., Williams-Hughes, C., Billups, C., Heier, L., Jaju, A., Michalski, J., Li, Y., Leary, S., Zhou, T., von Deimling, A., Jones, D. T. W., Fouladi, M., Pollack, I. F., Gajjar, A., \\u2026 Olson, J. M. (2018). Extensive Molecular and Clinical Heterogeneity in Patients With Histologically Diagnosed CNS-PNET Treated as a Single Entity: A Report From the Children\\u2019s Oncology Group Randomized ACNS0332 Trial. Journal of Clinical Oncology, 36(34), 3388\\u20133395. https:\\\/\\\/doi.org\\\/10.1200\\\/jco.2017.76.4720.\\u00a0Epub ahead of print. PMID: 30332335.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/nctn-logo.png\",\"program\":\"NCTN\",\"cancer_types\":[\"Medulloblastoma\"],\"cancer_locations\":[\"Head\"],\"data_types\":[\"MR\",\"CT\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":85,\"date_updated\":\"2021-05-04\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=ACNS0332\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ACNS0332-manifest.tcia\",\"blob\":\"chemotherapy and radiation therapy in treating young patients with newly diagnosed, previously untreated, high-risk medulloblastoma\\\/pnet acns0332 this collection contains data from the children\\u2019s oncology group (cog) clinical trial nct00392327, \\\"chemotherapy and radiation therapy in treating young patients with newly diagnosed, previously untreated, high-risk medulloblastoma\\\/pnet\\\". the\\u00a0study chair was james m. olson, m.d., ph.d. it was sponsored by nci and performed by the children's oncology group under study number acns0332. this randomized phase iii trial studies different chemotherapy and radiation therapy regimens to compare how well they work in treating young patients with newly diagnosed, previously untreated, high-risk medulloblastoma. \\u00a0select patient-level clinical data from this trial is available via the following link:\\u00a0https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/838.trial descriptionchildren with histologically diagnosed high-risk medulloblastoma, supratentorial primitive neuro-ectodermal tumor of the cns (cns-pnet), and pineoblastoma (pbl) have had poor survival despite intensive treatment.\\u00a0 the children\\u2019s oncology group (cog) study acns0332 was designed to test two approaches for treatment intensification for these patients: addition of carboplatin during irradiation and\\\/or addition of isotretinoin to the adjuvant regimen. carboplatin has demonstrated preclinical and clinical efficacy in these tumors and is well tolerated in children, whereas isotretinoin crosses the blood-brain barrier efficiently and is effective against preclinical models of medulloblastoma (mb).\\u00a0 molecular profiling later revealed tumor heterogeneity that was not detectable at protocol inception. enrollment of patients with cns-pnet\\\/pbl was subsequently discontinued, and outcomes for this part of the study reported.\\u00a0 eighty-five participants with institutionally diagnosed cns-pnets\\\/pbls were enrolled.\\u00a0of 60 patients with sufficient tissue, 31\\u00a0had tumors that\\u00a0were non-pineal in location. twenty-two patients\\u00a0(71%) of those 31 were diagnosed with\\u00a0tumor types\\u00a0not intended for trial inclusion, including 18 high-grade (hggs), two atypical teratoid rhbdoid tumors, and 2 ependymomas. outcomes across tumor types were strikingly different. neither carboplatin, nor isotretinoin significantly altered outcomes for all patients. survival for patients with hgg was similar to that of historic studies that avoid craniospinal irradiation and intensive chemotherapy.\\u00a0 for patients with cns-pnet\\\/pbl, prognosis is considerably better than previously assumed when molecularly confirmed hggs are removed. identification of molecular hggs may spare affected children from unhelpful intensive treatment. this trial highlights the challenges of a histology-based diagnosis for pediatric brain tumors and indicates that molecular profiling should become a standard component of initial diagnosis.pre-operative and post-operative mri scans of the brain with and without contrast and spinal mri with and without contrast were required.\\u00a0 all scans underwent central review.trial outcomes results of the trial have been reported in the following publication:hwang, e. i., kool, m., burger, p. c., capper, d., chavez, l., brabetz, s., williams-hughes, c., billups, c., heier, l., jaju, a., michalski, j., li, y., leary, s., zhou, t., von deimling, a., jones, d. t. w., fouladi, m., pollack, i. f., gajjar, a., \\u2026 olson, j. m. (2018). extensive molecular and clinical heterogeneity in patients with histologically diagnosed cns-pnet treated as a single entity: a report from the children\\u2019s oncology group randomized acns0332 trial. journal of clinical oncology, 36(34), 3388\\u20133395. https:\\\/\\\/doi.org\\\/10.1200\\\/jco.2017.76.4720.\\u00a0epub ahead of print. pmid: 30332335. medulloblastoma head mr ct clinical nctn\"},{\"id\":41253,\"type\":\"collection\",\"slug\":\"acrin-6698\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/acrin-6698\\\/\",\"title\":\"ACRIN 6698\\\/I-SPY2 Breast DWI\",\"short_title\":\"ACRIN-6698\",\"doi\":\"10.7937\\\/tcia.kk02-6d95\",\"summary\":\"The American College of Radiology Imaging Network (ACRIN) trial 6698 (NCT01564368) was a multi-center study to evaluate the effectiveness of quantitative diffusion weighted imaging (DWI) for assessing breast cancer response to neoadjuvant chemotherapy (NAC).\\u00a0 \\u00a0ACRIN 6698 was performed as a sub-study of the ongoing I-SPY 2 TRIAL (Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And moLecular Analysis 2), an adaptive, multi-agent phase II trial designed to quickly identify new agents for breast cancer.\\u00a0\\u00a0Patients recruited for the I-SPY 2 TRIAL and enrolling at sites meeting DWI qualification requirements were eligible for the 6698 trial. 406 women with invasive breast cancer were prospectively enrolled to ACRIN 6698 at ten institutions between August 2012 to January 2015, and 272 were randomized to I\\u2011SPY\\u00a02 experimental treatment or control arms. Patients underwent breast DWI using a 4-b value protocol, as well as standard T2-weighted and dynamic contrast enhanced (DCE)\\u00a0scans. MRI studies were conducted at 4 timepoints over the course of NAC: pre-treatment (T0), early-treatment after 3 cycles paclitaxel (T1), mid-treatment between paclitaxel and AC (T2) and post-treatment (T3). Of the 272 treated patients, 242 comprised the primary analysis cohort (30 were excluded for missing or non-evaluable DWI exams). This TCIA collection includes all MRI studies received by the UCSF image analysis lab, excluding studies with no analyzable acquisitions. A separate download option is provided to access only those studies in the primary analysis cohort.\\u00a0In addition, DWI test\\\/retest data acquired at baseline or early-treatment in a subset of patients is included in the full collection and is available as a separate download option (N=89 subjects consented and imaged, 71 analyzable test\\\/retest acquisition pairs).The ACRIN 6698 image data set is currently a unique collection for investigating the utility of DWI for monitoring of response to neoadjuvant breast cancer treatment. While many smaller and\\\/or single-site DWI studies have been published, the multi-center and quality-control aspects of this data allow investigators true evaluation of analysis techniques in the clinical trial environment. The multi-b value protocol also allows evaluation of higher order diffusion models and evaluation at two different clinically relevant b values (600 and 800 s\\\/mm2). Furthermore, the embedded test-retest arm of the study will allow evaluation of repeatability and reproducibility of new DWI metrics and analysis techniques.In addition to the original DWI data the collection includes derived ADC maps with manually delimited tumor segmentations from the primary study analysis (for all studies rated as analyzable in the QC evaluation), plus T2-weighted images, DCE images with derived enhancement maps, clinical data and outcome data (pathologic complete response [pCR] at surgery). Additional information about the trial is available in the Study Protocol and Case Report Forms.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image2021-5-24_12-40-49.png\",\"program\":\"NCI Trials\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MR\",\"SEG\",\"Demographic\",\"Molecular Test\",\"Classification\",\"Follow-Up\",\"Other\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":385,\"date_updated\":\"2021-12-17\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=ACRIN-6698\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ACRIN-6698-I-SPY2-Breast-DWI-manifest.tcia\",\"blob\":\"acrin 6698\\\/i-spy2 breast dwi acrin-6698 the american college of radiology imaging network (acrin) trial 6698 (nct01564368) was a multi-center study to evaluate the effectiveness of quantitative diffusion weighted imaging (dwi) for assessing breast cancer response to neoadjuvant chemotherapy (nac).\\u00a0 \\u00a0acrin 6698 was performed as a sub-study of the ongoing i-spy 2 trial (investigation of serial studies to predict your therapeutic response with imaging and molecular analysis 2), an adaptive, multi-agent phase ii trial designed to quickly identify new agents for breast cancer.\\u00a0\\u00a0patients recruited for the i-spy 2 trial and enrolling at sites meeting dwi qualification requirements were eligible for the 6698 trial. 406 women with invasive breast cancer were prospectively enrolled to acrin 6698 at ten institutions between august 2012 to january 2015, and 272 were randomized to i\\u2011spy\\u00a02 experimental treatment or control arms. patients underwent breast dwi using a 4-b value protocol, as well as standard t2-weighted and dynamic contrast enhanced (dce)\\u00a0scans. mri studies were conducted at 4 timepoints over the course of nac: pre-treatment (t0), early-treatment after 3 cycles paclitaxel (t1), mid-treatment between paclitaxel and ac (t2) and post-treatment (t3). of the 272 treated patients, 242 comprised the primary analysis cohort (30 were excluded for missing or non-evaluable dwi exams). this tcia collection includes all mri studies received by the ucsf image analysis lab, excluding studies with no analyzable acquisitions. a separate download option is provided to access only those studies in the primary analysis cohort.\\u00a0in addition, dwi test\\\/retest data acquired at baseline or early-treatment in a subset of patients is included in the full collection and is available as a separate download option (n=89 subjects consented and imaged, 71 analyzable test\\\/retest acquisition pairs).the acrin 6698 image data set is currently a unique collection for investigating the utility of dwi for monitoring of response to neoadjuvant breast cancer treatment. while many smaller and\\\/or single-site dwi studies have been published, the multi-center and quality-control aspects of this data allow investigators true evaluation of analysis techniques in the clinical trial environment. the multi-b value protocol also allows evaluation of higher order diffusion models and evaluation at two different clinically relevant b values (600 and 800 s\\\/mm2). furthermore, the embedded test-retest arm of the study will allow evaluation of repeatability and reproducibility of new dwi metrics and analysis techniques.in addition to the original dwi data the collection includes derived adc maps with manually delimited tumor segmentations from the primary study analysis (for all studies rated as analyzable in the qc evaluation), plus t2-weighted images, dce images with derived enhancement maps, clinical data and outcome data (pathologic complete response [pcr] at surgery). additional information about the trial is available in the study protocol and case report forms. breast cancer breast mr seg demographic molecular test classification follow-up other clinical image analyses nci trials\"},{\"id\":41173,\"type\":\"collection\",\"slug\":\"acrin-contralateral-breast-mr\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/acrin-contralateral-breast-mr\\\/\",\"title\":\"ACRIN 6667\",\"short_title\":\"ACRIN-Contralateral-Breast-MR\",\"doi\":\"10.7937\\\/Q1EE-J082\",\"summary\":\"This dataset relates to NCI Clinical trial, \\\"Magnetic Resonance Imaging in Women Recently Diagnosed With Unilateral Breast Cancer (ACRIN-6667)\\\". The dataset consists of 984 patients but only 969 were included in the primary data analysis due to study criteria.Even after careful clinical and mammographic evaluation, cancer is found in the contralateral breast in up to 10% of women who have received treatment for unilateral breast cancer. ACRIN 6667 was conducted to determine whether magnetic resonance imaging (MRI) could improve on clinical breast examination and mammography in detecting contralateral breast cancer soon after the initial diagnosis of unilateral breast cancer. Additional information about the trial is available in the Study Protocol and Case Report Forms.\\u00a0METHODSA total of 969 women with a recent diagnosis of unilateral breast cancer and no abnormalities on mammographic and clinical examination of the contralateral breast underwent breast MRI. The diagnosis of MRI-detected cancer was confirmed by means of biopsy within 12 months after study entry. The absence of breast cancer was determined by means of biopsy, the absence of positive findings on repeat imaging and clinical examination, or both at 1 year of follow-up.\\u00a0RESULTSMRI detected clinically and mammographically occult breast cancer in the contralateral breast in 30 of 969 women who were enrolled in the study (3.1%). The sensitivity of MRI in the contralateral breast was 91%, and the specificity was 88%. The negative predictive value of MRI was 99%. A biopsy was performed on the basis of a positive MRI finding in 121 of the 969 women (12.5%), 30 of whom had specimens that were positive for cancer (24.8%); 18 of the 30 specimens were positive for invasive cancer. The mean diameter of the invasive tumors detected was 10.9 mm. The additional number of cancers detected was not influenced by breast density, menopausal status, or the histologic features of the primary tumor.CONCLUSIONSMRI can detect cancer in the contralateral breast that is missed by mammography and clinical examination at the time of the initial breast-cancer diagnosis.\",\"image\":false,\"program\":\"NCI Trials\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MR\",\"CR\",\"Demographic\",\"Diagnosis\",\"Other\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":984,\"date_updated\":\"2021-03-05\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=ACRIN-Contralateral-Breast-MR\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ACRIN-Contralateral-Breast-MR-Feb-2021-manifest.tcia\",\"blob\":\"acrin 6667 acrin-contralateral-breast-mr this dataset relates to nci clinical trial, \\\"magnetic resonance imaging in women recently diagnosed with unilateral breast cancer (acrin-6667)\\\". the dataset consists of 984 patients but only 969 were included in the primary data analysis due to study criteria.even after careful clinical and mammographic evaluation, cancer is found in the contralateral breast in up to 10% of women who have received treatment for unilateral breast cancer. acrin 6667 was conducted to determine whether magnetic resonance imaging (mri) could improve on clinical breast examination and mammography in detecting contralateral breast cancer soon after the initial diagnosis of unilateral breast cancer. additional information about the trial is available in the study protocol and case report forms.\\u00a0methodsa total of 969 women with a recent diagnosis of unilateral breast cancer and no abnormalities on mammographic and clinical examination of the contralateral breast underwent breast mri. the diagnosis of mri-detected cancer was confirmed by means of biopsy within 12 months after study entry. the absence of breast cancer was determined by means of biopsy, the absence of positive findings on repeat imaging and clinical examination, or both at 1 year of follow-up.\\u00a0resultsmri detected clinically and mammographically occult breast cancer in the contralateral breast in 30 of 969 women who were enrolled in the study (3.1%). the sensitivity of mri in the contralateral breast was 91%, and the specificity was 88%. the negative predictive value of mri was 99%. a biopsy was performed on the basis of a positive mri finding in 121 of the 969 women (12.5%), 30 of whom had specimens that were positive for cancer (24.8%); 18 of the 30 specimens were positive for invasive cancer. the mean diameter of the invasive tumors detected was 10.9 mm. the additional number of cancers detected was not influenced by breast density, menopausal status, or the histologic features of the primary tumor.conclusionsmri can detect cancer in the contralateral breast that is missed by mammography and clinical examination at the time of the initial breast-cancer diagnosis. breast cancer breast mr cr demographic diagnosis other clinical nci trials\"},{\"id\":41183,\"type\":\"collection\",\"slug\":\"acrin-dsc-mr-brain\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/acrin-dsc-mr-brain\\\/\",\"title\":\"ACRIN 6677\\\/RTOG 0625\",\"short_title\":\"ACRIN-DSC-MR-Brain\",\"doi\":\"10.7937\\\/tcia.2019.zr1pjf4i\",\"summary\":\"RTOG 0625\\\/ACRIN 6677 is a multicenter, randomized, phase II trial of bevacizumab with irinotecan or temozolomide in recurrent glioblastoma (GBM). This study investigated whether early posttreatment progression on FLAIR or postcontrast MRI assessed by central reading predicts overall survival (OS).METHODS:Of 123 enrolled patients, 107 had baseline and at least 1 posttreatment MRI. Two central neuroradiologists serially measured bidimensional (2D) and volumetric (3D) enhancement on postcontrast T1-weighted images and volume of FLAIR hyperintensity. Progression status on all posttreatment MRIs was determined using Macdonald and RANO imaging threshold criteria, with a third neuroradiologist adjudicating discrepancies of both progression occurrence and timing. For each MRI pulse sequence, Kaplan-Meier survival estimates and log-rank test were used to compare OS between cases with or without radiologic progression.Two sets of XLS spreadsheets (ACRIN-DSC-MR-Brain TCIA Anonymized and ACRIN-DSC-MR-Brain-HB TCIA Anonymized) are needed in order to obtain the entire clinical data set for this collection. The file sets are a random sample of ACRIN 6677 participants divided into 2 groups. Group 1\\\/ACRIN-DSC-MR-Brain TCIA Anonymized: a 75% random sample; Group 2\\\/ACRIN-DSC-MR-Brain-HB TCIA Anonymized: a 25% random sample initially held for testing\\\/validating algorithms trained on the 75% sample.\\u00a0 Both are available via the clinical download button.RESULTS:Radiologic progression occurred after 2 chemotherapy cycles (8 weeks) in 9 of 97 (9%), 9 of 73 (12%), and 11 of 98 (11%) 2D-T1, 3D-T1, and FLAIR cases, respectively, and 34 of 80 (43%), 21 of 58 (36%), and 37 of 79 (47%) corresponding cases after 4 cycles (16 weeks). Median OS among patients progressing at 8 or 16 weeks was significantly less than that among nonprogressors, as determined on 2D-T1 (114 vs 278 days and 214 vs 426 days, respectively; P < .0001 for both) and 3D-T1 (117 vs 306 days [P < .0001] and 223 vs 448 days [P = .0003], respectively) but not on FLAIR (201 vs 276 days [P = .38] and 303 vs 321 days [P = .13], respectively).CONCLUSION:Early progression on 2D-T1 and 3D-T1, but not FLAIR MRI, after 8 and 16 weeks of anti-vascular endothelial growth factor therapy has highly significant prognostic value for OS in recurrent GBM.\",\"image\":false,\"program\":\"NCI Trials\",\"cancer_types\":[\"Glioblastoma Multiforme\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"CT\",\"Demographic\",\"Diagnosis\",\"Follow-Up\",\"Measurement\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":123,\"date_updated\":\"2019-02-07\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=ACRIN-DSC-MR-Brain\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ACRIN-DSC-MR-Brain-NBIA-manifest.tcia\",\"blob\":\"acrin 6677\\\/rtog 0625 acrin-dsc-mr-brain rtog 0625\\\/acrin 6677 is a multicenter, randomized, phase ii trial of bevacizumab with irinotecan or temozolomide in recurrent glioblastoma (gbm). this study investigated whether early posttreatment progression on flair or postcontrast mri assessed by central reading predicts overall survival (os).methods:of 123 enrolled patients, 107 had baseline and at least 1 posttreatment mri. two central neuroradiologists serially measured bidimensional (2d) and volumetric (3d) enhancement on postcontrast t1-weighted images and volume of flair hyperintensity. progression status on all posttreatment mris was determined using macdonald and rano imaging threshold criteria, with a third neuroradiologist adjudicating discrepancies of both progression occurrence and timing. for each mri pulse sequence, kaplan-meier survival estimates and log-rank test were used to compare os between cases with or without radiologic progression.two sets of xls spreadsheets (acrin-dsc-mr-brain tcia anonymized and acrin-dsc-mr-brain-hb tcia anonymized) are needed in order to obtain the entire clinical data set for this collection. the file sets are a random sample of acrin 6677 participants divided into 2 groups. group 1\\\/acrin-dsc-mr-brain tcia anonymized: a 75% random sample; group 2\\\/acrin-dsc-mr-brain-hb tcia anonymized: a 25% random sample initially held for testing\\\/validating algorithms trained on the 75% sample.\\u00a0 both are available via the clinical download button.results:radiologic progression occurred after 2 chemotherapy cycles (8 weeks) in 9 of 97 (9%), 9 of 73 (12%), and 11 of 98 (11%) 2d-t1, 3d-t1, and flair cases, respectively, and 34 of 80 (43%), 21 of 58 (36%), and 37 of 79 (47%) corresponding cases after 4 cycles (16 weeks). median os among patients progressing at 8 or 16 weeks was significantly less than that among nonprogressors, as determined on 2d-t1 (114 vs 278 days and 214 vs 426 days, respectively; p < .0001 for both) and 3d-t1 (117 vs 306 days [p < .0001] and 223 vs 448 days [p = .0003], respectively) but not on flair (201 vs 276 days [p = .38] and 303 vs 321 days [p = .13], respectively).conclusion:early progression on 2d-t1 and 3d-t1, but not flair mri, after 8 and 16 weeks of anti-vascular endothelial growth factor therapy has highly significant prognostic value for os in recurrent gbm. glioblastoma multiforme brain mr ct demographic diagnosis follow-up measurement clinical nci trials\"},{\"id\":41233,\"type\":\"collection\",\"slug\":\"acrin-flt-breast\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/acrin-flt-breast\\\/\",\"title\":\"ACRIN 6688\",\"short_title\":\"ACRIN-FLT-Breast\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2017.ol20zmxg\",\"summary\":\"The objective of the ACRIN 6688 multi-center clinical trial was to correlate changes measured by 18F-FLT PET imaging, a measure of cellular proliferation, in the primary tumor early during NAC (neo-adjuvant chemotherapy) with pCR (pathologic complete response) in locally advanced breast cancer patients. The trial also examined both pre-therapy and post-therapy association of 18F-FLT uptake with the tissue proliferative marker Ki-67 to compare 18F-FLT PET\\\/CT against an accepted reference standard for cellular proliferation. The trial protocol is graphically described in the figure below. Additional information about the trial is available in the  Study Protocol and Case Report Forms.\\u00a0Potentially three 18 FLT\\\/CT imaging sessions would be conducted at the times indicated above for the 90 enrolled patients on the study. However, only 43 patients completed all three scans, 54 patients had pre\\\/post therapy scans of which 51 were evaluable for the primary aim (see Kostakaglu et al. 2015 ).\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image2018-8-14_14-27-5.png\",\"program\":\"NCI Trials\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"CT\",\"PT\",\"OT\",\"Demographic\",\"Follow-Up\",\"Measurement\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":83,\"date_updated\":\"2020-01-24\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=ACRIN-FLT-Breast\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ACRIN-FLT-Breast_2020-01-24.tcia\",\"blob\":\"acrin 6688 acrin-flt-breast the objective of the acrin 6688 multi-center clinical trial was to correlate changes measured by 18f-flt pet imaging, a measure of cellular proliferation, in the primary tumor early during nac (neo-adjuvant chemotherapy) with pcr (pathologic complete response) in locally advanced breast cancer patients. the trial also examined both pre-therapy and post-therapy association of 18f-flt uptake with the tissue proliferative marker ki-67 to compare 18f-flt pet\\\/ct against an accepted reference standard for cellular proliferation. the trial protocol is graphically described in the figure below. additional information about the trial is available in the  study protocol and case report forms.\\u00a0potentially three 18 flt\\\/ct imaging sessions would be conducted at the times indicated above for the 90 enrolled patients on the study. however, only 43 patients completed all three scans, 54 patients had pre\\\/post therapy scans of which 51 were evaluable for the primary aim (see kostakaglu et al. 2015 ). breast cancer breast ct pt ot demographic follow-up measurement clinical nci trials\"},{\"id\":41199,\"type\":\"collection\",\"slug\":\"acrin-fmiso-brain\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/acrin-fmiso-brain\\\/\",\"title\":\"ACRIN 6684\",\"short_title\":\"ACRIN-FMISO-Brain\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2018.vohlekok\",\"summary\":\"The objective of the ACRIN 6684 multi-center clinical trial was to determine the association of baseline FMISO PET uptake (maximal tumor to blood ratio, hypoxic volume) and MRI parameters (Ktrans, CBV) with overall survival, time to disease progression, and 6-month progression free survival in participants with newly diagnosed glioblastoma multiforme (GBM).\\u00a0The trial also collected standard brain cancer data such as Karnofsky performance status, but also pathological biomarkers that included MGMT status, HIF1-alpha, GLUT1, CAIX, CD31, and alpha-SMA expression assays.\\u00a0There are two sets of volumes of interest (VOI) included with the ACRIN 6684 collection of MRI, PET and low-dose CT patient images.\\u00a0 These include\\u00a0delineation of enhancing brain tumor lesions and\\u00a018F-FMISO PET hypoxia maps.\\u00a0 More information about these masks can be found on the Detailed Description tab below. \\u00a0Additional information about the trial is available in the Study Protocol and Case Report Forms.\\u00a0ACRIN 6684 Study ProtocolAfter establishing eligibility and enrollment to the study, baseline imaging of both MR and PET was performed within 2 weeks of starting therapy. FMISO, has been helpful in evaluating tumor oxygenation status, which may affect how well it responds to radiation and chemotherapy. The MRI scans were designed to measure tumor characteristics related to oxygenation status, including changes in blood flow, blood volume, and blood vessel size.In the original protocol, following baseline imaging was an optional test-retest scan for FMISO PET only. Also included were PET and MRI scans at 3 weeks after the onset of chemo\\\/radiation therapy, and 4 weeks following the end of standard treatment. Of the 50 patients enrolled in the study only one patient had a test-retest FMISO scan, and the requirement of scans mid and post therapy were dropped after the 4th case.\\u00a0 The current protocol appears in the figure on the right, and can be found online ( Protocol-ACRIN 6684 Amendment 7, 01.24.12 ).\\u00a0\\u00a0The latest protocol for ACRIN 6684 had PET and MR imaging performed only at baseline, up to 2 weeks prior to standard treatment (chemo + radiation therapy).\\u00a0 Mid and post-therapy scans were eliminated from the protocol after Case 4, and only one patient had a retest FMISO scan. Of the 50 enrolled patients, 42 patients had evaluable imaging data for the primary aims of the study (see Gerstner et al. 2016).Note: The MRI DWI\\\/DTI series acquired through GE or Siemens scanners for 30 patients have been stripped of their b-values and diffusion gradient matrix DICOM header fields making them unable to be processed for ADC map production. The patients scanned with Philips MRI scanners are intact.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image2018-8-14_15-9-18.png\",\"program\":\"NCI Trials\",\"cancer_types\":[\"Glioblastoma Multiforme\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"PT\",\"CT\",\"Demographic\",\"Diagnosis\",\"Follow-Up\",\"Measurement\",\"Treatment\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":45,\"date_updated\":\"2019-07-02\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=ACRIN-FMISO-Brain\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ACRIN-FMISO-Brain-July-2-2019-NBIA-manifest.tcia\",\"blob\":\"acrin 6684 acrin-fmiso-brain the objective of the acrin 6684 multi-center clinical trial was to determine the association of baseline fmiso pet uptake (maximal tumor to blood ratio, hypoxic volume) and mri parameters (ktrans, cbv) with overall survival, time to disease progression, and 6-month progression free survival in participants with newly diagnosed glioblastoma multiforme (gbm).\\u00a0the trial also collected standard brain cancer data such as karnofsky performance status, but also pathological biomarkers that included mgmt status, hif1-alpha, glut1, caix, cd31, and alpha-sma expression assays.\\u00a0there are two sets of volumes of interest (voi) included with the acrin 6684 collection of mri, pet and low-dose ct patient images.\\u00a0 these include\\u00a0delineation of enhancing brain tumor lesions and\\u00a018f-fmiso pet hypoxia maps.\\u00a0 more information about these masks can be found on the detailed description tab below. \\u00a0additional information about the trial is available in the study protocol and case report forms.\\u00a0acrin 6684 study protocolafter establishing eligibility and enrollment to the study, baseline imaging of both mr and pet was performed within 2 weeks of starting therapy. fmiso, has been helpful in evaluating tumor oxygenation status, which may affect how well it responds to radiation and chemotherapy. the mri scans were designed to measure tumor characteristics related to oxygenation status, including changes in blood flow, blood volume, and blood vessel size.in the original protocol, following baseline imaging was an optional test-retest scan for fmiso pet only. also included were pet and mri scans at 3 weeks after the onset of chemo\\\/radiation therapy, and 4 weeks following the end of standard treatment. of the 50 patients enrolled in the study only one patient had a test-retest fmiso scan, and the requirement of scans mid and post therapy were dropped after the 4th case.\\u00a0 the current protocol appears in the figure on the right, and can be found online ( protocol-acrin 6684 amendment 7, 01.24.12 ).\\u00a0\\u00a0the latest protocol for acrin 6684 had pet and mr imaging performed only at baseline, up to 2 weeks prior to standard treatment (chemo + radiation therapy).\\u00a0 mid and post-therapy scans were eliminated from the protocol after case 4, and only one patient had a retest fmiso scan. of the 50 enrolled patients, 42 patients had evaluable imaging data for the primary aims of the study (see gerstner et al. 2016).note: the mri dwi\\\/dti series acquired through ge or siemens scanners for 30 patients have been stripped of their b-values and diffusion gradient matrix dicom header fields making them unable to be processed for adc map production. the patients scanned with philips mri scanners are intact. glioblastoma multiforme brain mr pt ct demographic diagnosis follow-up measurement treatment clinical nci trials\"},{\"id\":41219,\"type\":\"collection\",\"slug\":\"acrin-hnscc-fdg-pet-ct\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/acrin-hnscc-fdg-pet-ct\\\/\",\"title\":\"ACRIN 6685\",\"short_title\":\"ACRIN-HNSCC-FDG-PET-CT\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.JQEJZZNG\",\"summary\":\"ACRIN-HNSCC-FDG-PET\\\/CT (ACRIN 6685) is a Multicenter Trial of FDG-PET\\\/CT Staging of Head and Neck Cancer\\u00a0and its Impact on the N0 Neck Surgical Treatment in Head and Neck Cancer Patients.\\u00a0The objective of this study was to determine the negative predictive value (NPV)\\u00a0of positron emission tomography (PET)\\\/computed tomography CT for clinically N0 neck based upon pathologic sampling of the neck lymph nodes and to\\u00a0determine PET\\\/CT\\u2019s potential to change treatment of the N0 neck.\\u00a0Primary Aim Findings:[18F]fluorodeoxyglucose-PET\\\/CT has high NPV for the N0 neck in T2 to T4 HNSCC. The surgical treatment plans on the basis of PET\\\/CT findings may be changed in approximately 22% of this group. These findings suggest that [18F]fluorodeoxyglucose-PET\\\/CT may assist the clinician in deciding on the best therapy for the clinically N0 neck in HNSCC. Well-designed clinical trials should be performed to test the outcome of omitting neck dissection by using PET\\\/CT.Study Design Summary:\\u00a0People with newly diagnosed head and neck squamous cell carcinoma being considered for surgical resection, with at least one side of the neck planned for dissection clinically N0, and at risk for occult metastasis (when risk based on clinical data is felt to be greater than 30%).\\u00a0A total of 287 participants were prospectively enrolled from 23 American College of Radiology Imaging Network-qualified institutions. PET\\\/CT was compared with findings at neck dissection.Two sets of XLS spreadsheets (file set 1 and file set 2) are needed in order to obtain the entire clinical data set for this collection. The file sets are a random sample of ACRIN 6685 participants divided into 2 groups. Group 1\\\/file set 1: a 75% random sample; Group 2: a 25% random sample initially held for testing\\\/validating algorithms trained on the 75% sample.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Screen-Shot-2020-02-25-at-2.21.57-PM.png\",\"program\":\"NCI Trials\",\"cancer_types\":[\"Head and Neck Squamous Cell Carcinoma\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"CT\",\"PT\",\"MR\",\"NM\",\"Classification\",\"Demographic\",\"Diagnosis\",\"Follow-Up\",\"Measurement\",\"Treatment\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":260,\"date_updated\":\"2021-11-23\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=ACRIN-HNSCC-FDG-PET-CT\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ACRIN-HNSCC-FDG-PET-CT-6685-Mar-2020.tcia\",\"blob\":\"acrin 6685 acrin-hnscc-fdg-pet-ct acrin-hnscc-fdg-pet\\\/ct (acrin 6685) is a multicenter trial of fdg-pet\\\/ct staging of head and neck cancer\\u00a0and its impact on the n0 neck surgical treatment in head and neck cancer patients.\\u00a0the objective of this study was to determine the negative predictive value (npv)\\u00a0of positron emission tomography (pet)\\\/computed tomography ct for clinically n0 neck based upon pathologic sampling of the neck lymph nodes and to\\u00a0determine pet\\\/ct\\u2019s potential to change treatment of the n0 neck.\\u00a0primary aim findings:[18f]fluorodeoxyglucose-pet\\\/ct has high npv for the n0 neck in t2 to t4 hnscc. the surgical treatment plans on the basis of pet\\\/ct findings may be changed in approximately 22% of this group. these findings suggest that [18f]fluorodeoxyglucose-pet\\\/ct may assist the clinician in deciding on the best therapy for the clinically n0 neck in hnscc. well-designed clinical trials should be performed to test the outcome of omitting neck dissection by using pet\\\/ct.study design summary:\\u00a0people with newly diagnosed head and neck squamous cell carcinoma being considered for surgical resection, with at least one side of the neck planned for dissection clinically n0, and at risk for occult metastasis (when risk based on clinical data is felt to be greater than 30%).\\u00a0a total of 287 participants were prospectively enrolled from 23 american college of radiology imaging network-qualified institutions. pet\\\/ct was compared with findings at neck dissection.two sets of xls spreadsheets (file set 1 and file set 2) are needed in order to obtain the entire clinical data set for this collection. the file sets are a random sample of acrin 6685 participants divided into 2 groups. group 1\\\/file set 1: a 75% random sample; group 2: a 25% random sample initially held for testing\\\/validating algorithms trained on the 75% sample.\\u00a0 head and neck squamous cell carcinoma head-neck ct pt mr nm classification demographic diagnosis follow-up measurement treatment clinical nci trials\"},{\"id\":41269,\"type\":\"collection\",\"slug\":\"acrin-nsclc-fdg-pet\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/acrin-nsclc-fdg-pet\\\/\",\"title\":\"ACRIN 6668\",\"short_title\":\"ACRIN-NSCLC-FDG-PET\",\"doi\":\"10.7937\\\/tcia.2019.30ilqfcl\",\"summary\":\"Positron Emission Tomography Pre- and Post-treatment Assessment for Locally Advanced Non-small Cell Lung Carcinoma\\u00a0This was a multicenter clinical trial by the ACRIN Cooperative Group (now part of ECOG-ACRIN) and the RTOG Cooperative Group (now part of NRG) using FDG-PET imaging both pre- and post-chemoradiotherapy.\\u00a0\\u00a0The objective of the ACRIN 6668 multi-center clinical trial was\\u00a0to determine if the PET standardized uptake value (SUV)\\u00a0measurement from FDG-PET imaging shortly after treatment is a useful predictor\\u00a0of long-term clinical outcome (survival) after definitive chemoradiotherapy.\\u00a0Eligible patients were those older than 18 years with AJCC-criteria clinical stage IIB\\\/III non-small cell lung carcinoma who were being planned for definitive concurrent chemoradiotherapy (inoperable disease). Additional information about the trial is available in the\\u00a0Study Protocol\\u00a0and\\u00a0Case Report Forms.Primary Aim Findings:Higher post-treatment tumor SUV (SUVpeak, SUVmax) is associated with worse survival in stage III NSCLC, although a clear SUV cutoff value for routine clinical use as a prognostic factor was uncertain. Later analyses found that larger pre-treatment metabolic tumor volumes (MTVs) were associated with significantly worse overall survival. Other secondary analyses found potentially predictive image texture biomarkers.Study Design Summary:\\u00a0Patients received conventional concurrent platinum-based chemoradiotherapy without surgery; post-radiotherapy consolidation chemotherapy was allowed. A baseline whole-body\\u00a0 FDG-PET scan was performed prior to therapy. A second post-treatment whole-body FDG-PET scan occurred approximately 14 weeks after radiotherapy (at least 4 weeks after adjuvant chemotherapy). Pre-treatment FDG-PET scans were performed on ACRIN-qualified scanners. Post-treatment FDG-PET scans were required to be performed within 12\\u201316 weeks after completion of therapy, using the same scanner as that used for the pre-treatment scans.Two sets of XLS spreadsheets (file set 1 and file set 2) are needed in order to obtain the entire clinical data set for this collection. The file sets are a random sample of ACRIN 6668 participants divided into 2 groups. Group 1\\\/file set 1: a 75% random sample; Group 2: a 25% random sample initially held for testing\\\/validating algorithms trained on the 75% sample.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image2019-1-15_17-48-59.png\",\"program\":\"NCI Trials\",\"cancer_types\":[\"Non-small Cell Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"PT\",\"CT\",\"NM\",\"MR\",\"CR\",\"DX\",\"SC\",\"Demographic\",\"Diagnosis\",\"Follow-Up\",\"Measurement\",\"Treatment\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":242,\"date_updated\":\"2020-02-11\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=ACRIN-NSCLC-FDG-PET\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ACRIN-6668-NSCLC-FDG-PET-FEB-04-2020-manifest.tcia\",\"blob\":\"acrin 6668 acrin-nsclc-fdg-pet positron emission tomography pre- and post-treatment assessment for locally advanced non-small cell lung carcinoma\\u00a0this was a multicenter clinical trial by the acrin cooperative group (now part of ecog-acrin) and the rtog cooperative group (now part of nrg) using fdg-pet imaging both pre- and post-chemoradiotherapy.\\u00a0\\u00a0the objective of the acrin 6668 multi-center clinical trial was\\u00a0to determine if the pet standardized uptake value (suv)\\u00a0measurement from fdg-pet imaging shortly after treatment is a useful predictor\\u00a0of long-term clinical outcome (survival) after definitive chemoradiotherapy.\\u00a0eligible patients were those older than 18 years with ajcc-criteria clinical stage iib\\\/iii non-small cell lung carcinoma who were being planned for definitive concurrent chemoradiotherapy (inoperable disease). additional information about the trial is available in the\\u00a0study protocol\\u00a0and\\u00a0case report forms.primary aim findings:higher post-treatment tumor suv (suvpeak, suvmax) is associated with worse survival in stage iii nsclc, although a clear suv cutoff value for routine clinical use as a prognostic factor was uncertain. later analyses found that larger pre-treatment metabolic tumor volumes (mtvs) were associated with significantly worse overall survival. other secondary analyses found potentially predictive image texture biomarkers.study design summary:\\u00a0patients received conventional concurrent platinum-based chemoradiotherapy without surgery; post-radiotherapy consolidation chemotherapy was allowed. a baseline whole-body\\u00a0 fdg-pet scan was performed prior to therapy. a second post-treatment whole-body fdg-pet scan occurred approximately 14 weeks after radiotherapy (at least 4 weeks after adjuvant chemotherapy). pre-treatment fdg-pet scans were performed on acrin-qualified scanners. post-treatment fdg-pet scans were required to be performed within 12\\u201316 weeks after completion of therapy, using the same scanner as that used for the pre-treatment scans.two sets of xls spreadsheets (file set 1 and file set 2) are needed in order to obtain the entire clinical data set for this collection. the file sets are a random sample of acrin 6668 participants divided into 2 groups. group 1\\\/file set 1: a 75% random sample; group 2: a 25% random sample initially held for testing\\\/validating algorithms trained on the 75% sample.\\u00a0 non-small cell lung cancer lung pt ct nm mr cr dx sc demographic diagnosis follow-up measurement treatment clinical nci trials\"},{\"id\":45451,\"type\":\"collection\",\"slug\":\"adrenal-acc-ki67-seg\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/adrenal-acc-ki67-seg\\\/\",\"title\":\"Voxel-level segmentation of pathologically-proven Adrenocortical carcinoma with Ki-67 expression\",\"short_title\":\"Adrenal-ACC-Ki67-Seg\",\"doi\":\"10.7937\\\/1FPG-VM46\",\"summary\":\"Adrenocortical carcinoma (ACC) is a rare tumor of the adrenal cortex with a reported annual incidence of one case per million population. ACC is a highly aggressive, highly fatal tumor with 5-year overall survival rates ranging from 14% to 44%. Diagnosis of ACC is primarily based on histopathological parameters from resected tumors, which include Ki-67 expression status. The Ki-67 index is one of the most important established prognostic markers for local recurrence of ACC. Radiomic feature extraction showed a significant association between radiomic signature and Ki-67 expression status in ACC.This retrospectively acquired data includes contrast enhanced CT imaging studies of 53 confirmed ACC patients between 2006 to 2018 with available clinical and pathological data, including Ki-67 index. Semi-automatic segmentation of the adrenal tumor was created using AMIRA, then manually refined by an experienced radiologist. Voxel level segmentation of the adrenal lesion are included as well. The segmentations of each contrast-enhanced CT were done for the purpose of radiomic features extraction.\\u00a0The participants in this dataset fulfilled these inclusion criteria:Pathologically proven Adrenocortical carcinomaUnderwent surgical resection of the tumourThe Ki-67 index was determined as part of the histopathological evaluation of the resected tissueImaging data (pre-resection contrast-enhanced CT of the abdomen) were available.\\u00a0Data from patients whose Ki-67 was quantified in biopsied tissue samples rather than from resected whole tumor, were excluded from this study. This exclusion was based on previous studies concluding that Ki-67 quantification should be based on tissue samples collected from the whole tumour.There was no publicly-available library for adrenal lesions prior to this dataset.\\u00a0 It can serve as a training set for machine learning algorithms for various purposes including segmentation and classification of adrenal tumors. We used the radiomic features extracted to predict the Ki-67 index (through regression) without the need of surgical intervention as described in this paper.\\u00a0\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Adrenal-ACC-Ki67-Seg_SelectedFigure.png\",\"program\":\"Community\",\"cancer_types\":[\"Adrenocortical Carcinoma\"],\"cancer_locations\":[\"Adrenal\"],\"data_types\":[\"CT\",\"SEG\",\"Demographic\",\"Diagnosis\",\"Follow-Up\",\"Measurement\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":53,\"date_updated\":\"2023-05-15\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=Adrenal-ACC-Ki67-Seg\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Adrenal-ACC-Ki67-Seg_v1.tcia\",\"blob\":\"voxel-level segmentation of pathologically-proven adrenocortical carcinoma with ki-67 expression adrenal-acc-ki67-seg adrenocortical carcinoma (acc) is a rare tumor of the adrenal cortex with a reported annual incidence of one case per million population. acc is a highly aggressive, highly fatal tumor with 5-year overall survival rates ranging from 14% to 44%. diagnosis of acc is primarily based on histopathological parameters from resected tumors, which include ki-67 expression status. the ki-67 index is one of the most important established prognostic markers for local recurrence of acc. radiomic feature extraction showed a significant association between radiomic signature and ki-67 expression status in acc.this retrospectively acquired data includes contrast enhanced ct imaging studies of 53 confirmed acc patients between 2006 to 2018 with available clinical and pathological data, including ki-67 index. semi-automatic segmentation of the adrenal tumor was created using amira, then manually refined by an experienced radiologist. voxel level segmentation of the adrenal lesion are included as well. the segmentations of each contrast-enhanced ct were done for the purpose of radiomic features extraction.\\u00a0the participants in this dataset fulfilled these inclusion criteria:pathologically proven adrenocortical carcinomaunderwent surgical resection of the tumourthe ki-67 index was determined as part of the histopathological evaluation of the resected tissueimaging data (pre-resection contrast-enhanced ct of the abdomen) were available.\\u00a0data from patients whose ki-67 was quantified in biopsied tissue samples rather than from resected whole tumor, were excluded from this study. this exclusion was based on previous studies concluding that ki-67 quantification should be based on tissue samples collected from the whole tumour.there was no publicly-available library for adrenal lesions prior to this dataset.\\u00a0 it can serve as a training set for machine learning algorithms for various purposes including segmentation and classification of adrenal tumors. we used the radiomic features extracted to predict the ki-67 index (through regression) without the need of surgical intervention as described in this paper.\\u00a0\\u00a0 adrenocortical carcinoma adrenal ct seg demographic diagnosis follow-up measurement clinical image analyses community\"},{\"id\":43669,\"type\":\"collection\",\"slug\":\"advanced-mri-breast-lesions\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/advanced-mri-breast-lesions\\\/\",\"title\":\"Standard and Delayed Contrast-Enhanced MRI of Malignant and Benign Breast Lesions with Histological and Clinical Supporting Data\",\"short_title\":\"Advanced-MRI-Breast-Lesions\",\"doi\":\"10.7937\\\/C7X1-YN57\",\"summary\":\"The application of deep-learning methods to breast MRI has a potential to improve diagnosis as well as predict pathological features of the tumors and their response to therapy, and is currently widely investigated. Standard contrast-enhanced MRI of the breast provides high sensitivity but variable specificity in detecting breast cancer, and may lead to excessive benign biopsies. Large, well annotated datasets are needed to improve the current results in this field.This dataset is a single-institutional, retrospective collection of 632 breast-MRI imaging sessions acquired on a 1.5T MR system between 2018-2021. Supporting data, collected for 200 patients from clinical notes, radiology reports and pathology reports, is included: patient age, lesion location, malignant\\\/benign outcome, pathology and grade (if applicable), receptor status and KI67 (if applicable).All enhancing\\\/suspicious positions that were mentioned in the radiological reports were first delineated manually by a certified radiologist with 23 years of experience. Then, these regions were thresholded automatically, keeping the pixels in which the intensity of the first\\\/second subtracted images (subtraction of the pre-contrast image from the first\\\/second post-contrast image) were above the threshold of 75.The main uniqueness of this data is that it contains the standard protocol series (T1-weighted DCE sequence with 5 time points- before and 4 time points after contrast injection, and T2-weighted MRI with\\\/without fat suppression) as well as the delayed contrast T1-images acquired 20-28 minutes after contrast administration and the calculated treatment response assessment maps (TRAMs).We recently introduced the utility of delayed-contrast MRI for calculating treatment response assessment maps (TRAMs) for improved specificity and accuracy in breast cancer diagnosis. The TRAMs are calculated by subtracting 3D T1-MRIs acquired 5 minutes post-contrast injection from those acquired ~22 minutes thus depicting the spatial distribution of contrast accumulation\\\/clearance. Furthermore, the application of deep-learning methods to breast MRI has a potential to improve diagnosis as well as predict pathological features of the tumors and their response to therapy.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Advanced-MRI-Breast-Lesions_collection1_cropped.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MR\",\"SEG\",\"Histopathology\",\"Molecular Test\",\"Classification\",\"Demographic\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":632,\"date_updated\":\"2024-04-15\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Advanced-MRI-Breast-Lesions\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Advanced-MRI-Breast-Lesions-DA-RAD-APR2024.tcia\",\"blob\":\"standard and delayed contrast-enhanced mri of malignant and benign breast lesions with histological and clinical supporting data advanced-mri-breast-lesions the application of deep-learning methods to breast mri has a potential to improve diagnosis as well as predict pathological features of the tumors and their response to therapy, and is currently widely investigated. standard contrast-enhanced mri of the breast provides high sensitivity but variable specificity in detecting breast cancer, and may lead to excessive benign biopsies. large, well annotated datasets are needed to improve the current results in this field.this dataset is a single-institutional, retrospective collection of 632 breast-mri imaging sessions acquired on a 1.5t mr system between 2018-2021. supporting data, collected for 200 patients from clinical notes, radiology reports and pathology reports, is included: patient age, lesion location, malignant\\\/benign outcome, pathology and grade (if applicable), receptor status and ki67 (if applicable).all enhancing\\\/suspicious positions that were mentioned in the radiological reports were first delineated manually by a certified radiologist with 23 years of experience. then, these regions were thresholded automatically, keeping the pixels in which the intensity of the first\\\/second subtracted images (subtraction of the pre-contrast image from the first\\\/second post-contrast image) were above the threshold of 75.the main uniqueness of this data is that it contains the standard protocol series (t1-weighted dce sequence with 5 time points- before and 4 time points after contrast injection, and t2-weighted mri with\\\/without fat suppression) as well as the delayed contrast t1-images acquired 20-28 minutes after contrast administration and the calculated treatment response assessment maps (trams).we recently introduced the utility of delayed-contrast mri for calculating treatment response assessment maps (trams) for improved specificity and accuracy in breast cancer diagnosis. the trams are calculated by subtracting 3d t1-mris acquired 5 minutes post-contrast injection from those acquired ~22 minutes thus depicting the spatial distribution of contrast accumulation\\\/clearance. furthermore, the application of deep-learning methods to breast mri has a potential to improve diagnosis as well as predict pathological features of the tumors and their response to therapy. breast cancer breast mr seg histopathology molecular test classification demographic clinical community\"},{\"id\":43509,\"type\":\"collection\",\"slug\":\"ahep0731\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ahep0731\\\/\",\"title\":\"Risk-Based Therapy in Treating Younger Patients With Newly Diagnosed Liver Cancer\",\"short_title\":\"AHEP0731\",\"doi\":\"10.7937\\\/F2DB-8826\",\"summary\":\"This collection contains data from the National Cancer Institute Clinical Trial NCT00980460, \\\"Risk-Based Therapy in Treating Younger Patients With Newly Diagnosed Liver Cancer.\\\" It was sponsored by NCI's Children\\u2019s Oncology Group (COG) under study number AHEP0731. This phase III trial studies the side effects and how well risk-based therapy works in treating younger patients with newly diagnosed liver cancer. Select individual patient-level data from this trial can be requested from the NCTN\\\/NCORP Data Archive.Trial DescriptionSurgery, chemotherapy drugs (cancer fighting medicines), and when necessary, liver transplant, are the main current treatments for hepatoblastoma. The stage of the cancer is one factor used to decide the best treatment. Treating patients according to the risk group they are in may help get rid of the cancer, keep it from coming back, and decrease the side effects of chemotherapy.Hepatoblastoma treatment with curative intent requires surgical resection, but only about a third of newly diagnosed patients with hepatoblastoma have resectable disease at diagnosis. Patients who have upfront resection typically receive a total of 4\\u20136 cycles of adjuvant chemotherapy post-surgery, with the combination of cisplatin, fluorouracil, and vincristine. The aim is to investigate whether event-free survival in children with hepatoblastoma who had complete resection at diagnosis could be maintained with two cycles of adjuvant chemotherapy.\\u00a0 This multicentre, phase 3 trial was designed to test a risk-based treatment approach for children with hepatoblastoma, to diminish toxicity in low-risk patients, improve survival in intermediate-risk patients, and identify new agents that may be used in high-risk and recurrent patients.\\u00a0 Patients were staged for risk classification using the Children\\u2019s Oncology Group staging guidelines before the initiation of chemotherapy, with stage IV indicating metastatic disease. \\u00a0Pretreatment extent of disease (PRETEXT) grouping also was performed at the time of diagnosis and with any subsequent abdominal computed tomography or magnetic resonance imaging and was used to guide the surgical management but was not used for risk classification.\\u00a0 The response rate and outcome to the combination of vincristine and irinotecan administered in an upfront window to children newly diagnosed with high-risk hepatoblastoma was determined.For Low-Risk patients CT chest was used for metastatic tumor response assessment.\\u00a0 Abdominal Ultrasound was obtained at baseline.\\u00a0 For Intermediate- and High-Risk patients abdominal ultrasound, CT and\\\/or MRI was used for primary tumor response assessment and CT chest for metastatic tumor response assessment.Trial Outcomes Results of the trial for Low-Risk patients have been reported in the following publication:Katzenstein, H. M., Langham, M. R., Malogolowkin, M. H., Krailo, M. D., Towbin, A. J., McCarville, M. B., Finegold, M. J., Ranganathan, S., Dunn, S., McGahren, E. D., Tiao, G. M., O\\u2019Neill, A. F., Qayed, M., Furman, W. L., Xia, C., Rodriguez-Galindo, C., & Meyers, R. L. (2019). Minimal adjuvant chemotherapy for children with hepatoblastoma resected at diagnosis (AHEP0731): a Children\\u2019s Oncology Group, multicentre, phase 3 trial. The Lancet Oncology, 20(5), 719\\u2013727. DOI:\\u00a0https:\\\/\\\/doi.org\\\/10.1016\\\/s1470-2045(18)30895-7.\\u00a0Epub 2019 Apr 8. Erratum in: Lancet Oncol. 2019 May;20(5):e243. PMID: 30975630; PMCID: PMC6499702. Epub 2019 Apr 8. Erratum in: Lancet Oncol. 2019 May;20(5):e243. PMID: 30975630; PMCID: PMC6499702.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/nctn-logo.png\",\"program\":\"NCTN\",\"cancer_types\":[\"Hepatoblastoma\"],\"cancer_locations\":[\"Liver\"],\"data_types\":[\"CT\",\"US\",\"MR\",\"PT\",\"XA\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":190,\"date_updated\":\"2024-10-30\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=AHEP0731\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/AHEP0731_v2_20241030_Restricted.tcia\",\"blob\":\"risk-based therapy in treating younger patients with newly diagnosed liver cancer ahep0731 this collection contains data from the national cancer institute clinical trial nct00980460, \\\"risk-based therapy in treating younger patients with newly diagnosed liver cancer.\\\" it was sponsored by nci's children\\u2019s oncology group (cog) under study number ahep0731. this phase iii trial studies the side effects and how well risk-based therapy works in treating younger patients with newly diagnosed liver cancer. select individual patient-level data from this trial can be requested from the nctn\\\/ncorp data archive.trial descriptionsurgery, chemotherapy drugs (cancer fighting medicines), and when necessary, liver transplant, are the main current treatments for hepatoblastoma. the stage of the cancer is one factor used to decide the best treatment. treating patients according to the risk group they are in may help get rid of the cancer, keep it from coming back, and decrease the side effects of chemotherapy.hepatoblastoma treatment with curative intent requires surgical resection, but only about a third of newly diagnosed patients with hepatoblastoma have resectable disease at diagnosis. patients who have upfront resection typically receive a total of 4\\u20136 cycles of adjuvant chemotherapy post-surgery, with the combination of cisplatin, fluorouracil, and vincristine. the aim is to investigate whether event-free survival in children with hepatoblastoma who had complete resection at diagnosis could be maintained with two cycles of adjuvant chemotherapy.\\u00a0 this multicentre, phase 3 trial was designed to test a risk-based treatment approach for children with hepatoblastoma, to diminish toxicity in low-risk patients, improve survival in intermediate-risk patients, and identify new agents that may be used in high-risk and recurrent patients.\\u00a0 patients were staged for risk classification using the children\\u2019s oncology group staging guidelines before the initiation of chemotherapy, with stage iv indicating metastatic disease. \\u00a0pretreatment extent of disease (pretext) grouping also was performed at the time of diagnosis and with any subsequent abdominal computed tomography or magnetic resonance imaging and was used to guide the surgical management but was not used for risk classification.\\u00a0 the response rate and outcome to the combination of vincristine and irinotecan administered in an upfront window to children newly diagnosed with high-risk hepatoblastoma was determined.for low-risk patients ct chest was used for metastatic tumor response assessment.\\u00a0 abdominal ultrasound was obtained at baseline.\\u00a0 for intermediate- and high-risk patients abdominal ultrasound, ct and\\\/or mri was used for primary tumor response assessment and ct chest for metastatic tumor response assessment.trial outcomes results of the trial for low-risk patients have been reported in the following publication:katzenstein, h. m., langham, m. r., malogolowkin, m. h., krailo, m. d., towbin, a. j., mccarville, m. b., finegold, m. j., ranganathan, s., dunn, s., mcgahren, e. d., tiao, g. m., o\\u2019neill, a. f., qayed, m., furman, w. l., xia, c., rodriguez-galindo, c., & meyers, r. l. (2019). minimal adjuvant chemotherapy for children with hepatoblastoma resected at diagnosis (ahep0731): a children\\u2019s oncology group, multicentre, phase 3 trial. the lancet oncology, 20(5), 719\\u2013727. doi:\\u00a0https:\\\/\\\/doi.org\\\/10.1016\\\/s1470-2045(18)30895-7.\\u00a0epub 2019 apr 8. erratum in: lancet oncol. 2019 may;20(5):e243. pmid: 30975630; pmcid: pmc6499702. epub 2019 apr 8. erratum in: lancet oncol. 2019 may;20(5):e243. pmid: 30975630; pmcid: pmc6499702. hepatoblastoma liver ct us mr pt xa clinical nctn\"},{\"id\":41853,\"type\":\"collection\",\"slug\":\"ahod0831\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ahod0831\\\/\",\"title\":\"Combination Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed Hodgkin Lymphoma\",\"short_title\":\"AHOD0831\",\"doi\":\"10.7937\\\/CV5M-1H59\",\"summary\":\"This collection contains data from the Children\\u2019s Oncology Group (COG) Clinical Trial NCT01026220, \\u201cCombination Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed Hodgkin Lymphoma\\\". Principal Investigator: Kara Kelly, MD (pediatric oncologist and the Chair of Roswell Park Oishei Children\\u2019s Cancer and Blood Disorders Program, Buffalo, NY). It was sponsored by NCI and performed by the Children's Oncology Group under study number AHOD0831. This phase III trial is studying how well giving combination chemotherapy together with radiation therapy works in treating young patients with newly diagnosed Hodgkin lymphoma. Therapeutic agents used on trial include 1 or more of the following: Biological: bleomycin sulfate; Drug: doxorubicin hydrochloride; Drug: liposomal vincristine sulfate; Drug: vinorelbine tartrate; Drug: cyclophosphamide; Drug: etoposide phosphate; Drug: prednisone; Biological: filgrastimDrug: ifosfamide. Select patient-level clinical data from this trial is available via the following link: https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/1137.Trial Primary Objective:I. To maintain the overall survival (as defined by 4-year \\\"second-event\\\" free survival) for subjects with high risk Hodgkin lymphoma at or above 95%.Trial Secondary Objectives:I. To maintain 3-year event-free survival for subjects with high risk Hodgkin lymphoma at or above 93%.II. To maintain comparable overall survival (as defined by 4-year \\\"second-event\\\" free survival) between subjects with high risk Hodgkin lymphoma who have a rapid or slow response to the initial 2 cycles of ABVE-PC* by intensifying therapy through the addition of 2 cycles of ifosfamide\\\/vinorelbine in those with a slow early response.III. To investigate whether very early response assessment measured by FDG-PET after 1 cycle of chemotherapy identifies a subject cohort that can be studied in future trials and that is distinguishable from currently defined RER after 2 cycles.IV. To describe the patterns of relapse after ABVE-PC* and risk-adapted radiotherapy.Trial Description and OutcomesThe AHOD0831 study for pediatric patients with high risk Hodgkin lymphoma tested a response-based approach designed to limit cumulative alkylator exposure and reduce radiation volumes. 166 patients were enrolled in this study from December 2009 to January 2012, of whom 165 were eligible and 141 completed the trial. This dataset contains the images from the 165 eligible patients.\\u00a0 Study dates were approximately 2009-2017.Patients (Stage IIIB\\\/IVB) received two cycles of ABVE-PC (doxorubicin, bleomycin, vincristine, etoposide, prednisone, cyclophosphamide). Rapid early responders [RER, no positron emission tomography (PET) activity above mediastinal blood pool] were consolidated with 2 cycles of ABVE-PC. Slow early responders(SER) received 2 cycles of ifosfamide\\\/vinorelbine and 2 cycles of ABVE-PC.Radiotherapy was administered to sites of initial bulk and\\\/or SER. By intent-to-treat analysis, 4-year second event-free survival (EFS; freedom from second relapse or malignancy) was 91.9% [95% con\\ufb01dence interval(CI): 86.1 \\u2013 95.3%], below the projected baseline of 95% (P = 0.038). Five-year \\ufb01rst EFS and overall survival (OS) rates are 79.1% (95% CI: 71.5 \\u201384.8%) and 95% (95% CI: 88.8 \\u2013 97.8%). Eight of 11 SER patients with persistent PET positive lesions at the end of chemotherapy had clinical evidence of active disease (3 biopsy-proven, 5 with progressive disease or later relapses). Although this response-directed approach did not reach the ambitiously high pre-speci\\ufb01ed target for second EFS, EFS and OS rates are comparable with results of recent trials despite the reduction in radiotherapy volumes from historical involved \\ufb01elds. Persistent PET at end of chemotherapy identi\\ufb01es a cohort at an especially high risk for relapse\\\/early progression.Trial PublicationResults of the trial have been reported in the following publication:Kelly KM, Cole PD, Pei Q, Bush R, Roberts KB, Hodgson DC, McCarten KM, Cho SY, Schwartz C. Response-adapted therapy for the treatment of children with newly diagnosed high risk Hodgkin lymphoma (AHOD0831): a report from the Children's Oncology Group. Br J Haematol. 2019 Oct;187(1):39-48. doi: 10.1111\\\/bjh.16014. Epub 2019 Jun 10. PMID: 31180135; PMCID: PMC6857800.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/nctn-logo.png\",\"program\":\"NCTN\",\"cancer_types\":[\"Hodgkin Lymphoma\"],\"cancer_locations\":[\"Various\"],\"data_types\":[\"CT\",\"PT\",\"MR\",\"NM\",\"CR\",\"DX\",\"SC\",\"XA\",\"OT\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":165,\"date_updated\":\"2022-06-08\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=AHOD0831\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/AHOD0831-manifest-June-2022.tcia\",\"blob\":\"combination chemotherapy and radiation therapy in treating young patients with newly diagnosed hodgkin lymphoma ahod0831 this collection contains data from the children\\u2019s oncology group (cog) clinical trial nct01026220, \\u201ccombination chemotherapy and radiation therapy in treating young patients with newly diagnosed hodgkin lymphoma\\\". principal investigator: kara kelly, md (pediatric oncologist and the chair of roswell park oishei children\\u2019s cancer and blood disorders program, buffalo, ny). it was sponsored by nci and performed by the children's oncology group under study number ahod0831. this phase iii trial is studying how well giving combination chemotherapy together with radiation therapy works in treating young patients with newly diagnosed hodgkin lymphoma. therapeutic agents used on trial include 1 or more of the following: biological: bleomycin sulfate; drug: doxorubicin hydrochloride; drug: liposomal vincristine sulfate; drug: vinorelbine tartrate; drug: cyclophosphamide; drug: etoposide phosphate; drug: prednisone; biological: filgrastimdrug: ifosfamide. select patient-level clinical data from this trial is available via the following link: https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/1137.trial primary objective:i. to maintain the overall survival (as defined by 4-year \\\"second-event\\\" free survival) for subjects with high risk hodgkin lymphoma at or above 95%.trial secondary objectives:i. to maintain 3-year event-free survival for subjects with high risk hodgkin lymphoma at or above 93%.ii. to maintain comparable overall survival (as defined by 4-year \\\"second-event\\\" free survival) between subjects with high risk hodgkin lymphoma who have a rapid or slow response to the initial 2 cycles of abve-pc* by intensifying therapy through the addition of 2 cycles of ifosfamide\\\/vinorelbine in those with a slow early response.iii. to investigate whether very early response assessment measured by fdg-pet after 1 cycle of chemotherapy identifies a subject cohort that can be studied in future trials and that is distinguishable from currently defined rer after 2 cycles.iv. to describe the patterns of relapse after abve-pc* and risk-adapted radiotherapy.trial description and outcomesthe ahod0831 study for pediatric patients with high risk hodgkin lymphoma tested a response-based approach designed to limit cumulative alkylator exposure and reduce radiation volumes. 166 patients were enrolled in this study from december 2009 to january 2012, of whom 165 were eligible and 141 completed the trial. this dataset contains the images from the 165 eligible patients.\\u00a0 study dates were approximately 2009-2017.patients (stage iiib\\\/ivb) received two cycles of abve-pc (doxorubicin, bleomycin, vincristine, etoposide, prednisone, cyclophosphamide). rapid early responders [rer, no positron emission tomography (pet) activity above mediastinal blood pool] were consolidated with 2 cycles of abve-pc. slow early responders(ser) received 2 cycles of ifosfamide\\\/vinorelbine and 2 cycles of abve-pc.radiotherapy was administered to sites of initial bulk and\\\/or ser. by intent-to-treat analysis, 4-year second event-free survival (efs; freedom from second relapse or malignancy) was 91.9% [95% con\\ufb01dence interval(ci): 86.1 \\u2013 95.3%], below the projected baseline of 95% (p = 0.038). five-year \\ufb01rst efs and overall survival (os) rates are 79.1% (95% ci: 71.5 \\u201384.8%) and 95% (95% ci: 88.8 \\u2013 97.8%). eight of 11 ser patients with persistent pet positive lesions at the end of chemotherapy had clinical evidence of active disease (3 biopsy-proven, 5 with progressive disease or later relapses). although this response-directed approach did not reach the ambitiously high pre-speci\\ufb01ed target for second efs, efs and os rates are comparable with results of recent trials despite the reduction in radiotherapy volumes from historical involved \\ufb01elds. persistent pet at end of chemotherapy identi\\ufb01es a cohort at an especially high risk for relapse\\\/early progression.trial publicationresults of the trial have been reported in the following publication:kelly km, cole pd, pei q, bush r, roberts kb, hodgson dc, mccarten km, cho sy, schwartz c. response-adapted therapy for the treatment of children with newly diagnosed high risk hodgkin lymphoma (ahod0831): a report from the children's oncology group. br j haematol. 2019 oct;187(1):39-48. doi: 10.1111\\\/bjh.16014. epub 2019 jun 10. pmid: 31180135; pmcid: pmc6857800. hodgkin lymphoma various ct pt mr nm cr dx sc xa ot clinical nctn\"},{\"id\":41493,\"type\":\"collection\",\"slug\":\"aml-cytomorphology_lmu\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/aml-cytomorphology_lmu\\\/\",\"title\":\"A Single-cell Morphological Dataset of Leukocytes from AML Patients and Non-malignant Controls\",\"short_title\":\"AML-Cytomorphology_LMU\",\"doi\":\"10.7937\\\/tcia.2019.36f5o9ld\",\"summary\":\"The Munich AML Morphology Dataset contains 18,365 expert-labeled single-cell images taken from peripheral blood smears of 100 patients diagnosed with Acute Myeloid Leukemia at Munich University Hospital between 2014 and 2017, as well as 100 patients without signs of hematological malignancy. Image acquisition was done using a M8 digital microscope \\\/ scanner (Precipoint GmbH, Freising, Germany) at 100-fold optical magnification and oil immersion. Pathological and non-pathological leukocytes were classified into a standard morphological classification scheme derived from clinical practice by trained experts. To quantify inter- and intra-rater variability of examiners, a subset of images was re-annotated up to two times. The dataset has been used by the authors to train a convolutional neural network for single-cell morphology classification.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Acute Myeloid Leukemia\"],\"cancer_locations\":[\"Blood\"],\"data_types\":[\"Histopathology\",\"Other\",\"Classification\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":200,\"date_updated\":\"2019-10-24\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22AML-Cytomorphology_LMU%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjczNSIsInBhc3Njb2RlIjoiYTczZTE1NzU1MjI5MzZkODRhZTY3MTcxMmU1YTg2YWY1ZTZlODI4MyIsInBhY2thZ2VfaWQiOiI3MzUiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"a single-cell morphological dataset of leukocytes from aml patients and non-malignant controls aml-cytomorphology_lmu the munich aml morphology dataset contains 18,365 expert-labeled single-cell images taken from peripheral blood smears of 100 patients diagnosed with acute myeloid leukemia at munich university hospital between 2014 and 2017, as well as 100 patients without signs of hematological malignancy. image acquisition was done using a m8 digital microscope \\\/ scanner (precipoint gmbh, freising, germany) at 100-fold optical magnification and oil immersion. pathological and non-pathological leukocytes were classified into a standard morphological classification scheme derived from clinical practice by trained experts. to quantify inter- and intra-rater variability of examiners, a subset of images was re-annotated up to two times. the dataset has been used by the authors to train a convolutional neural network for single-cell morphology classification. acute myeloid leukemia blood histopathology other classification image analyses community\"},{\"id\":41375,\"type\":\"collection\",\"slug\":\"aml-cytomorphology_mll_helmholtz\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/aml-cytomorphology_mll_helmholtz\\\/\",\"title\":\"A morphological dataset of white blood cells from patients with four different genetic AML entities and non-malignant controls\",\"short_title\":\"AML-Cytomorphology_MLL_Helmholtz\",\"doi\":\"10.7937\\\/6PPE-4020\",\"summary\":\"This dataset comprises four prevalent AML subtypes with defining genetic abnormalities and typical morphological features according to the WHO 2022 classification: (i) APL with PML::RARA fusion, (ii) AML with NPM1 mutation, (iii) AML with CBFB::MYH11 fusion (without NPM1 mutation), and (iv) AML with RUNX1::RUNX1T1 fusion, as well as a control group of healthy stem cell donors.\\u00a0A total of 189 peripheral blood smears from the Munich Leukemia Laboratory (MLL) database from the years 2009 to 2020 were digitized.\\u00a0First, all blood smears were scanned with 10x magnification and an overview image was created. Using the Metasystems Metafer platform, cell detection was performed automatically using a segmentation threshold and logarithmic color transformation. Further analysis regarding the quality of the region within the blood smear was performed automatically. Per patient, 99-500 white blood cells were then scanned in 40x magnification via oil immersion microscopy in .TIF format, corresponding to 24,9\\u03bcm x 24,9\\u03bcm (144x144 pixels). For this, a CMOS Color Camera from MetaSystems with a resolution of 4096x3000px and a pixel size of 3,45\\u03bcm x 3,45\\u03bcm was used. Four pixels were binned into one, leading to a size of 6.9\\u03bcm x 6.9\\u03bcm, and a resolution of 6.9\\u03bcm \\\/ 40 (1px = 0,1725\\u03bcm).\\u00a0Additional information about patient age, sex and blood counts are provided in a separate .csv file.To our knowledge, this dataset covers the morphological complexity of acute myeloid leukemia in peripheral blood smears in unseen quality and quantity.\\u00a0\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Acute Myeloid Leukemia\"],\"cancer_locations\":[\"Blood\"],\"data_types\":[\"Histopathology\",\"Demographic\",\"Measurement\",\"Diagnosis\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":189,\"date_updated\":\"2023-10-13\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22AML-Cytomorphology_MLL_Helmholtz%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjY2NyIsInBhc3Njb2RlIjoiYTljMjRlMmQ1NTcwNDc1NzRjOGU4ZTU5NjIwY2IxMjcyOTEwOGRkOSIsInBhY2thZ2VfaWQiOiI2NjciLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"a morphological dataset of white blood cells from patients with four different genetic aml entities and non-malignant controls aml-cytomorphology_mll_helmholtz this dataset comprises four prevalent aml subtypes with defining genetic abnormalities and typical morphological features according to the who 2022 classification: (i) apl with pml::rara fusion, (ii) aml with npm1 mutation, (iii) aml with cbfb::myh11 fusion (without npm1 mutation), and (iv) aml with runx1::runx1t1 fusion, as well as a control group of healthy stem cell donors.\\u00a0a total of 189 peripheral blood smears from the munich leukemia laboratory (mll) database from the years 2009 to 2020 were digitized.\\u00a0first, all blood smears were scanned with 10x magnification and an overview image was created. using the metasystems metafer platform, cell detection was performed automatically using a segmentation threshold and logarithmic color transformation. further analysis regarding the quality of the region within the blood smear was performed automatically. per patient, 99-500 white blood cells were then scanned in 40x magnification via oil immersion microscopy in .tif format, corresponding to 24,9\\u03bcm x 24,9\\u03bcm (144x144 pixels). for this, a cmos color camera from metasystems with a resolution of 4096x3000px and a pixel size of 3,45\\u03bcm x 3,45\\u03bcm was used. four pixels were binned into one, leading to a size of 6.9\\u03bcm x 6.9\\u03bcm, and a resolution of 6.9\\u03bcm \\\/ 40 (1px = 0,1725\\u03bcm).\\u00a0additional information about patient age, sex and blood counts are provided in a separate .csv file.to our knowledge, this dataset covers the morphological complexity of acute myeloid leukemia in peripheral blood smears in unseen quality and quantity.\\u00a0 acute myeloid leukemia blood histopathology demographic measurement diagnosis clinical community\"},{\"id\":41419,\"type\":\"collection\",\"slug\":\"anti-pd-1_lung\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/anti-pd-1_lung\\\/\",\"title\":\"Anti-PD-1 Immunotherapy Lung\",\"short_title\":\"Anti-PD-1_Lung\",\"doi\":\"10.7937\\\/tcia.2019.zjjwb9ip\",\"summary\":\"This collection includes 46 lung cases treated with\\u00a0anti-PD1 immunotherapy in 2016, each with pre-treatment and most with 1 imaging follow-up timepoint.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"PT\",\"CT\",\"SC\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":46,\"date_updated\":\"2019-04-19\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Anti-PD-1_Lung\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Anti-PD-1-LungNBIA-manifest-1555593712098.tcia\",\"blob\":\"anti-pd-1 immunotherapy lung anti-pd-1_lung this collection includes 46 lung cases treated with\\u00a0anti-pd1 immunotherapy in 2016, each with pre-treatment and most with 1 imaging follow-up timepoint. lung cancer lung pt ct sc community\"},{\"id\":41425,\"type\":\"collection\",\"slug\":\"anti-pd-1_melanoma\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/anti-pd-1_melanoma\\\/\",\"title\":\"Anti-PD-1 Immunotherapy Melanoma\",\"short_title\":\"Anti-PD-1_MELANOMA\",\"doi\":\"10.7937\\\/tcia.2019.1ae0qtcu\",\"summary\":\"This collection includes 47 melanoma cases treated with anti-PD1 immunotherapy in 2016, each with pre-treatment and 1 or more imaging follow-up timepoints.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Melanoma\"],\"cancer_locations\":[\"Skin\"],\"data_types\":[\"MR\",\"CT\",\"PT\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":47,\"date_updated\":\"2018-07-30\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=Anti-PD-1_MELANOMA\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Anti-PD-1_MELANOMA-7-30-2018.tcia\",\"blob\":\"anti-pd-1 immunotherapy melanoma anti-pd-1_melanoma this collection includes 47 melanoma cases treated with anti-pd1 immunotherapy in 2016, each with pre-treatment and 1 or more imaging follow-up timepoints. melanoma skin mr ct pt community\"},{\"id\":43397,\"type\":\"collection\",\"slug\":\"arar0331\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/arar0331\\\/\",\"title\":\"Radiation Therapy, Amifostine, and Chemotherapy in Treating Young Patients With Newly Diagnosed Nasopharyngeal Cancer\",\"short_title\":\"ARAR0331\",\"doi\":\"10.7937\\\/WTEC-MN22\",\"summary\":\"This collection contains data from the Children\\u2019s Oncology Group (COG) Clinical Trial NCT00274937, \\u201cRadiation Therapy, Amifostine, and Chemotherapy in Treating Young Patients With Newly Diagnosed Nasopharyngeal Cancer\\\". Principal Investigator: Carlos Rodriguez-Galindo, MD.\\u00a0 It was sponsored by NCI and performed by the Children's Oncology Group under study number ARAR0331. This phase III trial is studying how well radiation therapy, amifostine, and chemotherapy work in treating young patients with newly diagnosed nasopharyngeal cancer. Radiation therapy uses high-energy x-rays to kill tumor cells. Drugs, such as amifostine, may protect normal cells from the side effects of radiation therapy. Drugs used in chemotherapy, such as cisplatin and fluorouracil, work in different ways to stop the growth of tumor cells, either by killing the cells or by stopping them from dividing. Giving radiation therapy together with amifostine and chemotherapy may kill more tumor cells. Select patient-level data from this trial is available via the following link: https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/1302. \\u00a0Trial Outcomes Results of the trial have been reported in the following publications:Jeha S, Pei D, Choi J, Cheng C, Sandlund JT, Coustan-Smith E, Campana D, Inaba H, Rubnitz JE, Ribeiro RC, Gruber TA, Raimondi SC, Khan RB, Yang JJ, Mullighan CG, Downing JR, Evans WE, Relling MV, Pui CH. Improved CNS Control of Childhood Acute Lymphoblastic Leukemia Without Cranial Irradiation: St Jude Total Therapy Study 16. J Clin Oncol. 2019 Dec 10;37(35):3377-3391. doi: 10.1200\\\/JCO.19.01692. Epub 2019 Oct 28.Rodriguez-Galindo C, Krailo MD, Krasin MJ, Huang L, McCarville MB, Hicks J, Pashankar F, Pappo AS. Treatment of Childhood Nasopharyngeal Carcinoma With Induction Chemotherapy and Concurrent Chemoradiotherapy: Results of the Children's Oncology Group ARAR0331 Study. J Clin Oncol. 2019 Dec 10;37(35):3369-3376. doi: 10.1200\\\/JCO.19.01276. Epub 2019 Sep 25. Erratum in: J Clin Oncol. 2021 May 10;39(14):1602.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/nctn-logo.png\",\"program\":\"NCTN\",\"cancer_types\":[\"Nasopharyngeal\"],\"cancer_locations\":[\"Head\"],\"data_types\":[\"MR\",\"PT\",\"CT\",\"NM\",\"RTIMAGE\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":108,\"date_updated\":\"2022-10-21\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=ARAR0331\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ARAR0331-manifest-September-2022.tcia\",\"blob\":\"radiation therapy, amifostine, and chemotherapy in treating young patients with newly diagnosed nasopharyngeal cancer arar0331 this collection contains data from the children\\u2019s oncology group (cog) clinical trial nct00274937, \\u201cradiation therapy, amifostine, and chemotherapy in treating young patients with newly diagnosed nasopharyngeal cancer\\\". principal investigator: carlos rodriguez-galindo, md.\\u00a0 it was sponsored by nci and performed by the children's oncology group under study number arar0331. this phase iii trial is studying how well radiation therapy, amifostine, and chemotherapy work in treating young patients with newly diagnosed nasopharyngeal cancer. radiation therapy uses high-energy x-rays to kill tumor cells. drugs, such as amifostine, may protect normal cells from the side effects of radiation therapy. drugs used in chemotherapy, such as cisplatin and fluorouracil, work in different ways to stop the growth of tumor cells, either by killing the cells or by stopping them from dividing. giving radiation therapy together with amifostine and chemotherapy may kill more tumor cells. select patient-level data from this trial is available via the following link: https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/1302. \\u00a0trial outcomes results of the trial have been reported in the following publications:jeha s, pei d, choi j, cheng c, sandlund jt, coustan-smith e, campana d, inaba h, rubnitz je, ribeiro rc, gruber ta, raimondi sc, khan rb, yang jj, mullighan cg, downing jr, evans we, relling mv, pui ch. improved cns control of childhood acute lymphoblastic leukemia without cranial irradiation: st jude total therapy study 16. j clin oncol. 2019 dec 10;37(35):3377-3391. doi: 10.1200\\\/jco.19.01692. epub 2019 oct 28.rodriguez-galindo c, krailo md, krasin mj, huang l, mccarville mb, hicks j, pashankar f, pappo as. treatment of childhood nasopharyngeal carcinoma with induction chemotherapy and concurrent chemoradiotherapy: results of the children's oncology group arar0331 study. j clin oncol. 2019 dec 10;37(35):3369-3376. doi: 10.1200\\\/jco.19.01276. epub 2019 sep 25. erratum in: j clin oncol. 2021 may 10;39(14):1602. nasopharyngeal head mr pt ct nm rtimage clinical nctn\"},{\"id\":45441,\"type\":\"collection\",\"slug\":\"aren0532\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/aren0532\\\/\",\"title\":\"Vincristine, Dactinomycin, and Doxorubicin With or Without Radiation Therapy or Observation Only in Treating Younger Patients Who Are Undergoing Surgery for Newly Diagnosed Stage I, Stage II, or Stage III Wilms' Tumor\",\"short_title\":\"AREN0532\",\"doi\":\"10.7937\\\/6PJ1-M859\",\"summary\":\"\\u00a0This collection contains data from the Children\\u2019s Oncology Group (COG) Clinical Trial NCT00352534, \\u201cVincristine, Dactinomycin, and Doxorubicin With or Without Radiation Therapy or Observation Only in Treating Younger Patients Who Are Undergoing Surgery for Newly Diagnosed Stage I, Stage II, or Stage III Wilms' Tumor\\\". Principal Investigator: Conrad Fernandez, MD in Halifax, NS.\\u00a0 It was sponsored by NCI and performed by the Children's Oncology Group under study number AREN0532. This phase III trial is studying vincristine, dactinomycin, and doxorubicin with or without radiation therapy or observation only to see how well they work in treating patients undergoing surgery for newly diagnosed stage I, stage II, or stage III Wilms' tumor. Select patient-level clinical data from this trial is available via the following link: https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/689.Trial Description\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 COG AREN0532 is a treatment study of kidney tumors which have not spread to other parts of the body.\\u00a0544 very low and standard risk favorable histology Wilms Tumor patients entered the trial. At accrual all patients were Stage I-III and had not received any prior therapy. Dates of therapy were from 2006 to 2013.The National Wilms Tumor Study (NWTS) approach to treating stage III favorable-histology Wilms tumor (FHWT) is Regimen DD4A (vincristine, dactinomycin, and doxorubicin) and radiation therapy. Further risk strati\\ufb01cation is desired to improve outcomes and reduce late effects. In a 2018 JCO paper, the trial evaluated clinical and biologic variables for patients with stage III FHWT without combined loss of heterozygosity (LOH) at chromosomes 1p and 16q. This paper included 535 patients with stage III disease. Relapse after stage III treatment is associated with an overall survival (OS) of only 50% despite intensive salvage chemotherapy and\\\/or autologous bone marrow transplantation. It is thus highly desirable to identify patients who need augmentation of initial therapy with the hope of preventing relapse.A novel \\ufb01nding in this study was the remarkably strong predictive value of combining LOH and lymph node status. The relapse rate was exceptionally low among patients with tumors that were LOH - and lymph node \\u2013 negative. However, this trial demonstrated that those with combined lymph node involvement and LOH 1p or 16q had a significantly worse 4-year EFS outcome of 74%. There is a trend toward a poorer 4-year OS in this comparison; however, it is not statistically different.Approximately two thirds of patients had delayed-nephrectomy tissue submitted for central pathology review. Most patients with blastemal-type Wilms tumor but none of seven patients with low-risk\\\/ completely necrotic Wilms tumor experienced relapse, consistent with the \\ufb01ndings of the International Society of Pediatric Oncology (SIOP) that histologic response to preoperative chemotherapy plays an important role in predicting outcome.The results of this trial described the overall good outcome of patients with stage III FHWT using DD4A with radiation therapy and identified an association of combined lymph node and LOH status, as well as postchemotherapy, delayed nephrectomy histology, with EFS.Data from the 2018 J Clin Oncol. paper, cited below: A total of 535 patients with stage III disease were studied. Median follow-up was 5.2 years (range, 0.2 to 9.5). Four-year event-free survival (EFS) and overall survival estimates were 88% (95% CI,85% to 91%) and 97% (95% CI, 95% to 99%), respectively. A total of 58 of 66 relapses occurred in the \\ufb01rst 2 years, predominantly pulmonary (n = 36). Eighteen patients died, 14 secondary to disease.A better EFS was associated with negative lymph node status (P less than .01) and absence of LOH 1p or 16q (P less than .01), but not with gross residual disease or peritoneal implants. In contrast, the 4-year EFS was only 74% in patients with combined positive lymph node status and LOH 1p or 16q. A total of 123 patients (23%) had delayed nephrectomy. Submitted delayed nephrectomy histology showed anaplasia (n = 8; excluded from survival analysis); low risk\\\/completely necrotic (n = 7; zero relapses), intermediate risk (n = 63; six relapses), and high-risk\\\/blastemal type (n=7; \\ufb01ve relapses).Trial Outcomes Results of the trial have been reported in the following publications:Fernandez CV, Mullen EA, Chi YY, Ehrlich PF, Perlman EJ, Kalapurakal JA, Khanna G, Paulino AC, Hamilton TE, Gow KW, Tochner Z, Hoffer FA, Withycombe JS, Shamberger RC, Kim Y, Geller JI, Anderson JR, Grundy PE, Dome JS. Outcome and Prognostic Factors in Stage III Favorable-Histology Wilms Tumor: A Report From the Children's Oncology Group Study AREN0532. J Clin Oncol. 2018 Jan 20;36(3):254-261. doi: 10.1200\\\/JCO.2017.73.7999. Epub 2017 Dec 6. Erratum in: J Clin Oncol. 2019 Oct 10;37(29):2710. PMID: 29211618; PMCID: PMC5773840. https:\\\/\\\/doi.org\\\/10.1200\\\/jco.2017.73.7999Dix DB, Fernandez CV, Chi YY, Mullen EA, Geller JI, Gratias EJ, Khanna G, Kalapurakal JA, Perlman EJ, Seibel NL, Ehrlich PF, Malogolowkin M, Anderson J, Gastier-Foster J, Shamberger RC, Kim Y, Grundy PE, Dome JS; AREN0532 and AREN0533 study committees. Augmentation of Therapy for Combined Loss of Heterozygosity 1p and 16q in Favorable Histology Wilms Tumor: A Children's Oncology Group AREN0532 and AREN0533 Study Report. J Clin Oncol. 2019 Oct 20;37(30):2769-2777. doi: 10.1200\\\/JCO.18.01972. Epub 2019 Aug 26. PMID: 31449468; PMCID: PMC7001789. https:\\\/\\\/doi.org\\\/10.1200\\\/jco.18.01972\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/nctn-logo.png\",\"program\":\"NCTN\",\"cancer_types\":[\"Wilms Tumor\"],\"cancer_locations\":[\"Kidney\"],\"data_types\":[\"CT\",\"MR\",\"US\",\"RTIMAGE\",\"PT\",\"CR\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":544,\"date_updated\":\"2022-03-31\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=AREN0532\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/AREN0532-March-2022.tcia\",\"blob\":\"vincristine, dactinomycin, and doxorubicin with or without radiation therapy or observation only in treating younger patients who are undergoing surgery for newly diagnosed stage i, stage ii, or stage iii wilms' tumor aren0532 \\u00a0this collection contains data from the children\\u2019s oncology group (cog) clinical trial nct00352534, \\u201cvincristine, dactinomycin, and doxorubicin with or without radiation therapy or observation only in treating younger patients who are undergoing surgery for newly diagnosed stage i, stage ii, or stage iii wilms' tumor\\\". principal investigator: conrad fernandez, md in halifax, ns.\\u00a0 it was sponsored by nci and performed by the children's oncology group under study number aren0532. this phase iii trial is studying vincristine, dactinomycin, and doxorubicin with or without radiation therapy or observation only to see how well they work in treating patients undergoing surgery for newly diagnosed stage i, stage ii, or stage iii wilms' tumor. select patient-level clinical data from this trial is available via the following link: https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/689.trial description\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 cog aren0532 is a treatment study of kidney tumors which have not spread to other parts of the body.\\u00a0544 very low and standard risk favorable histology wilms tumor patients entered the trial. at accrual all patients were stage i-iii and had not received any prior therapy. dates of therapy were from 2006 to 2013.the national wilms tumor study (nwts) approach to treating stage iii favorable-histology wilms tumor (fhwt) is regimen dd4a (vincristine, dactinomycin, and doxorubicin) and radiation therapy. further risk strati\\ufb01cation is desired to improve outcomes and reduce late effects. in a 2018 jco paper, the trial evaluated clinical and biologic variables for patients with stage iii fhwt without combined loss of heterozygosity (loh) at chromosomes 1p and 16q. this paper included 535 patients with stage iii disease. relapse after stage iii treatment is associated with an overall survival (os) of only 50% despite intensive salvage chemotherapy and\\\/or autologous bone marrow transplantation. it is thus highly desirable to identify patients who need augmentation of initial therapy with the hope of preventing relapse.a novel \\ufb01nding in this study was the remarkably strong predictive value of combining loh and lymph node status. the relapse rate was exceptionally low among patients with tumors that were loh - and lymph node \\u2013 negative. however, this trial demonstrated that those with combined lymph node involvement and loh 1p or 16q had a significantly worse 4-year efs outcome of 74%. there is a trend toward a poorer 4-year os in this comparison; however, it is not statistically different.approximately two thirds of patients had delayed-nephrectomy tissue submitted for central pathology review. most patients with blastemal-type wilms tumor but none of seven patients with low-risk\\\/ completely necrotic wilms tumor experienced relapse, consistent with the \\ufb01ndings of the international society of pediatric oncology (siop) that histologic response to preoperative chemotherapy plays an important role in predicting outcome.the results of this trial described the overall good outcome of patients with stage iii fhwt using dd4a with radiation therapy and identified an association of combined lymph node and loh status, as well as postchemotherapy, delayed nephrectomy histology, with efs.data from the 2018 j clin oncol. paper, cited below: a total of 535 patients with stage iii disease were studied. median follow-up was 5.2 years (range, 0.2 to 9.5). four-year event-free survival (efs) and overall survival estimates were 88% (95% ci,85% to 91%) and 97% (95% ci, 95% to 99%), respectively. a total of 58 of 66 relapses occurred in the \\ufb01rst 2 years, predominantly pulmonary (n = 36). eighteen patients died, 14 secondary to disease.a better efs was associated with negative lymph node status (p less than .01) and absence of loh 1p or 16q (p less than .01), but not with gross residual disease or peritoneal implants. in contrast, the 4-year efs was only 74% in patients with combined positive lymph node status and loh 1p or 16q. a total of 123 patients (23%) had delayed nephrectomy. submitted delayed nephrectomy histology showed anaplasia (n = 8; excluded from survival analysis); low risk\\\/completely necrotic (n = 7; zero relapses), intermediate risk (n = 63; six relapses), and high-risk\\\/blastemal type (n=7; \\ufb01ve relapses).trial outcomes results of the trial have been reported in the following publications:fernandez cv, mullen ea, chi yy, ehrlich pf, perlman ej, kalapurakal ja, khanna g, paulino ac, hamilton te, gow kw, tochner z, hoffer fa, withycombe js, shamberger rc, kim y, geller ji, anderson jr, grundy pe, dome js. outcome and prognostic factors in stage iii favorable-histology wilms tumor: a report from the children's oncology group study aren0532. j clin oncol. 2018 jan 20;36(3):254-261. doi: 10.1200\\\/jco.2017.73.7999. epub 2017 dec 6. erratum in: j clin oncol. 2019 oct 10;37(29):2710. pmid: 29211618; pmcid: pmc5773840. https:\\\/\\\/doi.org\\\/10.1200\\\/jco.2017.73.7999dix db, fernandez cv, chi yy, mullen ea, geller ji, gratias ej, khanna g, kalapurakal ja, perlman ej, seibel nl, ehrlich pf, malogolowkin m, anderson j, gastier-foster j, shamberger rc, kim y, grundy pe, dome js; aren0532 and aren0533 study committees. augmentation of therapy for combined loss of heterozygosity 1p and 16q in favorable histology wilms tumor: a children's oncology group aren0532 and aren0533 study report. j clin oncol. 2019 oct 20;37(30):2769-2777. doi: 10.1200\\\/jco.18.01972. epub 2019 aug 26. pmid: 31449468; pmcid: pmc7001789. https:\\\/\\\/doi.org\\\/10.1200\\\/jco.18.01972 wilms tumor kidney ct mr us rtimage pt cr clinical nctn\"},{\"id\":41869,\"type\":\"collection\",\"slug\":\"aren0533\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/aren0533\\\/\",\"title\":\"Combination Chemotherapy With or Without Radiation Therapy in Treating Young Patients With Newly Diagnosed Stage III or Stage IV Wilms Tumor\",\"short_title\":\"AREN0533\",\"doi\":\"10.7937\\\/SJEZ-CJ78\",\"summary\":\"This collection contains data from the Children\\u2019s Oncology Group (COG) Clinical Trial NCT00379340, \\u201cCombination Chemotherapy With or Without Radiation Therapy in Treating Young Patients With Newly Diagnosed Stage III or Stage IV Wilms Tumor, \\\" Study Chair: David Dix, MD. It was sponsored by NCI and performed by the Children's Oncology Group under study number AREN0533. This phase III trial is studying how well combination chemotherapy with or without radiation therapy works in treating young patients with newly diagnosed stage III or stage IV favorable histology Wilms tumor. Select patient-level clinical data from this trial is available via the following link: https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/737.Trial DescriptionPatients with stage IV favorable histology Wilms tumor (FHWT), the majority of whom have pulmonary metastases, have inferior outcomes compared with those with localized disease. Their treatment is also complicated by a risk of late effects, including cardiac dysfunction, lung toxicity, musculoskeletal and soft tissue defects, and second malignancies. The AREN0533 study applied two separate strategies for risk stratification for patients with Stage III and IV favorable histology Wilms tumor. The first is the identification of patients with pulmonary nodules who can be spared bilateral pulmonary irradiation. Patients with Stage IV favorable histology Wilms tumor have a 4-year event-free survival (EFS) of 75% with chemotherapy and irradiation to sites of metastatic disease (most frequently in the lungs). European investigators are able to spare 75% of their patients with pulmonary nodules from irradiation based on the initial response to chemotherapy. The response of the lung metastases to 6 weeks of chemotherapy consisting of vincristine, dactinomycin, and doxorubicin (Regimen DD-4A) was used to determine if radiation of lung nodules is needed. Patients who had complete disappearance of their lung metastases (or who had tissue confirmation that the nodules do not contain viable tumor) at the Week 6 evaluation were considered rapid responders and continued with DD-4A. Patients who did not have complete resolution of pulmonary nodules by Chest CT underwent pulmonary irradiation and were switched to Regimen M (DD-4A variation with dactinomycin and doxorubicin given on the same day plus cyclophosphamide and etoposide). Central radiology review of the chest CTs were performed on all Stage IV patients with lung metastases at study enrollment and at Week 6. The second risk stratification variable was the allelic loss of 1p and 16q. Patients with Stage III and IV favorable histology Wilms tumor with loss of heterozygosity (LOH) of both 1p and 16q have a 4-yr EFS of 65%. Patients with LOH of 1p and 16q were assigned to Regimen M in an attempt to improve the 4-year EFS of this group of patients to 84%.Trial OutcomesResults of the trial have been reported in the following publications:Dix DB, Seibel NL, Chi YY, Khanna G, Gratias E, Anderson JR, Mullen EA, Geller JI, Kalapurakal JA, Paulino AC, Perlman EJ, Ehrlich PF, Malogolowkin M, Gastier-Foster JM, Wagner E, Grundy PE, Fernandez CV, Dome JS. Treatment of Stage IV Favorable Histology Wilms Tumor With Lung Metastases: A Report From the Children's Oncology Group AREN0533 Study. J Clin Oncol. 2018 Jun 1;36(16):1564-1570. doi: 10.1200\\\/JCO.2017.77.1931. Epub 2018 Apr 16. PMID: 29659330; PMCID: PMC6075846.Dix, D. B., Fernandez, C. V., Chi, Y.-Y., Mullen, E. A., Geller, J. I., Gratias, E. J., Khanna, G., Kalapurakal, J. A., Perlman, E. J., Seibel, N. L., Ehrlich, P. F., Malogolowkin, M., Anderson, J., Gastier-Foster, J., Shamberger, R. C., Kim, Y., Grundy, P. E., & Dome, J. S. (2019). Augmentation of Therapy for Combined Loss of Heterozygosity 1p and 16q in Favorable Histology Wilms Tumor: A Children\\u2019s Oncology Group AREN0532 and AREN0533 Study Report. In Journal of Clinical Oncology (Vol. 37, Issue 30, pp. 2769\\u20132777). American Society of Clinical Oncology (ASCO). https:\\\/\\\/doi.org\\\/10.1200\\\/jco.18.01972\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/nctn-logo.png\",\"program\":\"NCTN\",\"cancer_types\":[\"Wilms Tumor\"],\"cancer_locations\":[\"Kidney\"],\"data_types\":[\"CT\",\"US\",\"MR\",\"CR\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":294,\"date_updated\":\"2022-01-20\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=AREN0533\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/AREN0533-Nov-30-2021-manifest.tcia\",\"blob\":\"combination chemotherapy with or without radiation therapy in treating young patients with newly diagnosed stage iii or stage iv wilms tumor aren0533 this collection contains data from the children\\u2019s oncology group (cog) clinical trial nct00379340, \\u201ccombination chemotherapy with or without radiation therapy in treating young patients with newly diagnosed stage iii or stage iv wilms tumor, \\\" study chair: david dix, md. it was sponsored by nci and performed by the children's oncology group under study number aren0533. this phase iii trial is studying how well combination chemotherapy with or without radiation therapy works in treating young patients with newly diagnosed stage iii or stage iv favorable histology wilms tumor. select patient-level clinical data from this trial is available via the following link: https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/737.trial descriptionpatients with stage iv favorable histology wilms tumor (fhwt), the majority of whom have pulmonary metastases, have inferior outcomes compared with those with localized disease. their treatment is also complicated by a risk of late effects, including cardiac dysfunction, lung toxicity, musculoskeletal and soft tissue defects, and second malignancies. the aren0533 study applied two separate strategies for risk stratification for patients with stage iii and iv favorable histology wilms tumor. the first is the identification of patients with pulmonary nodules who can be spared bilateral pulmonary irradiation. patients with stage iv favorable histology wilms tumor have a 4-year event-free survival (efs) of 75% with chemotherapy and irradiation to sites of metastatic disease (most frequently in the lungs). european investigators are able to spare 75% of their patients with pulmonary nodules from irradiation based on the initial response to chemotherapy. the response of the lung metastases to 6 weeks of chemotherapy consisting of vincristine, dactinomycin, and doxorubicin (regimen dd-4a) was used to determine if radiation of lung nodules is needed. patients who had complete disappearance of their lung metastases (or who had tissue confirmation that the nodules do not contain viable tumor) at the week 6 evaluation were considered rapid responders and continued with dd-4a. patients who did not have complete resolution of pulmonary nodules by chest ct underwent pulmonary irradiation and were switched to regimen m (dd-4a variation with dactinomycin and doxorubicin given on the same day plus cyclophosphamide and etoposide). central radiology review of the chest cts were performed on all stage iv patients with lung metastases at study enrollment and at week 6. the second risk stratification variable was the allelic loss of 1p and 16q. patients with stage iii and iv favorable histology wilms tumor with loss of heterozygosity (loh) of both 1p and 16q have a 4-yr efs of 65%. patients with loh of 1p and 16q were assigned to regimen m in an attempt to improve the 4-year efs of this group of patients to 84%.trial outcomesresults of the trial have been reported in the following publications:dix db, seibel nl, chi yy, khanna g, gratias e, anderson jr, mullen ea, geller ji, kalapurakal ja, paulino ac, perlman ej, ehrlich pf, malogolowkin m, gastier-foster jm, wagner e, grundy pe, fernandez cv, dome js. treatment of stage iv favorable histology wilms tumor with lung metastases: a report from the children's oncology group aren0533 study. j clin oncol. 2018 jun 1;36(16):1564-1570. doi: 10.1200\\\/jco.2017.77.1931. epub 2018 apr 16. pmid: 29659330; pmcid: pmc6075846.dix, d. b., fernandez, c. v., chi, y.-y., mullen, e. a., geller, j. i., gratias, e. j., khanna, g., kalapurakal, j. a., perlman, e. j., seibel, n. l., ehrlich, p. f., malogolowkin, m., anderson, j., gastier-foster, j., shamberger, r. c., kim, y., grundy, p. e., & dome, j. s. (2019). augmentation of therapy for combined loss of heterozygosity 1p and 16q in favorable histology wilms tumor: a children\\u2019s oncology group aren0532 and aren0533 study report. in journal of clinical oncology (vol. 37, issue 30, pp. 2769\\u20132777). american society of clinical oncology (asco). https:\\\/\\\/doi.org\\\/10.1200\\\/jco.18.01972 wilms tumor kidney ct us mr cr clinical nctn\"},{\"id\":41861,\"type\":\"collection\",\"slug\":\"aren0534\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/aren0534\\\/\",\"title\":\"Combination Chemotherapy and Surgery in Treating Young Patients With Wilms Tumor\",\"short_title\":\"AREN0534\",\"doi\":\"10.7937\\\/TCIA.5M9S-6Y97\",\"summary\":\"This collection contains data from the Children\\u2019s Oncology Group (COG) Clinical Trial  NCT00945009, \\u201cCombination Chemotherapy and Surgery in Treating Young Patients With Wilms Tumor, \\\" Study Chair: Peter F. Ehrlich, M.D. M.S.C. It was sponsored by NCI and performed by the Children's Oncology Group under study number AREN0534. This phase III trial studies how well combination chemotherapy and surgery work in treating young patients with Wilms tumor. Select patient-level clinical data from this trial is available via the following link: https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/728.Trial DescriptionChildren with bilateral Wilms tumor account for 5-7% of all patients with Wilms tumor. Certain patients, with syndromes associated with Wilms tumor development, have been identified to be at increased risk for bilateral tumors. Due to an increased risk for renal failure, patients with bilateral disease at presentation are treated with preoperative chemotherapy in order to preserve renal parenchyma. Although this recommendation was made nearly 30 years ago, patients with bilateral tumors have not been formally studied in prior cooperative trials. Recent evidence suggests that survival of these patients is inferior to similar patients with unilateral tumor. This study is designed to improve the survival of these children, while continuing the emphasis on preserving renal function. Patients were enrolled and imaging studies were centrally reviewed to assess for bilateral renal lesions. They were treated with 3-drug induction chemotherapy (vincristine, dactinomycin, and doxorubicin) for 6 or 12 weeks based on radiographic response followed by surgery and further chemotherapy determined by histology. Radiation therapy was provided for postchemotherapy stage III and IV disease. One hundred eighty-nine of 208 patients were evaluable. Four-year EFS and OS were 82.1% (95% CI: 73.5%\\u201390.8%) and 94.9% (95% CI: 90.1%\\u201399.7%. Twenty-three patients relapsed and 7 had disease progression. After induction chemotherapy 163 of 189 (84.0%) underwent definitive surgical treatment in at least 1 kidney by 12 weeks and 39% retained parts of both kidneys. Surgical approaches included: unilateral total nephrectomy with contralateral partial nephrectomy (48%), bilateral partial nephrectomy (35%), unilateral total nephrectomy (10.5%), unilateral partial nephrectomy (4%), and bilateral total nephrectomies (2.5%). This treatment approach including standardized 3-drug preoperative chemotherapy, surgical resection within 12 weeks of diagnosis and response and histology-based postoperative therapy improved EFS and OS and preservation of renal parenchyma compared with historical outcomes for children with bilateral Wilms tumor.Before enrollment, real-time central review of diagnostic imaging, pathology (if obtained), and operative notes confirmed the status of BWT. After 6 weeks, cross-sectional imaging was performed and a tumor response was assigned for each kidney. After 4 chemotherapy cycles (12 weeks), repeat cross sectional imaging was performed and definitive surgery was required. Response was based on the Response Evaluation Criteria in Solid Tumor (RECIST 1.1) modified to include 3 lesions per kidney.Trial OutcomesResults of the trial have been reported in the following publication:Ehrlich P, Chi YY, Chintagumpala MM, Hoffer FA, Perlman EJ, Kalapurakal JA, Warwick A, Shamberger RC, Khanna G, Hamilton TE, Gow KW, Paulino AC, Gratias EJ, Mullen EA, Geller JI, Grundy PE, Fernandez CV, Ritchey ML, Dome JS. Results of the First Prospective Multi-institutional Treatment Study in Children With Bilateral Wilms Tumor (AREN0534): A Report From the Children's Oncology Group. Ann Surg. 2017 Sep;266(3):470-478. doi: 10.1097\\\/SLA.0000000000002356. Erratum in: Ann Surg. 2018 Mar;267(3):e64. PMID: 28795993; PMCID: PMC5629006.Ehrlich PF, Chi YY, Chintagumpala MM, Hoffer FA, Perlman EJ, Kalapurakal JA, Tornwall B, Warwick A, Shamberger RC, Khanna G, Hamilton TE, Gow KW, Paulino AC, Gratias EJ, Mullen EA, Geller JI, Grundy PE, Fernandez CV, Dome JS. Results of Treatment for Patients With Multicentric or Bilaterally Predisposed Unilateral Wilms Tumor (AREN0534): A report from the Children's Oncology Group. Cancer. 2020 Aug 1;126(15):3516-3525. doi: 10.1002\\\/cncr.32958. Epub 2020 May 27. PMID: 32459384; PMCID: PMC7769115.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/nctn-logo.png\",\"program\":\"NCTN\",\"cancer_types\":[\"Wilms Tumor\"],\"cancer_locations\":[\"Kidney\"],\"data_types\":[\"CT\",\"MR\",\"US\",\"OT\",\"PT\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":239,\"date_updated\":\"2021-11-09\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=AREN0534\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/AREN0534-Oct-26-2021-manifest.tcia\",\"blob\":\"combination chemotherapy and surgery in treating young patients with wilms tumor aren0534 this collection contains data from the children\\u2019s oncology group (cog) clinical trial  nct00945009, \\u201ccombination chemotherapy and surgery in treating young patients with wilms tumor, \\\" study chair: peter f. ehrlich, m.d. m.s.c. it was sponsored by nci and performed by the children's oncology group under study number aren0534. this phase iii trial studies how well combination chemotherapy and surgery work in treating young patients with wilms tumor. select patient-level clinical data from this trial is available via the following link: https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/728.trial descriptionchildren with bilateral wilms tumor account for 5-7% of all patients with wilms tumor. certain patients, with syndromes associated with wilms tumor development, have been identified to be at increased risk for bilateral tumors. due to an increased risk for renal failure, patients with bilateral disease at presentation are treated with preoperative chemotherapy in order to preserve renal parenchyma. although this recommendation was made nearly 30 years ago, patients with bilateral tumors have not been formally studied in prior cooperative trials. recent evidence suggests that survival of these patients is inferior to similar patients with unilateral tumor. this study is designed to improve the survival of these children, while continuing the emphasis on preserving renal function. patients were enrolled and imaging studies were centrally reviewed to assess for bilateral renal lesions. they were treated with 3-drug induction chemotherapy (vincristine, dactinomycin, and doxorubicin) for 6 or 12 weeks based on radiographic response followed by surgery and further chemotherapy determined by histology. radiation therapy was provided for postchemotherapy stage iii and iv disease. one hundred eighty-nine of 208 patients were evaluable. four-year efs and os were 82.1% (95% ci: 73.5%\\u201390.8%) and 94.9% (95% ci: 90.1%\\u201399.7%. twenty-three patients relapsed and 7 had disease progression. after induction chemotherapy 163 of 189 (84.0%) underwent definitive surgical treatment in at least 1 kidney by 12 weeks and 39% retained parts of both kidneys. surgical approaches included: unilateral total nephrectomy with contralateral partial nephrectomy (48%), bilateral partial nephrectomy (35%), unilateral total nephrectomy (10.5%), unilateral partial nephrectomy (4%), and bilateral total nephrectomies (2.5%). this treatment approach including standardized 3-drug preoperative chemotherapy, surgical resection within 12 weeks of diagnosis and response and histology-based postoperative therapy improved efs and os and preservation of renal parenchyma compared with historical outcomes for children with bilateral wilms tumor.before enrollment, real-time central review of diagnostic imaging, pathology (if obtained), and operative notes confirmed the status of bwt. after 6 weeks, cross-sectional imaging was performed and a tumor response was assigned for each kidney. after 4 chemotherapy cycles (12 weeks), repeat cross sectional imaging was performed and definitive surgery was required. response was based on the response evaluation criteria in solid tumor (recist 1.1) modified to include 3 lesions per kidney.trial outcomesresults of the trial have been reported in the following publication:ehrlich p, chi yy, chintagumpala mm, hoffer fa, perlman ej, kalapurakal ja, warwick a, shamberger rc, khanna g, hamilton te, gow kw, paulino ac, gratias ej, mullen ea, geller ji, grundy pe, fernandez cv, ritchey ml, dome js. results of the first prospective multi-institutional treatment study in children with bilateral wilms tumor (aren0534): a report from the children's oncology group. ann surg. 2017 sep;266(3):470-478. doi: 10.1097\\\/sla.0000000000002356. erratum in: ann surg. 2018 mar;267(3):e64. pmid: 28795993; pmcid: pmc5629006.ehrlich pf, chi yy, chintagumpala mm, hoffer fa, perlman ej, kalapurakal ja, tornwall b, warwick a, shamberger rc, khanna g, hamilton te, gow kw, paulino ac, gratias ej, mullen ea, geller ji, grundy pe, fernandez cv, dome js. results of treatment for patients with multicentric or bilaterally predisposed unilateral wilms tumor (aren0534): a report from the children's oncology group. cancer. 2020 aug 1;126(15):3516-3525. doi: 10.1002\\\/cncr.32958. epub 2020 may 27. pmid: 32459384; pmcid: pmc7769115. wilms tumor kidney ct mr us ot pt clinical nctn\"},{\"id\":45407,\"type\":\"collection\",\"slug\":\"b-mode-and-ceus-liver\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/b-mode-and-ceus-liver\\\/\",\"title\":\"Ultrasound data of a variety of liver masses\",\"short_title\":\"B-mode-and-CEUS-Liver\",\"doi\":\"10.7937\\\/TCIA.2021.v4z7-tc39\",\"summary\":\"Data were generated as part of two ongoing clinical trials investigating the use of contrast-enhanced ultrasound to a) characterize indeterminate liver lesions and b) monitor treatment response to loco regional therapy. Ultrasound data was obtained on a variety of state of the art ultrasound scanners with curvilinear probes. Gain, dynamic range, focus position and depth were optimized for image quality by the performing sonographer. Images of the mass in both sagittal and transverse planes were obtained and saved in DICOM format. Full cine-loops of the contrast enhanced ultrasound are also saved in DICOM format. The reference standard used for lesion characterization included tissue pathology and contrast-enhanced cross-sectional imaging within 1 month of the ultrasound exam. The initial treatment response to transarterial chemoembolization is also available for many hepatocellular carcinoma (HCC) cases and uses pathology, retreatment angiography, or longer-term tumor response on cross-sectional imaging as a reference standard.We expect these images can be used in a wide variety of image processing application. We are currently exploring a variety of automated intelligence algorithms for AI-based lesion characterization. Algorithms for object detection and segmentation may also be of interest. As the studies that have generated this data are also ongoing, it is expected that we can add volumetric data, contrast-enhanced ultrasound cine loops, and longer-term treatment response data to this data set in the future.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ceus.png\",\"program\":\"Community\",\"cancer_types\":[\"Liver Cancer\"],\"cancer_locations\":[\"Liver\"],\"data_types\":[\"US\",\"Diagnosis\",\"Follow-Up\",\"Treatment\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":120,\"date_updated\":\"2022-02-18\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=B-mode-and-CEUS-Liver\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/B-mode-and-CEUS-Liver_20220218.tcia\",\"blob\":\"ultrasound data of a variety of liver masses b-mode-and-ceus-liver data were generated as part of two ongoing clinical trials investigating the use of contrast-enhanced ultrasound to a) characterize indeterminate liver lesions and b) monitor treatment response to loco regional therapy. ultrasound data was obtained on a variety of state of the art ultrasound scanners with curvilinear probes. gain, dynamic range, focus position and depth were optimized for image quality by the performing sonographer. images of the mass in both sagittal and transverse planes were obtained and saved in dicom format. full cine-loops of the contrast enhanced ultrasound are also saved in dicom format. the reference standard used for lesion characterization included tissue pathology and contrast-enhanced cross-sectional imaging within 1 month of the ultrasound exam. the initial treatment response to transarterial chemoembolization is also available for many hepatocellular carcinoma (hcc) cases and uses pathology, retreatment angiography, or longer-term tumor response on cross-sectional imaging as a reference standard.we expect these images can be used in a wide variety of image processing application. we are currently exploring a variety of automated intelligence algorithms for ai-based lesion characterization. algorithms for object detection and segmentation may also be of interest. as the studies that have generated this data are also ongoing, it is expected that we can add volumetric data, contrast-enhanced ultrasound cine loops, and longer-term treatment response data to this data set in the future. liver cancer liver us diagnosis follow-up treatment clinical community\"},{\"id\":49867,\"type\":\"collection\",\"slug\":\"bcbm-radiogenomics\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/bcbm-radiogenomics\\\/\",\"title\":\"MRI Dataset of Metastatic Breast Cancer to the Brain with Expert-reviewed Segmentations and Tumor-derived Radiomic Features\\u00a0\",\"short_title\":\"BCBM-RadioGenomics\",\"doi\":\"10.7937\\\/rrse-w278\",\"summary\":\"This dataset consists of 268 T1-post contrast Magnetic Resonance Images (MRIs) in NIfTI format from 165 patients with histologically confirmed metastatic breast cancer to the brain. Some patients underwent repeat Gamma Knife treatments (usually for recurrence), resulting in 268 total studies. Each MRI has a unique identifier to the patient and their treatment session. For example, if a patient had their first treatment this was labeled PatientID-0, and their second treatment (maybe some months later for a new metastasis) labeled as PatientID-1.The data were obtained on 1.5T and 3T scanners for stereotactic radiosurgery (SRS) treatment for metastatic breast cancer to the brain.\\u00a0 Each scan has isotropic spacing varying from 0.5mm to 1.0mm (although a fixed value in each scan). A brain extraction tool was applied to remove the skull and face (HD-BET). We also performed intensity inhomogeneity correction using N4 bias correction.\\u00a0The dataset includes segmented masks for tumor, postoperative tumor bed\\\/resection cavity, and neighboring structures of interest as well as a comprehensive catalog of 107 tumor-derived radiomic features (for each segmentation) generated using Pyradiomics. The segmentations were created and reviewed by at least one neurosurgeon and one radiation oncologist during their initial treatment.\\u00a0 We also provide clinical demographics (patient age and gender) and genetic status for progesterone receptor (PR), estrogen receptor (ER), and Human Epidermal Growth Factor Receptor 2 (HER2).\\u00a0This collection provides a platform for diverse applications ranging from the development of machine learning models in computer vision and radiogenomics to educational uses for neurosurgery, radiation oncology, and neuroradiology trainees.\\u00a0 The comprehensiveness of this data makes it a prime resource for investigations on any individual component (raw images, genetics, radiomics, etc.)\\u2013whether serving as a validation dataset or as an exploratory one. Experts in computer vision may use the expert-labeled segmentations here to augment or train a tumor segmentation algorithm de novo. For more radiogenomic investigations, researchers may decide they do not agree with the radiomic features provided and want to try a different set of radiomic features (i.e. a wavelet transformed or scatter transformed) and re-train for a classification goal (genetic status prediction, outcome prediction, etc.). Our data provides the raw segmentation information in which researchers can derive their own radiomic features using their algorithms of choice.\\u00a0Note: There are minor inconsistencies in the segmentations\\\/label names. The segmentations\\\/label names were written years ago by several technicians who used varying abbreviations, misspellings, and acronyms.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/BCBM-RadioGenomics-scaled.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Brain Cancer\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"Segmentation\",\"Radiomic Feature\",\"Demographic\",\"Molecular Test\",\"Other\"],\"supporting_data\":[\"radiomic features\",\"Clinical\"],\"species\":[\"Human\"],\"subjects\":165,\"date_updated\":\"2025-01-28\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijk4NSIsInBhc3Njb2RlIjoiMDg1ODg3MDhmOGEyZTlmMTBiNTIyNmQ1Yjg4OTIzMWQxZjVjMTEzMiIsInBhY2thZ2VfaWQiOiI5ODUiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"mri dataset of metastatic breast cancer to the brain with expert-reviewed segmentations and tumor-derived radiomic features\\u00a0 bcbm-radiogenomics this dataset consists of 268 t1-post contrast magnetic resonance images (mris) in nifti format from 165 patients with histologically confirmed metastatic breast cancer to the brain. some patients underwent repeat gamma knife treatments (usually for recurrence), resulting in 268 total studies. each mri has a unique identifier to the patient and their treatment session. for example, if a patient had their first treatment this was labeled patientid-0, and their second treatment (maybe some months later for a new metastasis) labeled as patientid-1.the data were obtained on 1.5t and 3t scanners for stereotactic radiosurgery (srs) treatment for metastatic breast cancer to the brain.\\u00a0 each scan has isotropic spacing varying from 0.5mm to 1.0mm (although a fixed value in each scan). a brain extraction tool was applied to remove the skull and face (hd-bet). we also performed intensity inhomogeneity correction using n4 bias correction.\\u00a0the dataset includes segmented masks for tumor, postoperative tumor bed\\\/resection cavity, and neighboring structures of interest as well as a comprehensive catalog of 107 tumor-derived radiomic features (for each segmentation) generated using pyradiomics. the segmentations were created and reviewed by at least one neurosurgeon and one radiation oncologist during their initial treatment.\\u00a0 we also provide clinical demographics (patient age and gender) and genetic status for progesterone receptor (pr), estrogen receptor (er), and human epidermal growth factor receptor 2 (her2).\\u00a0this collection provides a platform for diverse applications ranging from the development of machine learning models in computer vision and radiogenomics to educational uses for neurosurgery, radiation oncology, and neuroradiology trainees.\\u00a0 the comprehensiveness of this data makes it a prime resource for investigations on any individual component (raw images, genetics, radiomics, etc.)\\u2013whether serving as a validation dataset or as an exploratory one. experts in computer vision may use the expert-labeled segmentations here to augment or train a tumor segmentation algorithm de novo. for more radiogenomic investigations, researchers may decide they do not agree with the radiomic features provided and want to try a different set of radiomic features (i.e. a wavelet transformed or scatter transformed) and re-train for a classification goal (genetic status prediction, outcome prediction, etc.). our data provides the raw segmentation information in which researchers can derive their own radiomic features using their algorithms of choice.\\u00a0note: there are minor inconsistencies in the segmentations\\\/label names. the segmentations\\\/label names were written years ago by several technicians who used varying abbreviations, misspellings, and acronyms. brain cancer brain mr segmentation radiomic feature demographic molecular test other radiomic features clinical community\"},{\"id\":41413,\"type\":\"collection\",\"slug\":\"bone-marrow-cytomorphology_mll_helmholtz_fraunhofer\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/bone-marrow-cytomorphology_mll_helmholtz_fraunhofer\\\/\",\"title\":\"An Expert-Annotated Dataset of Bone Marrow Cytology in Hematologic Malignancies\",\"short_title\":\"Bone-Marrow-Cytomorphology_MLL_Helmholtz_Fraunhofer\",\"doi\":\"10.7937\\\/TCIA.AXH3-T579\",\"summary\":\"The dataset contains a collection of over 170,000 de-identified, expert-annotated cells from the bone marrow smears of 945 patients stained using the May-Gr\\u00fcnwald-Giemsa\\\/Pappenheim stain. The diagnosis distribution in the cohort included a variety of hematological diseases reflective of the sample entry of a large laboratory specialized in leukemia diagnostics. Image acquisition was performed using a brightfield microscope with 40x magnification and oil immersion.Large datasets with a high quality of both data acquisition and annotation are key prerequisites to develop data-driven, computational methods in diagnostic medicine. In the case of bone marrow morphology, a key diagnostic method for a broad range of hematologic diseases, only few datasets are publicly available so far, which are orders of magnitude smaller than the one presented here. Inclusion of our dataset into TCIA provides both medical researchers and bioinformaticians with a public resource for education and algorithm improvement.All samples were processed in the Munich Leukemia Laboratory (MLL), scanned using equipment developed at Fraunhofer IIS and post-processed using software developed at\\u00a0Helmholtz Munich.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Leukemia & Lymphoma Cancers\"],\"cancer_locations\":[\"Marrow\"],\"data_types\":[\"Histopathology\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":945,\"date_updated\":\"2021-11-12\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22Bone-Marrow-Cytomorphology%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjYwMSIsInBhc3Njb2RlIjoiM2ZkYjI2ZTJiNmE4MjVjMzUyYjE4MDQ4ZGU1ZGI4ZDk0YTA3NmU4MSIsInBhY2thZ2VfaWQiOiI2MDEiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"an expert-annotated dataset of bone marrow cytology in hematologic malignancies bone-marrow-cytomorphology_mll_helmholtz_fraunhofer the dataset contains a collection of over 170,000 de-identified, expert-annotated cells from the bone marrow smears of 945 patients stained using the may-gr\\u00fcnwald-giemsa\\\/pappenheim stain. the diagnosis distribution in the cohort included a variety of hematological diseases reflective of the sample entry of a large laboratory specialized in leukemia diagnostics. image acquisition was performed using a brightfield microscope with 40x magnification and oil immersion.large datasets with a high quality of both data acquisition and annotation are key prerequisites to develop data-driven, computational methods in diagnostic medicine. in the case of bone marrow morphology, a key diagnostic method for a broad range of hematologic diseases, only few datasets are publicly available so far, which are orders of magnitude smaller than the one presented here. inclusion of our dataset into tcia provides both medical researchers and bioinformaticians with a public resource for education and algorithm improvement.all samples were processed in the munich leukemia laboratory (mll), scanned using equipment developed at fraunhofer iis and post-processed using software developed at\\u00a0helmholtz munich. leukemia & lymphoma cancers marrow histopathology community\"},{\"id\":41535,\"type\":\"collection\",\"slug\":\"brain-tr-gammaknife\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/brain-tr-gammaknife\\\/\",\"title\":\"Brain Tumor Recurrence Prediction after Gamma Knife Radiotherapy from MRI and Related DICOM-RT: An Open Annotated Dataset and Baseline Algorithm\",\"short_title\":\"Brain-TR-GammaKnife\",\"doi\":\"10.7937\\\/xb6d-py67\",\"summary\":\"Here we release a brain cancer MRI dataset with the companion Gamma Knife treatment planning and follow-up data for the purpose of tumor recurrence prediction. The dataset consists of 47 subjects. A total of 244 lesions were collected with annotations. The dataset contains original patient MRI images (in DICOM format), radiation therapy structure data (in DICOM and NRRD format), code, and clinical information. First, dose MRI images were resampled to original MRI spacing via a linear transformation. Second, each region in each patient's MRI was extracted and cropped out; note that one patient may have multiple lesions and\\\/or multiple imaging sessions. Third, the corresponding radiation dose information was cropped out to the resampled aligned lesion mask. In this way, each lesion MRI is paired with its radiation dose MRI. The release of this dataset is expected to contribute to the development of automated brain tumor recurrence prediction algorithms.IntroductionPrediction and identification of tumor recurrence are critical for brain cancer treatment design and planning. Stereotactic radiation therapy delivered with Gamma Knife has been developed as one of the common treatment approaches in addition to others by delivering radiation that accurately to the tumor while minimizing toxicity to nearby healthy tissues. Artificial intelligence (AI)-based research has shown great potential in brain tumor MRI analysis recently with its effective data-driven feature extraction and recognition capabilities. Here we release a brain cancer MRI dataset with the companion Gamma Knife treatment planning and follow-up data for the purpose of tumor recurrence prediction. The dataset contains original patient MRI images, radiation therapy data, and additional clinical information. The included MRI series are T1 MPRAGE with Gadolinium contrast acquired on a 1.5 T Siemens Magnetom scanner for Gamma Knife treatment for brain metastasis. This public dataset retrospectively involved a total of 47 brain cancer patients, organized based on patient-level, course-level, and lesion-level, respectively. Patient-level keeps all the data related to a specific patient altogether. Each course-level treatment data has been stored for each patient. Among all the patients, 17 patients received more than 1 course of treatment, 7 received more than 2 courses, and 1 patient received up to 8 courses in total. Regarding to the lesion-level, a total of 244 lesions are collected with annotations. 221 lesions are stable, and 23 are recurrence, which makes the dataset imbalance from the lesion-level perspective. Lesion annotations are provided, and inclusive preprocessing steps have been specified to simplify the usage of this dataset. The release of this dataset is expected to contribute to future development of automated brain tumor recurrence prediction algorithms and promote the clinical implementations associated with AI-based computer-aided diagnosis field.Data Records and PopulationThis dataset was collected retrospectively under IRB-approval (2017-0266) from a clinical database of patients treated for brain metastases with Gamma Knife radiation therapy at the University of Mississippi Medical Center (UMMC). The dataset consisted of 47 brain cancer with 21 males and 26 females who developed brain metastases for various primary tumors and then underwent Gamma Knife stereotactic radiation therapy in an effort to control the identified brain lesions. A total of 244 lesions are collected with annotations. Additionally, these patients participated in treatment follow-up during which the response to treatment could be adequately assessed to identify recurring lesions and those that responded to treatment by stopping growth or even shrinking. A keyed spreadsheet stored various other relevant clinical information as well as how many treatment sessions were used to deliver the dose for each target. It should be noted that each patient may have undergone more than one treatment course as they developed new lesions and\\\/or treated lesions recurred. For each patient, the treatment targets were identified and delineated through collaboration between radiation oncologists and neurosurgeons with validation from neuroradiologists.Image AcquisitionIn preparation for the dataset, an MRI was acquired on a 1.5 T Siemens Magnetom scanner. The included MRI series are T1 MPRAGE with Gadolinium contrast and a voxel size of 1 \\u00d7 1 \\u00d7 1 mm which acts as the primary planning dataset for Gamma Knife treatment for brain metastases. For each patient, the treatment planning DICOM data was collected in the MRI dataset with its accompanying RTStruct, which indicates the lesions that were targeted using a given MRI. The RTDose information provides how much and where dose was deposited for each respective lesion\\\/target. The treatment sessions were recorded to describe the dose delivery for a target represented in the RTDose as it may have been delivered in as few as 1, but maybe as many as 5 treatment sessions. Additionally, each patient may have undergone more than one treatment course as they developed new lesions and\\\/or treated lesions recurred. The dose units are in Gray (Gy, AKA J\\\/kg) which is typical in radiation therapy. The dose was calculated using the TMR10 algorithm in the GammaPlan\\u00ae (version 11) software which does not consider tissue heterogeneity in the calculation but is consistent with previous Gamma Knife treatment practices across many institutions. Treatment was ultimately delivered on the Gamma Knife Icon\\u00ae at the same institution. After collection, all DICOM data (MRI images, RTStruct, and RTDose) was fully anonymized removing all protected health information and treatment-related dates. The anonymization process included mapping them to identifiers that match the clinical information in the database spreadsheet.Data Preprocessing and UsageIn this dataset, both original and preprocessed data are available for both clinical and engineering researchers. For preprocessed images, we specify a series of preprocessing steps that have been conducted. First, dose MRI images were resampled to the same spacing as the original patient MRI via a linear transformation process because the raw MRI and dose files share not only different dimensions but incompatible actual spacing. Second, each lesion region in each patient's MRI was extracted and cropped out based on the patient's lesion mask. It is noted that one patient may have multiple lesions. Third, the corresponding radiation dose information was also cropped out using the same lesion mask because the masks are shared through the same coordinate after resampling. In this way, each lesion MRI is paired with its radiation dose MRI. For any AI model training or test, the voxel values are recommended to be normalized to the\\u00a0range of 0 to 1 in each cropped lesion and dose MRI pair. Resizing is another necessary preprocessing step to keep the cropped training and test samples in the same dimension. In our dataset, we kept the original dimension of the cropped lesions. Since the dataset is imbalanced in terms of lesion-level, data augmentation techniques are suggested to be employed before model training.The aim of this dataset is to facilitate the development and evolution of automated tumor recurrence identification. Further potential applications based on this dataset include radiation treatment planning, evaluating and refining, and automated dose delivery planning. AI research community may take advantage of this public data resource to achieve promising results and promote the implementation of artificial intelligence-assist tumor recurrence prediction. Different preprocessing procedures in terms of various focuses and implementation can be used, and more advanced AI frameworks and techniques are expected to be developed.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Figure_Brain-TR-GammaKnife.png\",\"program\":\"Community\",\"cancer_types\":[\"Brain Cancer\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"RTSTRUCT\",\"RTDOSE\",\"Segmentation\",\"Demographic\",\"Diagnosis\",\"Other\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":47,\"date_updated\":\"2023-03-21\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Brain-TR-GammaKnife\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Brain-TR-GammaKnife_v1_March2023.tcia\",\"blob\":\"brain tumor recurrence prediction after gamma knife radiotherapy from mri and related dicom-rt: an open annotated dataset and baseline algorithm brain-tr-gammaknife here we release a brain cancer mri dataset with the companion gamma knife treatment planning and follow-up data for the purpose of tumor recurrence prediction. the dataset consists of 47 subjects. a total of 244 lesions were collected with annotations. the dataset contains original patient mri images (in dicom format), radiation therapy structure data (in dicom and nrrd format), code, and clinical information. first, dose mri images were resampled to original mri spacing via a linear transformation. second, each region in each patient's mri was extracted and cropped out; note that one patient may have multiple lesions and\\\/or multiple imaging sessions. third, the corresponding radiation dose information was cropped out to the resampled aligned lesion mask. in this way, each lesion mri is paired with its radiation dose mri. the release of this dataset is expected to contribute to the development of automated brain tumor recurrence prediction algorithms.introductionprediction and identification of tumor recurrence are critical for brain cancer treatment design and planning. stereotactic radiation therapy delivered with gamma knife has been developed as one of the common treatment approaches in addition to others by delivering radiation that accurately to the tumor while minimizing toxicity to nearby healthy tissues. artificial intelligence (ai)-based research has shown great potential in brain tumor mri analysis recently with its effective data-driven feature extraction and recognition capabilities. here we release a brain cancer mri dataset with the companion gamma knife treatment planning and follow-up data for the purpose of tumor recurrence prediction. the dataset contains original patient mri images, radiation therapy data, and additional clinical information. the included mri series are t1 mprage with gadolinium contrast acquired on a 1.5 t siemens magnetom scanner for gamma knife treatment for brain metastasis. this public dataset retrospectively involved a total of 47 brain cancer patients, organized based on patient-level, course-level, and lesion-level, respectively. patient-level keeps all the data related to a specific patient altogether. each course-level treatment data has been stored for each patient. among all the patients, 17 patients received more than 1 course of treatment, 7 received more than 2 courses, and 1 patient received up to 8 courses in total. regarding to the lesion-level, a total of 244 lesions are collected with annotations. 221 lesions are stable, and 23 are recurrence, which makes the dataset imbalance from the lesion-level perspective. lesion annotations are provided, and inclusive preprocessing steps have been specified to simplify the usage of this dataset. the release of this dataset is expected to contribute to future development of automated brain tumor recurrence prediction algorithms and promote the clinical implementations associated with ai-based computer-aided diagnosis field.data records and populationthis dataset was collected retrospectively under irb-approval (2017-0266) from a clinical database of patients treated for brain metastases with gamma knife radiation therapy at the university of mississippi medical center (ummc). the dataset consisted of 47 brain cancer with 21 males and 26 females who developed brain metastases for various primary tumors and then underwent gamma knife stereotactic radiation therapy in an effort to control the identified brain lesions. a total of 244 lesions are collected with annotations. additionally, these patients participated in treatment follow-up during which the response to treatment could be adequately assessed to identify recurring lesions and those that responded to treatment by stopping growth or even shrinking. a keyed spreadsheet stored various other relevant clinical information as well as how many treatment sessions were used to deliver the dose for each target. it should be noted that each patient may have undergone more than one treatment course as they developed new lesions and\\\/or treated lesions recurred. for each patient, the treatment targets were identified and delineated through collaboration between radiation oncologists and neurosurgeons with validation from neuroradiologists.image acquisitionin preparation for the dataset, an mri was acquired on a 1.5 t siemens magnetom scanner. the included mri series are t1 mprage with gadolinium contrast and a voxel size of 1 \\u00d7 1 \\u00d7 1 mm which acts as the primary planning dataset for gamma knife treatment for brain metastases. for each patient, the treatment planning dicom data was collected in the mri dataset with its accompanying rtstruct, which indicates the lesions that were targeted using a given mri. the rtdose information provides how much and where dose was deposited for each respective lesion\\\/target. the treatment sessions were recorded to describe the dose delivery for a target represented in the rtdose as it may have been delivered in as few as 1, but maybe as many as 5 treatment sessions. additionally, each patient may have undergone more than one treatment course as they developed new lesions and\\\/or treated lesions recurred. the dose units are in gray (gy, aka j\\\/kg) which is typical in radiation therapy. the dose was calculated using the tmr10 algorithm in the gammaplan\\u00ae (version 11) software which does not consider tissue heterogeneity in the calculation but is consistent with previous gamma knife treatment practices across many institutions. treatment was ultimately delivered on the gamma knife icon\\u00ae at the same institution. after collection, all dicom data (mri images, rtstruct, and rtdose) was fully anonymized removing all protected health information and treatment-related dates. the anonymization process included mapping them to identifiers that match the clinical information in the database spreadsheet.data preprocessing and usagein this dataset, both original and preprocessed data are available for both clinical and engineering researchers. for preprocessed images, we specify a series of preprocessing steps that have been conducted. first, dose mri images were resampled to the same spacing as the original patient mri via a linear transformation process because the raw mri and dose files share not only different dimensions but incompatible actual spacing. second, each lesion region in each patient's mri was extracted and cropped out based on the patient's lesion mask. it is noted that one patient may have multiple lesions. third, the corresponding radiation dose information was also cropped out using the same lesion mask because the masks are shared through the same coordinate after resampling. in this way, each lesion mri is paired with its radiation dose mri. for any ai model training or test, the voxel values are recommended to be normalized to the\\u00a0range of 0 to 1 in each cropped lesion and dose mri pair. resizing is another necessary preprocessing step to keep the cropped training and test samples in the same dimension. in our dataset, we kept the original dimension of the cropped lesions. since the dataset is imbalanced in terms of lesion-level, data augmentation techniques are suggested to be employed before model training.the aim of this dataset is to facilitate the development and evolution of automated tumor recurrence identification. further potential applications based on this dataset include radiation treatment planning, evaluating and refining, and automated dose delivery planning. ai research community may take advantage of this public data resource to achieve promising results and promote the implementation of artificial intelligence-assist tumor recurrence prediction. different preprocessing procedures in terms of various focuses and implementation can be used, and more advanced ai frameworks and techniques are expected to be developed. brain cancer brain mr rtstruct rtdose segmentation demographic diagnosis other clinical image analyses software\\\/source code community\"},{\"id\":41519,\"type\":\"collection\",\"slug\":\"brain-tumor-progression\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/brain-tumor-progression\\\/\",\"title\":\"Brain-Tumor-Progression\",\"short_title\":\"Brain-Tumor-Progression\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2018.15quzvnb\",\"summary\":\"This collection includes datasets from 20 subjects with primary newly diagnosed glioblastoma who were treated with surgery and standard concomitant chemo-radiation therapy (CRT) followed by adjuvant chemotherapy.\\u00a0 Two MRI exams are included for each patient: within 90 days following CRT completion and at progression (determined clinically, and based on a combination of clinical performance and\\\/or imaging findings, and punctuated by a change in treatment or intervention).\\u00a0All image sets are in DICOM format and contain T1w (pre and post-contrast agent), FLAIR, T2w, ADC, normalized cerebral blood flow, normalized relative cerebral blood volume, standardized relative cerebral blood volume, and binary tumor masks (generated using T1w images).\\u00a0 The perfusion images were generated from dynamic susceptibility contrast (GRE-EPI DSC) imaging following a preload of contrast agent.\\u00a0 All of the series are co-registered with the T1+C images.\\u00a0 The intent of this dataset is for assessing deep learning algorithm performance to predict tumor progression.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Brain Cancer\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":20,\"date_updated\":\"2018-01-31\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=Brain-Tumor-Progression\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Brain-Tumor-Progression_2018-01-31.tcia\",\"blob\":\"brain-tumor-progression brain-tumor-progression this collection includes datasets from 20 subjects with primary newly diagnosed glioblastoma who were treated with surgery and standard concomitant chemo-radiation therapy (crt) followed by adjuvant chemotherapy.\\u00a0 two mri exams are included for each patient: within 90 days following crt completion and at progression (determined clinically, and based on a combination of clinical performance and\\\/or imaging findings, and punctuated by a change in treatment or intervention).\\u00a0all image sets are in dicom format and contain t1w (pre and post-contrast agent), flair, t2w, adc, normalized cerebral blood flow, normalized relative cerebral blood volume, standardized relative cerebral blood volume, and binary tumor masks (generated using t1w images).\\u00a0 the perfusion images were generated from dynamic susceptibility contrast (gre-epi dsc) imaging following a preload of contrast agent.\\u00a0 all of the series are co-registered with the t1+c images.\\u00a0 the intent of this dataset is for assessing deep learning algorithm performance to predict tumor progression. brain cancer brain mr image analyses community\"},{\"id\":48983,\"type\":\"collection\",\"slug\":\"brats-africa\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/brats-africa\\\/\",\"title\":\"Expanding the Brain Tumor Segmentation (BraTS) data to include African Populations\",\"short_title\":\"BraTS-Africa\",\"doi\":\"10.7937\\\/v8h6-8x67\",\"summary\":\"The dataset is a collection of retrospective pre-operative brain magnetic resonance imaging (MRI) scans, clinically acquired from six diagnostic centers in Nigeria. The scans are from 146 patients who have brain MRIs indicating central nervous system neoplasms, diffuse glioma, low-grade glioma, or glioblastoma\\\/high-grade glioma. The brain scans were multiparametric MR images (mpMRI), specifically T1, T1 CE, T2, and T2 FLAIR,\\u00a0 acquired on 1.5T MRI between January 2010 and December 2022.\\u00a0Scans were obtained from different scanners using each center\\u2019s acquisition protocol. Each scan was de-identified and de-faced to remove personal identifiers and presented in their original state with respect to resolution and orientation. To ensure uniformity across scans and modalities, a standardized pre-processing protocol was applied to adjust the image dimensions and voxel sizes. The scans were extracted from the PACs as DICOM files and converted to the Neuroimaging Informatics Technology Initiative (NlfTI) file format to facilitate computational analysis, following the well-accepted pre-processing protocol of the International Brain Tumour Segmentation (BraTS) challenge. All scans were subjected to sanity checks to confirm the presence of all required sequences. Specifically, all mpMRI volumes were reoriented to the left posterior-superior (LPS) coordinate system, and the T1 CE scan of each patient was rigidly (6 degrees of freedom) registered and resampled to an isotropic resolution of 1 mm3 based on a common anatomical atlas, namely SRI. The remaining scans (i.e., T1, T2, FLAIR) of each patient were then rigidly co-registered to this resampled T1 CE scan by first obtaining the rigid transformation matrix to T1 CE, then combining with the transformation matrix from T1 CE to the SRI atlas, and resampling. The N4 bias field correction was applied in all scans to correct for intensity non-uniformities caused by the inhomogeneity of the scanner's magnetic field during image acquisition to facilitate an improved registration of all scans to the common anatomical atlas. Brain extraction was also performed using a standard process for\\u00a0 skull-stripping to remove all non-brain tissue (including neck, fat, eyeballs, and skull) from the image and create a brain mask to\\u00a0 enable further computational analyses.\\u00a0Inclusion Criteria\\u00a0All Brain MRI Scans of patients with clinical features of brain tumors from the study site acquired between January 2010 and December 2022, including, central nervous system (CNS) neoplasms, specifically diffuse glioma, or low-grade glioma (LGG) or glioblastoma\\\/high-grade glioma (GBM\\\/HGG).Exclusion Criteria\\u00a0Any brain image or scan that is not an MRI or acquired before January 2010 or after December 2022.Image AnnotationThe expert-annotated tumor sub-regions for each of the 146 cases are provided along with a metadata (csv file) of study location, scanner type, where available.Benefit to ResearchersThe contribution of BraTS-Africa dataset is two-fold: 1) its potential for use in research leading towards generalizable and inclusive diagnostic tools applicable across all settings including resource constrained environments, and 2) its ability to describe the peculiarities of neuroimaging in African settings.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/BRATS_banner_noCaption.png\",\"program\":\"Community\",\"cancer_types\":[\"Brain Cancer\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"Segmentation\",\"Diagnosis\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":146,\"date_updated\":\"2024-09-04\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijk0OCIsInBhc3Njb2RlIjoiOTg2MzVlMGRmNzc3NWQ0NWJmZTQ2NjlhYzQwNjNmYjcxMjU0MzI1NyIsInBhY2thZ2VfaWQiOiI5NDgiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"expanding the brain tumor segmentation (brats) data to include african populations brats-africa the dataset is a collection of retrospective pre-operative brain magnetic resonance imaging (mri) scans, clinically acquired from six diagnostic centers in nigeria. the scans are from 146 patients who have brain mris indicating central nervous system neoplasms, diffuse glioma, low-grade glioma, or glioblastoma\\\/high-grade glioma. the brain scans were multiparametric mr images (mpmri), specifically t1, t1 ce, t2, and t2 flair,\\u00a0 acquired on 1.5t mri between january 2010 and december 2022.\\u00a0scans were obtained from different scanners using each center\\u2019s acquisition protocol. each scan was de-identified and de-faced to remove personal identifiers and presented in their original state with respect to resolution and orientation. to ensure uniformity across scans and modalities, a standardized pre-processing protocol was applied to adjust the image dimensions and voxel sizes. the scans were extracted from the pacs as dicom files and converted to the neuroimaging informatics technology initiative (nlfti) file format to facilitate computational analysis, following the well-accepted pre-processing protocol of the international brain tumour segmentation (brats) challenge. all scans were subjected to sanity checks to confirm the presence of all required sequences. specifically, all mpmri volumes were reoriented to the left posterior-superior (lps) coordinate system, and the t1 ce scan of each patient was rigidly (6 degrees of freedom) registered and resampled to an isotropic resolution of 1 mm3 based on a common anatomical atlas, namely sri. the remaining scans (i.e., t1, t2, flair) of each patient were then rigidly co-registered to this resampled t1 ce scan by first obtaining the rigid transformation matrix to t1 ce, then combining with the transformation matrix from t1 ce to the sri atlas, and resampling. the n4 bias field correction was applied in all scans to correct for intensity non-uniformities caused by the inhomogeneity of the scanner's magnetic field during image acquisition to facilitate an improved registration of all scans to the common anatomical atlas. brain extraction was also performed using a standard process for\\u00a0 skull-stripping to remove all non-brain tissue (including neck, fat, eyeballs, and skull) from the image and create a brain mask to\\u00a0 enable further computational analyses.\\u00a0inclusion criteria\\u00a0all brain mri scans of patients with clinical features of brain tumors from the study site acquired between january 2010 and december 2022, including, central nervous system (cns) neoplasms, specifically diffuse glioma, or low-grade glioma (lgg) or glioblastoma\\\/high-grade glioma (gbm\\\/hgg).exclusion criteria\\u00a0any brain image or scan that is not an mri or acquired before january 2010 or after december 2022.image annotationthe expert-annotated tumor sub-regions for each of the 146 cases are provided along with a metadata (csv file) of study location, scanner type, where available.benefit to researchersthe contribution of brats-africa dataset is two-fold: 1) its potential for use in research leading towards generalizable and inclusive diagnostic tools applicable across all settings including resource constrained environments, and 2) its ability to describe the peculiarities of neuroimaging in african settings. brain cancer brain mr segmentation diagnosis other community\"},{\"id\":41599,\"type\":\"collection\",\"slug\":\"breast-cancer-screening-dbt\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/breast-cancer-screening-dbt\\\/\",\"title\":\"Breast Cancer Screening - Digital Breast Tomosynthesis\",\"short_title\":\"Breast-Cancer-Screening-DBT\",\"doi\":\"10.7937\\\/E4WT-CD02\",\"summary\":\"Breast cancer is among the most common cancers and a common cause of death among women. Over 39 million breast cancer screening exams are performed every year and are among the most common radiological tests. This creates a high need for accurate image interpretation. Machine learning has shown promise in interpretation of medical images. However, limited data for training and validation remains an issue.Here, we share a curated dataset of digital breast tomosynthesis images that includes normal, actionable, biopsy-proven benign, and biopsy-proven cancer cases.\\u00a0 The dataset contains four components: (1) DICOM images, (2) a spreadsheet indicating which group each case belongs to (3) annotation boxes, and (4) Image paths for patients\\\/studies\\\/views.\\u00a0 A detailed description of this dataset can be found in the following paper; please reference this paper if you use this dataset:M. Buda, A. Saha, R. Walsh, S. Ghate, N. Li, A. \\u015awi\\u0119cicki, J. Y. Lo, M. A. Mazurowski, Detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model.\\u00a0(https:\\\/\\\/doi.org\\\/10.1001\\\/jamanetworkopen.2021.19100).Additional information and resources related to this dataset can be found here:\\u00a0https:\\\/\\\/sites.duke.edu\\\/mazurowski\\\/resources\\\/digital-breast-tomosynthesis-database\\\/A Version 1 of the dataset contains only a subset of all data described in the paper above. More data will be share in subsequent versions.Please visit this discussion forum for any questions related to the data: https:\\\/\\\/www.reddit.com\\\/r\\\/DukeDBTData\\\/Required Preprocessing of DBT ImagesFor some of the images, the laterality stored in the DICOM header and\\\/or image orientation are incorrect. The reference standard \\\"truth\\\" boxes are defined with respect to the\\u00a0corrected\\u00a0image orientation in these instances. Therefore, it is\\u00a0crucial\\u00a0to provide your results for images in the correct image orientation. Python functions for loading image data from a DICOM file into 3D array of pixel values in the correct orientation and for displaying \\\"truth\\\" boxes (if any) are on\\u00a0GitHub. Please see the readme file there for instructions.DBTex Lesion Detection Challenge PredictionsThe\\u00a0DBTex lesion detection challenge tasked participating teams with detecting lesions in the BCS-DBT test set. The challenge had two phases:\\u00a0DBTex1\\u00a0and\\u00a0DBTex2.\\u00a0Here\\u00a0we provide the BCS-DBT lesion predictions made by all participating teams for both phases, for both the BCS-DBT test and validation sets, as \\u201cteam_predictions_bothphases.zip\\u201d. Please see\\u00a0here\\u00a0under \\u201cOutput format for the DBTex2 Challenge test set results\\u201d for a description of how these results are formatted. Finally, when comparing lesion bounding box predictions to the image data, be sure to load the images correctly according to the above \\u201cRequired Preprocessing of DBT Images\\u201d.If you use these predictions, please reference the DBTex challenge paper:Konz N, Buda M, Gu H, et al. A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis. JAMA Netw Open.\\u00a02023;6(2):e230524. doi:10.1001\\\/jamanetworkopen.2023.0524\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MG\",\"Other\",\"Classification\",\"Measurement\",\"Radiomic Feature\"],\"supporting_data\":[\"Clinical\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":5060,\"date_updated\":\"2024-01-18\",\"status\":\"Ongoing\",\"license\":\"CC BY-NC 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&PatientCriteria=DBT-P00001,DBT-P00002,DBT-P00018,DBT-P00047,DBT-P00069,DBT-P00082,DBT-P00084,DBT-P00114,DBT-P00133,DBT-P00160,DBT-P00165,DBT-P00174,DBT-P00176,DBT-P00187,DBT-P00213,DBT-P00234,DBT-P00235,DBT-P00238,DBT-P00271,DBT-P00273,DBT-P00283,DBT-P00351,DBT-P00385,DBT-P00417,DBT-P00431,DBT-P00450,DBT-P00547,DBT-P00549,DBT-P00558,DBT-P00577,DBT-P00585,DBT-P00643,DBT-P00651,DBT-P00655,DBT-P00697,DBT-P00705,DBT-P00714,DBT-P00718,DBT-P00789,DBT-P00802,DBT-P00803,DBT-P00839,DBT-P00843,DBT-P00844,DBT-P00864,DBT-P00866,DBT-P00871,DBT-P00878,DBT-P00902,DBT-P00908,DBT-P00925,DBT-P00945,DBT-P01020,DBT-P01043,DBT-P01047,DBT-P01083,DBT-P01105,DBT-P01117,DBT-P01150,DBT-P01152,DBT-P01153,DBT-P01173,DBT-P01177,DBT-P01206,DBT-P01207,DBT-P01212,DBT-P01226,DBT-P01239,DBT-P01249,DBT-P01293,DBT-P01296,DBT-P01305,DBT-P01334,DBT-P01353,DBT-P01356,DBT-P01374,DBT-P01490,DBT-P01496,DBT-P01536,DBT-P01538,DBT-P01555,DBT-P01562,DBT-P01564,DBT-P01578,DBT-P01604,DBT-P01610,DBT-P01630,DBT-P01634,DBT-P01635,DBT-P01642,DBT-P01700,DBT-P01705,DBT-P01746,DBT-P01772,DBT-P01778,DBT-P01791,DBT-P01792,DBT-P01799,DBT-P01807,DBT-P01849,DBT-P01851,DBT-P01916,DBT-P01932,DBT-P01938,DBT-P01941,DBT-P01948,DBT-P01957,DBT-P01967,DBT-P02012,DBT-P02018,DBT-P02042,DBT-P02072,DBT-P02085,DBT-P02111,DBT-P02113,DBT-P02117,DBT-P02160,DBT-P02170,DBT-P02183,DBT-P02185,DBT-P02189,DBT-P02204,DBT-P02214,DBT-P02226,DBT-P02242,DBT-P02248,DBT-P02253,DBT-P02284,DBT-P02294,DBT-P02296,DBT-P02301,DBT-P02314,DBT-P02322,DBT-P02326,DBT-P02331,DBT-P02341,DBT-P02360,DBT-P02381,DBT-P02394,DBT-P02469,DBT-P02498,DBT-P02501,DBT-P02516,DBT-P02526,DBT-P02528,DBT-P02545,DBT-P02558,DBT-P02560,DBT-P02574,DBT-P02598,DBT-P02648,DBT-P02669,DBT-P02686,DBT-P02732,DBT-P02754,DBT-P02766,DBT-P02776,DBT-P02816,DBT-P02864,DBT-P02898,DBT-P02946,DBT-P02959,DBT-P02989,DBT-P03002,DBT-P03027,DBT-P03038,DBT-P03056,DBT-P03092,DBT-P03097,DBT-P03110,DBT-P03113,DBT-P03129,DBT-P03166,DBT-P03188,DBT-P03190,DBT-P03215,DBT-P03240,DBT-P03263,DBT-P03317,DBT-P03340,DBT-P03343,DBT-P03346,DBT-P03360,DBT-P03370,DBT-P03377,DBT-P03390,DBT-P03403,DBT-P03448,DBT-P03464,DBT-P03470,DBT-P03483,DBT-P03501,DBT-P03543,DBT-P03574,DBT-P03576,DBT-P03582,DBT-P03592,DBT-P03598,DBT-P03605,DBT-P03621,DBT-P03628,DBT-P03651,DBT-P03689,DBT-P03694,DBT-P03710,DBT-P03725,DBT-P03726,DBT-P03728,DBT-P03831,DBT-P03898,DBT-P03901,DBT-P03932,DBT-P03936,DBT-P03937,DBT-P03948,DBT-P03968,DBT-P03979,DBT-P04008,DBT-P04017,DBT-P04031,DBT-P04033,DBT-P04037,DBT-P04045,DBT-P04081,DBT-P04084,DBT-P04087,DBT-P04097,DBT-P04105,DBT-P04106,DBT-P04139,DBT-P04158,DBT-P04163,DBT-P04171,DBT-P04174,DBT-P04208,DBT-P04210,DBT-P04228,DBT-P04247,DBT-P04257,DBT-P04293,DBT-P04295,DBT-P04299,DBT-P04310,DBT-P04317,DBT-P04344,DBT-P04346,DBT-P04368,DBT-P04380,DBT-P04396,DBT-P04446,DBT-P04452,DBT-P04455,DBT-P04497,DBT-P04515,DBT-P04517,DBT-P04541,DBT-P04577,DBT-P04586,DBT-P04596,DBT-P04622,DBT-P04642,DBT-P04677,DBT-P04684,DBT-P04693,DBT-P04695,DBT-P04697,DBT-P04744,DBT-P04803,DBT-P04808,DBT-P04837,DBT-P04857,DBT-P04874,DBT-P04884,DBT-P04911,DBT-P04963,DBT-P05000,DBT-P05005,DBT-P05018,DBT-P05025,DBT-P05060&CollectionCriteria=Breast-Cancer-Screening-DBT\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/BSC-DBT-Train-manifest.tcia\",\"blob\":\"breast cancer screening - digital breast tomosynthesis breast-cancer-screening-dbt breast cancer is among the most common cancers and a common cause of death among women. over 39 million breast cancer screening exams are performed every year and are among the most common radiological tests. this creates a high need for accurate image interpretation. machine learning has shown promise in interpretation of medical images. however, limited data for training and validation remains an issue.here, we share a curated dataset of digital breast tomosynthesis images that includes normal, actionable, biopsy-proven benign, and biopsy-proven cancer cases.\\u00a0 the dataset contains four components: (1) dicom images, (2) a spreadsheet indicating which group each case belongs to (3) annotation boxes, and (4) image paths for patients\\\/studies\\\/views.\\u00a0 a detailed description of this dataset can be found in the following paper; please reference this paper if you use this dataset:m. buda, a. saha, r. walsh, s. ghate, n. li, a. \\u015awi\\u0119cicki, j. y. lo, m. a. mazurowski, detection of masses and architectural distortions in digital breast tomosynthesis: a publicly available dataset of 5,060 patients and a deep learning model.\\u00a0(https:\\\/\\\/doi.org\\\/10.1001\\\/jamanetworkopen.2021.19100).additional information and resources related to this dataset can be found here:\\u00a0https:\\\/\\\/sites.duke.edu\\\/mazurowski\\\/resources\\\/digital-breast-tomosynthesis-database\\\/a version 1 of the dataset contains only a subset of all data described in the paper above. more data will be share in subsequent versions.please visit this discussion forum for any questions related to the data: https:\\\/\\\/www.reddit.com\\\/r\\\/dukedbtdata\\\/required preprocessing of dbt imagesfor some of the images, the laterality stored in the dicom header and\\\/or image orientation are incorrect. the reference standard \\\"truth\\\" boxes are defined with respect to the\\u00a0corrected\\u00a0image orientation in these instances. therefore, it is\\u00a0crucial\\u00a0to provide your results for images in the correct image orientation. python functions for loading image data from a dicom file into 3d array of pixel values in the correct orientation and for displaying \\\"truth\\\" boxes (if any) are on\\u00a0github. please see the readme file there for instructions.dbtex lesion detection challenge predictionsthe\\u00a0dbtex lesion detection challenge tasked participating teams with detecting lesions in the bcs-dbt test set. the challenge had two phases:\\u00a0dbtex1\\u00a0and\\u00a0dbtex2.\\u00a0here\\u00a0we provide the bcs-dbt lesion predictions made by all participating teams for both phases, for both the bcs-dbt test and validation sets, as \\u201cteam_predictions_bothphases.zip\\u201d. please see\\u00a0here\\u00a0under \\u201coutput format for the dbtex2 challenge test set results\\u201d for a description of how these results are formatted. finally, when comparing lesion bounding box predictions to the image data, be sure to load the images correctly according to the above \\u201crequired preprocessing of dbt images\\u201d.if you use these predictions, please reference the dbtex challenge paper:konz n, buda m, gu h, et al. a competition, benchmark, code, and data for using artificial intelligence to detect lesions in digital breast tomosynthesis. jama netw open.\\u00a02023;6(2):e230524. doi:10.1001\\\/jamanetworkopen.2023.0524 breast cancer breast mg other classification measurement radiomic feature clinical software\\\/source code community\"},{\"id\":41607,\"type\":\"collection\",\"slug\":\"breast-diagnosis\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/breast-diagnosis\\\/\",\"title\":\"BREAST-DIAGNOSIS\",\"short_title\":\"BREAST-DIAGNOSIS\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.SDNRQXXR\",\"summary\":\"The Breast-Diagnosis collection contains cases that are high-risk normals, DCIS, fibroids and lobular carcinomas. Each case has 3 or more distinct MR pulse sequences from a Phillips 1.5 T (usual sequences are labeled T2, STIR and BLISS but may occasionally include other pulse sequences and digital mammogram of tumor specimen). Multiple time point studies on the same patient are possible.The following is relevant to analyzing the contrast dynamics of the BLISS pulse sequences. The pulse sequence parameters (repetition, echo time, etc.) can be extracted from the DICOM tags. The contrast aspects are as follows: The volume of Magnevist (Bayer) gadolinium contrast injected into the brachial vein is based on a rule of thumb which in ml's is 10% of the patient weight in POUNDS (NOT kilograms as is recorded in the DICOM patient weight tag. Hence the injected volume for a 150 lb patient is 15 ml. (the DICOM tag entry on that patient will read \\\"68\\\"). The injection itself is 6 or 7 seconds, at a rate of 3cc per second. The first dynamic sequence is started 1 minute after the injection is started. Slice and pulse parameters are accessible in the DICOM tags.This collection also includes a spreadsheet (updated 7\\\/16\\\/11) with BIRADS MRI features from the imaging report and denoted key image slice with the approximate X-Y center position if a mass was found. Key clinical features and abstracts of the pathology report including ER, PR and HER2 results and Oncotype score are included when available.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MR\",\"CT\",\"PT\",\"MG\",\"Diagnosis\",\"Molecular Test\",\"Demographic\",\"Pathology Detail\",\"Radiomic Feature\",\"Measurement\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":88,\"date_updated\":\"2011-11-09\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=BREAST-DIAGNOSIS\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_BREAST-DIAGNOSIS_06-22-2015.tcia\",\"blob\":\"breast-diagnosis breast-diagnosis the breast-diagnosis collection contains cases that are high-risk normals, dcis, fibroids and lobular carcinomas. each case has 3 or more distinct mr pulse sequences from a phillips 1.5 t (usual sequences are labeled t2, stir and bliss but may occasionally include other pulse sequences and digital mammogram of tumor specimen). multiple time point studies on the same patient are possible.the following is relevant to analyzing the contrast dynamics of the bliss pulse sequences. the pulse sequence parameters (repetition, echo time, etc.) can be extracted from the dicom tags. the contrast aspects are as follows: the volume of magnevist (bayer) gadolinium contrast injected into the brachial vein is based on a rule of thumb which in ml's is 10% of the patient weight in pounds (not kilograms as is recorded in the dicom patient weight tag. hence the injected volume for a 150 lb patient is 15 ml. (the dicom tag entry on that patient will read \\\"68\\\"). the injection itself is 6 or 7 seconds, at a rate of 3cc per second. the first dynamic sequence is started 1 minute after the injection is started. slice and pulse parameters are accessible in the dicom tags.this collection also includes a spreadsheet (updated 7\\\/16\\\/11) with birads mri features from the imaging report and denoted key image slice with the approximate x-y center position if a mass was found. key clinical features and abstracts of the pathology report including er, pr and her2 results and oncotype score are included when available. breast cancer breast mr ct pt mg diagnosis molecular test demographic pathology detail radiomic feature measurement clinical image analyses community\"},{\"id\":41277,\"type\":\"collection\",\"slug\":\"breast-lesions-usg\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/breast-lesions-usg\\\/\",\"title\":\"A Curated Benchmark Dataset for Ultrasound Based Breast Lesion Analysis\",\"short_title\":\"Breast-Lesions-USG\",\"doi\":\"10.7937\\\/9WKK-Q141\",\"summary\":\"This dataset consists of 256 breast ultrasound scans collected from 256 patients and 266 benign and malignant segmented lesions.\\u00a0 It includes patient-level labels, image-level annotations, and tumor-level labels with all cases confirmed by follow-up care or biopsy result. Each scan was manually annotated and labeled by a radiologist experienced in breast ultrasound examination. In particular, each tumor was identified in the image via a freehand annotation and labeled according to BIRADS features. The tumor histopathological classification is stated for patients who underwent a biopsy. Patient-level labels include clinical data such as age, breast tissue composition, signs and symptoms. Image-level freehand annotations identify the tumor and other abnormal areas in the image. The tumor and image are labeled with BIRADS category, 7 BIRADS descriptors, and interpretation of critical findings as presence of breast diseases. Additional labels include the method of verification, tumor classification and histopathological diagnosis.Since the role of machine learning and theoretical computing towards the development of augmented inference in the field of cancer detection is indisputable, the quality of the data used to develop any explainable augmented inference methods is extremely important. This dataset can be used as an external testing set for assessing a model\\u2019s performance and for developing explainable AI or supervised machine learning models for the detection, segmentation and classification of breast abnormalities in ultrasound images.A detailed description of this dataset can be found here and should be cited along with the citation of the data:Paw\\u0142owska, A., \\u0106wierz-Pie\\u0144kowska, A., Domalik, A., Jagu\\u015b, D., Kasprzak, P., Matkowski, R., Fura, \\u0141., Nowicki, A., & Zolek, N. A Curated benchmark dataset for ultrasound based breast lesion analysis. Sci Data 11, 148 (2024).\\u00a0https:\\\/\\\/doi.org\\\/10.1038\\\/s41597-024-02984-z.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/BrEaST-Lesions_WIKI-Image_3.png\",\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"US\",\"Segmentation\",\"Radiomic Feature\",\"Classification\",\"Diagnosis\",\"Follow-Up\"],\"supporting_data\":[\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":256,\"date_updated\":\"2024-01-08\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/BrEaST-Lesions_USG-images_and_masks-Dec-15-2023.zip\",\"blob\":\"a curated benchmark dataset for ultrasound based breast lesion analysis breast-lesions-usg this dataset consists of 256 breast ultrasound scans collected from 256 patients and 266 benign and malignant segmented lesions.\\u00a0 it includes patient-level labels, image-level annotations, and tumor-level labels with all cases confirmed by follow-up care or biopsy result. each scan was manually annotated and labeled by a radiologist experienced in breast ultrasound examination. in particular, each tumor was identified in the image via a freehand annotation and labeled according to birads features. the tumor histopathological classification is stated for patients who underwent a biopsy. patient-level labels include clinical data such as age, breast tissue composition, signs and symptoms. image-level freehand annotations identify the tumor and other abnormal areas in the image. the tumor and image are labeled with birads category, 7 birads descriptors, and interpretation of critical findings as presence of breast diseases. additional labels include the method of verification, tumor classification and histopathological diagnosis.since the role of machine learning and theoretical computing towards the development of augmented inference in the field of cancer detection is indisputable, the quality of the data used to develop any explainable augmented inference methods is extremely important. this dataset can be used as an external testing set for assessing a model\\u2019s performance and for developing explainable ai or supervised machine learning models for the detection, segmentation and classification of breast abnormalities in ultrasound images.a detailed description of this dataset can be found here and should be cited along with the citation of the data:paw\\u0142owska, a., \\u0106wierz-pie\\u0144kowska, a., domalik, a., jagu\\u015b, d., kasprzak, p., matkowski, r., fura, \\u0141., nowicki, a., & zolek, n. a curated benchmark dataset for ultrasound based breast lesion analysis. sci data 11, 148 (2024).\\u00a0https:\\\/\\\/doi.org\\\/10.1038\\\/s41597-024-02984-z.\\u00a0 breast cancer breast us segmentation radiomic feature classification diagnosis follow-up software\\\/source code community\"},{\"id\":43603,\"type\":\"collection\",\"slug\":\"breast-mri-nact-pilot\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/breast-mri-nact-pilot\\\/\",\"title\":\"Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy\",\"short_title\":\"Breast-MRI-NACT-Pilot\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.QHsyhJKy\",\"summary\":\"This collection contains longitudinal DCE MRI studies of 64 patients undergoing neoadjuvant chemotherapy (NACT) for invasive breast cancer.\\u00a0 Patient population This pilot study to investigate the use of serial DCE MRI examinations during neoadjuvant chemotherapy for invasive breast cancer recruited 68 patients with stage II or III locally advanced breast cancer enrolled between 1995 and 2002 in an institutional review board (IRB)-approved research protocol.\\u00a0 All patients gave written informed consent to participate. All patients had confirmed breast cancer diagnoses based on histopathology of biopsy or surgical excision, and none had prior treatment with chemotherapy, surgery or radiation. All patients received pre-operative chemotherapy with four cycles of adriamycin-cytoxan administered every three weeks. A subset of 17 patients received additional weekly treatment with taxane after the Anthracycline regimen was completed. Three DCE-MRI scans were scheduled for all patients: MRI1\\u00a0before treatment; MRI2 after one cycle of chemotherapy;\\u00a0MRI3 after completion of Anthracycline treatment and before surgery or further treatment. Subjects receiving Taxane were scheduled for an additional exam, MRI4, after completion of all chemotherapy treatment and before surgery.\\u00a0Figure 1.\\u00a0UCSF Pilot NACT study schema. Only an N=17 subset of patients received Taxane and MRI4 as a result of changes in standard of care during the course of the study. Note: Not all patients received core biopsies as indicated in this schema.Exclusions: Of the 68 patients, four have been excluded from this collection: one did not undergo surgery, one did not have a pre-surgery time-point MRI, one was rejected due to unanalyzable MR images, and one had metastatic disease prior to the completion of treatment. None of the excluded patients were from the 17 subject Taxane group. Three patients are included here but may be excluded at researchers' discretion due to treatment anomalies: a) Two subjects in the Taxane group (UCSF_BR_30 and UCSF_BR_53) did not complete the Taxane treatment, but were moved directly to surgery early and did not have the final MRI4 examination, and b) Patient UCSF_BR_27 declined standard-of-care post-surgery radiation and hormonal treatments. These cases are noted in the accompanying clinical information workbook.Endpoint data: Recurrence-free survival (RFS) was assessed for each patient at 6-month or 1-year intervals following surgery. For patients that recurred, length of RFS was defined as the time from initial surgery to either local or distant recurrence. Other clinical and endpoint data includes patient age, lesion characteristics including pretreatment tumor size, histologic type, pathologic size, tumor subtype, and lymph node involvement. These data are included as supplemental information for the collection\\u00a0in the accompanying clinical information workbook. MRI acquisition Breast MRI was acquired on a 1.5-T scanner (Signa, GE Healthcare, Milwaukee, WI) using a bilateral phased array breast coil. The MR imaging protocol included a 3D localizer and a unilateral sagittal\\u00a0DCE acquisition. The DCE acquisition utilized a high spatial resolution, low temporal resolution, T1-weighted, fat-suppressed 3D fast gradient-recalled echo sequence\\u00a0developed for pre-surgical staging\\u00a0(TR\\\/TE 8\\\/4.2; flip angle 20 degrees; field of view 18-20 cm; acquisition matrix 256 x 192 x 60, section thickness 2 mm; spatial resolution 0.7 x 0.94 x 2.0 mm3). A minimum of three time points were acquired during each contrast-enhanced MRI protocol: a pre-contrast scan (t0), followed by 2 consecutive post-contrast time points: early (t1) and late (t2) phases.\\u00a0The gadopentetate dimeglumine contrast agent\\u00a0(Magnevist, Bayer HealthCare, Berlin, Germany), was injected at a dose of 0.1 mmol\\\/kg of body weight (injection rate = 1.2 mL per second) followed by a 10 mL saline flush, with injection starting coincident with the start of the early t 1 \\u00a0phase acquisition. Imaging time was approximately 5 minutes per acquisition, resulting in effective early and late post-contrast time points of 2.5 minutes and 7.5 minutes from the start of the contrast injection, respectively, using standard k-space sampling. Fat suppression was performed using a frequency-selective inversion recovery preparatory pulse.This shared data set was provided by David Newitt, PhD and Nola Hylton, PhD from\\u00a0the Breast Imaging Research Program at UCSF.This work was supported by grants:National Institute of Health\\\/National Cancer Institute: IUO1CA151235; RO1CA132870Susan G Komen: SAC110017\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Fig1.png\",\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MR\",\"SEG\",\"Demographic\",\"Diagnosis\",\"Molecular Test\",\"Follow-Up\",\"Treatment\",\"Measurement\",\"Other\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":64,\"date_updated\":\"2023-08-07\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=BREAST-MRI-NACT-Pilot\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/doiJNLP-J4OtCtbF.tcia\",\"blob\":\"single site breast dce-mri data and segmentations from patients undergoing neoadjuvant chemotherapy breast-mri-nact-pilot this collection contains longitudinal dce mri studies of 64 patients undergoing neoadjuvant chemotherapy (nact) for invasive breast cancer.\\u00a0 patient population this pilot study to investigate the use of serial dce mri examinations during neoadjuvant chemotherapy for invasive breast cancer recruited 68 patients with stage ii or iii locally advanced breast cancer enrolled between 1995 and 2002 in an institutional review board (irb)-approved research protocol.\\u00a0 all patients gave written informed consent to participate. all patients had confirmed breast cancer diagnoses based on histopathology of biopsy or surgical excision, and none had prior treatment with chemotherapy, surgery or radiation. all patients received pre-operative chemotherapy with four cycles of adriamycin-cytoxan administered every three weeks. a subset of 17 patients received additional weekly treatment with taxane after the anthracycline regimen was completed. three dce-mri scans were scheduled for all patients: mri1\\u00a0before treatment; mri2 after one cycle of chemotherapy;\\u00a0mri3 after completion of anthracycline treatment and before surgery or further treatment. subjects receiving taxane were scheduled for an additional exam, mri4, after completion of all chemotherapy treatment and before surgery.\\u00a0figure 1.\\u00a0ucsf pilot nact study schema. only an n=17 subset of patients received taxane and mri4 as a result of changes in standard of care during the course of the study. note: not all patients received core biopsies as indicated in this schema.exclusions: of the 68 patients, four have been excluded from this collection: one did not undergo surgery, one did not have a pre-surgery time-point mri, one was rejected due to unanalyzable mr images, and one had metastatic disease prior to the completion of treatment. none of the excluded patients were from the 17 subject taxane group. three patients are included here but may be excluded at researchers' discretion due to treatment anomalies: a) two subjects in the taxane group (ucsf_br_30 and ucsf_br_53) did not complete the taxane treatment, but were moved directly to surgery early and did not have the final mri4 examination, and b) patient ucsf_br_27 declined standard-of-care post-surgery radiation and hormonal treatments. these cases are noted in the accompanying clinical information workbook.endpoint data: recurrence-free survival (rfs) was assessed for each patient at 6-month or 1-year intervals following surgery. for patients that recurred, length of rfs was defined as the time from initial surgery to either local or distant recurrence. other clinical and endpoint data includes patient age, lesion characteristics including pretreatment tumor size, histologic type, pathologic size, tumor subtype, and lymph node involvement. these data are included as supplemental information for the collection\\u00a0in the accompanying clinical information workbook. mri acquisition breast mri was acquired on a 1.5-t scanner (signa, ge healthcare, milwaukee, wi) using a bilateral phased array breast coil. the mr imaging protocol included a 3d localizer and a unilateral sagittal\\u00a0dce acquisition. the dce acquisition utilized a high spatial resolution, low temporal resolution, t1-weighted, fat-suppressed 3d fast gradient-recalled echo sequence\\u00a0developed for pre-surgical staging\\u00a0(tr\\\/te 8\\\/4.2; flip angle 20 degrees; field of view 18-20 cm; acquisition matrix 256 x 192 x 60, section thickness 2 mm; spatial resolution 0.7 x 0.94 x 2.0 mm3). a minimum of three time points were acquired during each contrast-enhanced mri protocol: a pre-contrast scan (t0), followed by 2 consecutive post-contrast time points: early (t1) and late (t2) phases.\\u00a0the gadopentetate dimeglumine contrast agent\\u00a0(magnevist, bayer healthcare, berlin, germany), was injected at a dose of 0.1 mmol\\\/kg of body weight (injection rate = 1.2 ml per second) followed by a 10 ml saline flush, with injection starting coincident with the start of the early t 1 \\u00a0phase acquisition. imaging time was approximately 5 minutes per acquisition, resulting in effective early and late post-contrast time points of 2.5 minutes and 7.5 minutes from the start of the contrast injection, respectively, using standard k-space sampling. fat suppression was performed using a frequency-selective inversion recovery preparatory pulse.this shared data set was provided by david newitt, phd and nola hylton, phd from\\u00a0the breast imaging research program at ucsf.this work was supported by grants:national institute of health\\\/national cancer institute: iuo1ca151235; ro1ca132870susan g komen: sac110017 breast cancer breast mr seg demographic diagnosis molecular test follow-up treatment measurement other clinical image analyses community\"},{\"id\":41639,\"type\":\"collection\",\"slug\":\"burdenko-gbm-progression\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/burdenko-gbm-progression\\\/\",\"title\":\"Burdenko's Glioblastoma Progression Dataset\",\"short_title\":\"Burdenko-GBM-Progression\",\"doi\":\"10.7937\\\/E1QP-D183\",\"summary\":\"The Burdenko Glioblastoma Progression Dataset (BGPD) is a systematic data collection from 180 patients with primary glioblastoma treated at the Burdenko National Medical Research Center of Neurosurgery between 2014 and 2020.\\u00a0For each patient, the dataset includes imaging studies conducted for radiotherapy planning and follow-up studies. The radiotherapy studies consist of 4 MRI sequences (T1, T1C, T2, FLAIR), a topometric CT scan, and associated radiotherapy planning files (RTSTRUCT, RTPLAN, and RTDOSE). Follow-up studies (from 1 to 8-time per patient) include 2-4 MRI sequences (with a minimal set of T1C and FLAIR) per patient. Additional genetic information (IDH1\\\/2, MGMT mutations); and a treatment response status (tumour progression, tumour pseudoprogression, treatment response) are available for a subset of patients.\\u00a0The MRI studies were obtained from different sites, with scanners from 4 vendors and varying scanning protocols. CT studies were obtained using a unified scanning protocol.Planning and follow-up studiesEach RTSTRUCT contains information on Gross Tumour Volume (GTV), Clinical Target Volume (CTV), and Planning Target Volume (PTV). In addition, each RTSTRUCT includes annotations of 10 anatomical structures: Eyes (Left, Right), Lenses (Left, Right), Optic Nerves (Left, Right), Brain, Brain stem, Chiasm, and external contour (Head).\\u00a0\\u00a0For a subset of patients RTSTRUCTs include annotation of Gross Tumour Volume assessed on follow-up (longitudinal) studies.For each patient, we supplement imaging information with clinical data: IDH1\\\/2 gene mutation is available for 110 patients (97 negative, 13 positive), MGMT promoter methylation status is available for 92 patients (55 negative, 37 positive), death date is available for 78 patients (anonymized with preserved time shift), surgery date (anonymized with preserved time shift) and treatment response status (treatment response, tumour progression, tumour pseudoprogression) are available for all control studies.Additional informationAll MRIs are provided in the original acquisition space, while RTSTRUCTs, RTPLANs, and RTDOSEs are aligned with topometric CT scans. MRI to CT registration files are not provided as a part of the collection, however, here is a github link supporting a containerized solution (written in Python, based on ANTs library) that runs all necessary images and masks alignment.\\u00a0A subset of MRI images are para-transversal (direction cosine vectors as stored in Image Orientation Patient DICOM attribute form an orthonormal basis, but not a canonical one).\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Burdenko-GBM-Progression-scaled.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Glioblastoma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"CT\",\"MR\",\"RTPLAN\",\"RTDOSE\",\"RTSTRUCT\",\"Molecular Test\",\"Demographic\",\"Follow-Up\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":180,\"date_updated\":\"2023-04-10\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?saved-cart=nbia-77781679410547970\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Burdenko-GBM-Progression-March-2023-manifest.tcia\",\"blob\":\"burdenko's glioblastoma progression dataset burdenko-gbm-progression the burdenko glioblastoma progression dataset (bgpd) is a systematic data collection from 180 patients with primary glioblastoma treated at the burdenko national medical research center of neurosurgery between 2014 and 2020.\\u00a0for each patient, the dataset includes imaging studies conducted for radiotherapy planning and follow-up studies. the radiotherapy studies consist of 4 mri sequences (t1, t1c, t2, flair), a topometric ct scan, and associated radiotherapy planning files (rtstruct, rtplan, and rtdose). follow-up studies (from 1 to 8-time per patient) include 2-4 mri sequences (with a minimal set of t1c and flair) per patient. additional genetic information (idh1\\\/2, mgmt mutations); and a treatment response status (tumour progression, tumour pseudoprogression, treatment response) are available for a subset of patients.\\u00a0the mri studies were obtained from different sites, with scanners from 4 vendors and varying scanning protocols. ct studies were obtained using a unified scanning protocol.planning and follow-up studieseach rtstruct contains information on gross tumour volume (gtv), clinical target volume (ctv), and planning target volume (ptv). in addition, each rtstruct includes annotations of 10 anatomical structures: eyes (left, right), lenses (left, right), optic nerves (left, right), brain, brain stem, chiasm, and external contour (head).\\u00a0\\u00a0for a subset of patients rtstructs include annotation of gross tumour volume assessed on follow-up (longitudinal) studies.for each patient, we supplement imaging information with clinical data: idh1\\\/2 gene mutation is available for 110 patients (97 negative, 13 positive), mgmt promoter methylation status is available for 92 patients (55 negative, 37 positive), death date is available for 78 patients (anonymized with preserved time shift), surgery date (anonymized with preserved time shift) and treatment response status (treatment response, tumour progression, tumour pseudoprogression) are available for all control studies.additional informationall mris are provided in the original acquisition space, while rtstructs, rtplans, and rtdoses are aligned with topometric ct scans. mri to ct registration files are not provided as a part of the collection, however, here is a github link supporting a containerized solution (written in python, based on ants library) that runs all necessary images and masks alignment.\\u00a0a subset of mri images are para-transversal (direction cosine vectors as stored in image orientation patient dicom attribute form an orthonormal basis, but not a canonical one). glioblastoma brain ct mr rtplan rtdose rtstruct molecular test demographic follow-up clinical genomics software\\\/source code community\"},{\"id\":41845,\"type\":\"collection\",\"slug\":\"c-nmc-2019\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/c-nmc-2019\\\/\",\"title\":\"C_NMC_2019 Dataset: ALL Challenge dataset of ISBI 2019\",\"short_title\":\"C-NMC 2019\",\"doi\":\"10.7937\\\/tcia.2019.dc64i46r\",\"summary\":\"Acute lymphoblastic leukemia (ALL) constitutes approximately 25% of the pediatric cancers. In general, the task of identifying immature leukemic blasts from normal cells under the microscope is challenging because morphologically the images of the two cells appear similar.Challenge is split into 3 separate phases:Train set composition:Total subjects: 73, ALL (cancer): 47, Normal: 26Total cell images: 10,661, ALL(cancer): 7272, Normal: 3389 Preliminary test set composition:Total subjects: 28, ALL (cancer): 13, Normal: 15Total cell images: 1867, ALL(cancer): 1219, Normal: 648Final test set composition:Total subjects: 17, ALL (cancer): 9, Normal: 8Total cell images: 2586\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Leukemia\"],\"cancer_locations\":[\"Blood\",\"Bone\"],\"data_types\":[\"Histopathology\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":118,\"date_updated\":\"2019-03-26\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22C-NMC+2019%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjczNCIsInBhc3Njb2RlIjoiNDM3ZmMzM2RkMzQ1ZmMzZjNjM2FlY2JmZWQ0MThlY2NjYTkzM2RmMiIsInBhY2thZ2VfaWQiOiI3MzQiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"c_nmc_2019 dataset: all challenge dataset of isbi 2019 c-nmc 2019 acute lymphoblastic leukemia (all) constitutes approximately 25% of the pediatric cancers. in general, the task of identifying immature leukemic blasts from normal cells under the microscope is challenging because morphologically the images of the two cells appear similar.challenge is split into 3 separate phases:train set composition:total subjects: 73, all (cancer): 47, normal: 26total cell images: 10,661, all(cancer): 7272, normal: 3389 preliminary test set composition:total subjects: 28, all (cancer): 13, normal: 15total cell images: 1867, all(cancer): 1219, normal: 648final test set composition:total subjects: 17, all (cancer): 9, normal: 8total cell images: 2586 leukemia blood bone histopathology other community\"},{\"id\":42171,\"type\":\"collection\",\"slug\":\"c4kc-kits\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/c4kc-kits\\\/\",\"title\":\"Data from the training set of the 2019 Kidney and Kidney Tumor Segmentation Challenge\",\"short_title\":\"C4KC-KiTS\",\"doi\":\"10.7937\\\/TCIA.2019.IX49E8NX\",\"summary\":\"This collection contains CT scans and segmentations from subjects from the training set of the 2019 Kidney and Kidney Tumor Segmentation Challenge (KiTS19). The challenge aimed to accelerate progress in automatic 3D semantic segmentation by releasing a dataset of CT scans for 210 patients with manual semantic segmentations of the kidneys and tumors in the corticomedullary phase. The imaging was collected during routine care of patients who were treated by either partial or radical nephrectomy at the University of Minnesota Medical Center. Many of the CT scans were acquired at referring institutions and are therefore heterogeneous in terms of scanner manufacturers and acquisition protocols. Semantic segmentations were performed by students under the supervision of an experienced urologic cancer surgeon.ProtocolPlease refer to the data descriptor manuscript for a comprehensive account of the data collection and annotation process - arXiv:1904.00445.\\u00a0The Clinical Trial Time Point is calculated from Day of Surgery.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/c4kc-kits.png\",\"program\":\"Community\",\"cancer_types\":[\"Kidney Cancer\"],\"cancer_locations\":[\"Kidney\"],\"data_types\":[\"CT\",\"SEG\",\"Demographic\",\"Exposure\",\"Measurement\",\"Diagnosis\",\"Treatment\",\"Follow-Up\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":210,\"date_updated\":\"2020-06-18\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=C4KC-KiTS\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/C4KC-KiTS-NBIA-manifest.tcia\",\"blob\":\"data from the training set of the 2019 kidney and kidney tumor segmentation challenge c4kc-kits this collection contains ct scans and segmentations from subjects from the training set of the 2019 kidney and kidney tumor segmentation challenge (kits19). the challenge aimed to accelerate progress in automatic 3d semantic segmentation by releasing a dataset of ct scans for 210 patients with manual semantic segmentations of the kidneys and tumors in the corticomedullary phase. the imaging was collected during routine care of patients who were treated by either partial or radical nephrectomy at the university of minnesota medical center. many of the ct scans were acquired at referring institutions and are therefore heterogeneous in terms of scanner manufacturers and acquisition protocols. semantic segmentations were performed by students under the supervision of an experienced urologic cancer surgeon.protocolplease refer to the data descriptor manuscript for a comprehensive account of the data collection and annotation process - arxiv:1904.00445.\\u00a0the clinical trial time point is calculated from day of surgery. kidney cancer kidney ct seg demographic exposure measurement diagnosis treatment follow-up community\"},{\"id\":43517,\"type\":\"collection\",\"slug\":\"calgb50303\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/calgb50303\\\/\",\"title\":\"Rituximab and Combination Chemotherapy in Treating Patients With Diffuse Large B-Cell Non-Hodgkin's Lymphoma\",\"short_title\":\"CALGB50303\",\"doi\":\"10.7937\\\/CM65-A013\",\"summary\":\"This collection contains data from the National Cancer Institute Clinical Trial NCT00118209, \\\"Rituximab and Combination Chemotherapy in Treating Patients With Diffuse Large B-Cell Non-Hodgkin's Lymphoma.\\\" It was sponsored by NCI's Alliance for Clinical Trials in Oncology under study number CALGB 50303.\\u00a0 This randomized phase III trial is studying rituximab when given together with two different combination chemotherapy regimens to compare how well they work in treating patients with diffuse large B-cell lymphoma. Select individual patient-level data from this trial can be requested from the NCTN\\\/NCORP Data Archive.Trial DescriptionThis randomized phase III trial studies rituximab when given together with two different combination chemotherapy regimens to compare how well they work in treating patients with diffuse large B-cell non-Hodgkin's lymphoma. Monoclonal antibodies, such as rituximab, may block cancer growth in different ways by targeting certain cells. Drugs used in chemotherapy work in different ways to stop the growth of cancer cells, either by killing the cells, by stopping them from dividing, or by stopping them from spreading. Giving rituximab together with combination chemotherapy may kill more cancer cells. It is not yet known which combination chemotherapy regimen is more effective when given with rituximab in treating diffuse large B-cell non-Hodgkin's lymphoma.Alliance\\\/CALGB 50303 (NCT00118209), an intergroup, phase III study, compared dose-adjusted etoposide, prednisone, vincristine, cyclophosphamide, doxorubicin, and rituximab (DA-EPOCH-R) with standard rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) as frontline therapy for diffuse large B-cell lymphoma.\\u00a0\\u00a0Patients received six cycles of DA-EPOCH-R or R-CHOP. The primary objective was progression-free survival (PFS); secondary clinical objectives included response rate, overall survival (OS), and safety.\\u00a0\\u00a0Between 2005 and 2013, 524 patients were registered; 491 eligible patients were included in the final analysis. Most patients (74%) had stage III or IV disease; International Prognostic Index (IPI) risk groups included 26% IPI 0 to 1, 37% IPI 2, 25% IPI 3, and 12% IPI 4 to 5. At a median follow-up of 5 years, PFS was not statistically different between the arms (hazard ratio, 0.93; 95% CI, 0.68 to 1.27;\\u00a0P\\u00a0= .65), with a 2-year PFS rate of 78.9% (95% CI, 73.8% to 84.2%) for DA-EPOCH-R and 75.5% (95% CI, 70.2% to 81.1%) for R-CHOP. OS was not different (hazard ratio, 1.09; 95% CI, 0.75 to 1.59;\\u00a0P\\u00a0= .64), with a 2-year OS rate of 86.5% (95% CI, 82.3% to 91%) for DA-EPOCH-R and 85.7% (95% CI, 81.4% to 90.2%) for R-CHOP. Grade 3 and 4 adverse events were more common (P\\u00a0< .001) in the DA-EPOCH-R arm than the R-CHOP arm, including infection (16.9%\\u00a0v\\u00a010.7%, respectively), febrile neutropenia (35.0%\\u00a0v\\u00a017.7%, respectively), mucositis (8.4%\\u00a0v\\u00a02.1%, respectively), and neuropathy (18.6%\\u00a0v\\u00a03.3%, respectively). Five treatment-related deaths (2.1%) occurred in each arm.CT scans of the chest, abdomen and pelvis were utilized for tumor staging for 155 of these patients.\\u00a0 Serial fluorodeoxyglucose positron emission tomography (FDG-PET) was conducted at baseline, after 2 cycles of chemotherapy (interim PET [i-PET]), and at end of treatment (EoT) to identify biomarkers of response that are predictive of remission and survival.Trial Outcomes Results of the trial have been reported in the following publications:Sch\\u00f6der, H., Polley, M.-Y. C., Knopp, M. V., Hall, N., Kostakoglu, L., Zhang, J., Higley, H. R., Kelloff, G., Liu, H., Zelenetz, A. D., Cheson, B. D., Wagner-Johnston, N., Kahl, B. S., Friedberg, J. W., Hsi, E. D., Leonard, J. P., Schwartz, L. H., Wilson, W. H., & Bartlett, N. L. (2020). Prognostic value of interim FDG-PET in diffuse large cell lymphoma: results from the CALGB 50303 Clinical Trial. Blood, 135(25), 2224\\u20132234. https:\\\/\\\/doi.org\\\/10.1182\\\/blood.2019003277.\\u00a0PMID: 32232481; PMCID: PMC7316220.Bartlett, N. L., Wilson, W. H., Jung, S.-H., Hsi, E. D., Maurer, M. J., Pederson, L. D., Polley, M.-Y. C., Pitcher, B. N., Cheson, B. D., Kahl, B. S., Friedberg, J. W., Staudt, L. M., Wagner-Johnston, N. D., Blum, K. A., Abramson, J. S., Reddy, N. M., Winter, J. N., Chang, J. E., Gopal, A. K., \\u2026 Leonard, J. P. (2019). Dose-Adjusted EPOCH-R Compared With R-CHOP as Frontline Therapy for Diffuse Large B-Cell Lymphoma: Clinical Outcomes of the Phase III Intergroup Trial Alliance\\\/CALGB 50303. Journal of Clinical Oncology, 37(21), 1790\\u20131799.\\u00a0DOI:\\u00a0https:\\\/\\\/doi.org\\\/10.1200\\\/jco.18.01994.\\u00a0Epub 2019 Apr 2. PMID: 30939090; PMCID: PMC6774813.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/nctn-logo.png\",\"program\":\"NCTN\",\"cancer_types\":[\"Lymphoma\"],\"cancer_locations\":[\"Various\"],\"data_types\":[\"CT\",\"PT\",\"OT\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":155,\"date_updated\":\"2021-03-30\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=CALGB50303\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CALGB50303-Sept-17-2020-NBIA-manifest.tcia\",\"blob\":\"rituximab and combination chemotherapy in treating patients with diffuse large b-cell non-hodgkin's lymphoma calgb50303 this collection contains data from the national cancer institute clinical trial nct00118209, \\\"rituximab and combination chemotherapy in treating patients with diffuse large b-cell non-hodgkin's lymphoma.\\\" it was sponsored by nci's alliance for clinical trials in oncology under study number calgb 50303.\\u00a0 this randomized phase iii trial is studying rituximab when given together with two different combination chemotherapy regimens to compare how well they work in treating patients with diffuse large b-cell lymphoma. select individual patient-level data from this trial can be requested from the nctn\\\/ncorp data archive.trial descriptionthis randomized phase iii trial studies rituximab when given together with two different combination chemotherapy regimens to compare how well they work in treating patients with diffuse large b-cell non-hodgkin's lymphoma. monoclonal antibodies, such as rituximab, may block cancer growth in different ways by targeting certain cells. drugs used in chemotherapy work in different ways to stop the growth of cancer cells, either by killing the cells, by stopping them from dividing, or by stopping them from spreading. giving rituximab together with combination chemotherapy may kill more cancer cells. it is not yet known which combination chemotherapy regimen is more effective when given with rituximab in treating diffuse large b-cell non-hodgkin's lymphoma.alliance\\\/calgb 50303 (nct00118209), an intergroup, phase iii study, compared dose-adjusted etoposide, prednisone, vincristine, cyclophosphamide, doxorubicin, and rituximab (da-epoch-r) with standard rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (r-chop) as frontline therapy for diffuse large b-cell lymphoma.\\u00a0\\u00a0patients received six cycles of da-epoch-r or r-chop. the primary objective was progression-free survival (pfs); secondary clinical objectives included response rate, overall survival (os), and safety.\\u00a0\\u00a0between 2005 and 2013, 524 patients were registered; 491 eligible patients were included in the final analysis. most patients (74%) had stage iii or iv disease; international prognostic index (ipi) risk groups included 26% ipi 0 to 1, 37% ipi 2, 25% ipi 3, and 12% ipi 4 to 5. at a median follow-up of 5 years, pfs was not statistically different between the arms (hazard ratio, 0.93; 95% ci, 0.68 to 1.27;\\u00a0p\\u00a0= .65), with a 2-year pfs rate of 78.9% (95% ci, 73.8% to 84.2%) for da-epoch-r and 75.5% (95% ci, 70.2% to 81.1%) for r-chop. os was not different (hazard ratio, 1.09; 95% ci, 0.75 to 1.59;\\u00a0p\\u00a0= .64), with a 2-year os rate of 86.5% (95% ci, 82.3% to 91%) for da-epoch-r and 85.7% (95% ci, 81.4% to 90.2%) for r-chop. grade 3 and 4 adverse events were more common (p\\u00a0< .001) in the da-epoch-r arm than the r-chop arm, including infection (16.9%\\u00a0v\\u00a010.7%, respectively), febrile neutropenia (35.0%\\u00a0v\\u00a017.7%, respectively), mucositis (8.4%\\u00a0v\\u00a02.1%, respectively), and neuropathy (18.6%\\u00a0v\\u00a03.3%, respectively). five treatment-related deaths (2.1%) occurred in each arm.ct scans of the chest, abdomen and pelvis were utilized for tumor staging for 155 of these patients.\\u00a0 serial fluorodeoxyglucose positron emission tomography (fdg-pet) was conducted at baseline, after 2 cycles of chemotherapy (interim pet [i-pet]), and at end of treatment (eot) to identify biomarkers of response that are predictive of remission and survival.trial outcomes results of the trial have been reported in the following publications:sch\\u00f6der, h., polley, m.-y. c., knopp, m. v., hall, n., kostakoglu, l., zhang, j., higley, h. r., kelloff, g., liu, h., zelenetz, a. d., cheson, b. d., wagner-johnston, n., kahl, b. s., friedberg, j. w., hsi, e. d., leonard, j. p., schwartz, l. h., wilson, w. h., & bartlett, n. l. (2020). prognostic value of interim fdg-pet in diffuse large cell lymphoma: results from the calgb 50303 clinical trial. blood, 135(25), 2224\\u20132234. https:\\\/\\\/doi.org\\\/10.1182\\\/blood.2019003277.\\u00a0pmid: 32232481; pmcid: pmc7316220.bartlett, n. l., wilson, w. h., jung, s.-h., hsi, e. d., maurer, m. j., pederson, l. d., polley, m.-y. c., pitcher, b. n., cheson, b. d., kahl, b. s., friedberg, j. w., staudt, l. m., wagner-johnston, n. d., blum, k. a., abramson, j. s., reddy, n. m., winter, j. n., chang, j. e., gopal, a. k., \\u2026 leonard, j. p. (2019). dose-adjusted epoch-r compared with r-chop as frontline therapy for diffuse large b-cell lymphoma: clinical outcomes of the phase iii intergroup trial alliance\\\/calgb 50303. journal of clinical oncology, 37(21), 1790\\u20131799.\\u00a0doi:\\u00a0https:\\\/\\\/doi.org\\\/10.1200\\\/jco.18.01994.\\u00a0epub 2019 apr 2. pmid: 30939090; pmcid: pmc6774813. lymphoma various ct pt ot clinical nctn\"},{\"id\":41773,\"type\":\"collection\",\"slug\":\"catch\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/catch\\\/\",\"title\":\"CAnine CuTaneous Cancer Histology Dataset\",\"short_title\":\"CATCH\",\"doi\":\"10.7937\\\/TCIA.2M93-FX66\",\"summary\":\"We present a large-scale dataset of 350 histologic samples of seven different canine cutaneous tumors. All samples were obtained through surgical resection due to neoplastic indicators and were selected retrospectively from the biopsy archive of the Institute for Veterinary Pathology of the Freie Universit\\u00e4t Berlin according to sufficient tissue preservation and presence of characteristic histologic features for the corresponding tumor subtypes. Samples were stained with a routine Hematoxylin & Eosin dye and digitized with two Leica linear scanning systems at a resolution of 0.25 um\\\/pixel. Together with the 350 whole slide images, we provide a database consisting of 12,424 polygon annotations for\\u00a0six non-neoplastic tissue classes (epidermis, dermis, subcutis, bone, cartilage, and\\u00a0a joint class of inflammation and necrosis) and\\u00a0seven tumor classes (melanoma, mast cell tumor, squamous cell carcinoma, peripheral nerve sheath tumor, plasmacytoma, trichoblastoma, and histiocytoma).The polygon annotations were generated using the open source software SlideRunner (https:\\\/\\\/github.com\\\/DeepPathology\\\/SlideRunner). Within SlideRunner, users can view whole slide images (WSIs) and zoom through their magnification levels. Using multiple clicks or click-and-drag, the pathologist annotated polygons for 13 classes (epidermis, dermis, subcutis, bone, cartilage, a joint class of inflammation and necrosis, melanoma, mast cell tumor, squamous cell carcinoma, peripheral nerve sheath tumor, plasmacytoma, trichoblastoma, and histiocytoma) on 287 WSIs. The remaining WSIs were annotated by three medical students in their 8th semester supervised by the leading pathologist who later reviewed these annotations for correctness and completeness.\\u00a0Due to the large size of the dataset and the extensive annotations, it provides a good basis for segmentation and classification algorithms based on supervised learning. Previous work\\u00a0[1-4] has shown, that due to various homologies between the species, canine cutaneous tissue can serve as a model for human samples.\\u00a0\\u00a0Prouteau et al. have published an extensive comparison of the two species especially for cutaneous tumors and include homologies between canine and human oncology regarding \\\"clinical and histological appearance, biological behavior, tumor genetics, molecular pathways and targets, and response to therapies\\\" [1]. Ranieri et al. highlight that pet dogs and humans share many environmental risk factors and show the highest risk for cancer development at similar points of time respective to their life spans [2]. Both, Ranieri et al. and Pinho et al. highlight the potential of using insights from experiments on canine samples for developing human cancer treatments [2,3]. From a technical perspective, Aubreville et al. have shown that canine samples can be used to aid human cancer research through the use of transfer learning methods [4].Potential users of the dataset can load the SQLite database into their custom installation of SlideRunner and adapt or extend the database with custom annotations. Furthermore, we converted the annotations to the COCO JSON format, which is commonly used by computer scientists for training neural networks. Its pixel-level annotations can be used for supervised segmentation algorithms as opposed to datasets that only provide clinical data on slide\\u00a0level.ReferencesProuteau, Ana\\u00efs, and Catherine Andr\\u00e9. \\\"Canine melanomas as models for human melanomas: Clinical, histological, and genetic comparison.\\\" Genes 10.7 (2019): 501. https:\\\/\\\/doi.org\\\/10.3390\\\/genes10070501Ranieri, G., et al. \\\"A model of study for human cancer: Spontaneous occurring tumors in dogs. Biological features and translation for new anticancer therapies.\\\" Critical reviews in oncology\\\/hematology 88.1 (2013): 187-197. https:\\\/\\\/doi.org\\\/10.1016\\\/j.critrevonc.2013.03.005Pinho, Salom\\u00e9 S., et al. \\\"Canine tumors: a spontaneous animal model of human carcinogenesis.\\\" Translational Research 159.3 (2012): 165-172. https:\\\/\\\/doi.org\\\/10.1016\\\/j.trsl.2011.11.005Aubreville, Marc, et al. \\\"A completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research.\\\" Scientific data 7.1 (2020): 1-10. https:\\\/\\\/doi.org\\\/10.1038\\\/s41597-020-00756-z\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/catch-sliderunner.png\",\"program\":\"ICDC\",\"cancer_types\":[\"Skin Cancer\"],\"cancer_locations\":[\"Skin\"],\"data_types\":[\"Histopathology\",\"Pathology Detail\",\"Other\"],\"supporting_data\":[\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Canine\"],\"subjects\":282,\"date_updated\":\"2022-01-12\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22CATCH%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc1MCIsInBhc3Njb2RlIjoiMGQxM2Q3Njk1MTU1MWMzNTYwMjk5NGJiNGQ0OWE2YjBkNzlmZTA5MiIsInBhY2thZ2VfaWQiOiI3NTAiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"canine cutaneous cancer histology dataset catch we present a large-scale dataset of 350 histologic samples of seven different canine cutaneous tumors. all samples were obtained through surgical resection due to neoplastic indicators and were selected retrospectively from the biopsy archive of the institute for veterinary pathology of the freie universit\\u00e4t berlin according to sufficient tissue preservation and presence of characteristic histologic features for the corresponding tumor subtypes. samples were stained with a routine hematoxylin & eosin dye and digitized with two leica linear scanning systems at a resolution of 0.25 um\\\/pixel. together with the 350 whole slide images, we provide a database consisting of 12,424 polygon annotations for\\u00a0six non-neoplastic tissue classes (epidermis, dermis, subcutis, bone, cartilage, and\\u00a0a joint class of inflammation and necrosis) and\\u00a0seven tumor classes (melanoma, mast cell tumor, squamous cell carcinoma, peripheral nerve sheath tumor, plasmacytoma, trichoblastoma, and histiocytoma).the polygon annotations were generated using the open source software sliderunner (https:\\\/\\\/github.com\\\/deeppathology\\\/sliderunner). within sliderunner, users can view whole slide images (wsis) and zoom through their magnification levels. using multiple clicks or click-and-drag, the pathologist annotated polygons for 13 classes (epidermis, dermis, subcutis, bone, cartilage, a joint class of inflammation and necrosis, melanoma, mast cell tumor, squamous cell carcinoma, peripheral nerve sheath tumor, plasmacytoma, trichoblastoma, and histiocytoma) on 287 wsis. the remaining wsis were annotated by three medical students in their 8th semester supervised by the leading pathologist who later reviewed these annotations for correctness and completeness.\\u00a0due to the large size of the dataset and the extensive annotations, it provides a good basis for segmentation and classification algorithms based on supervised learning. previous work\\u00a0[1-4] has shown, that due to various homologies between the species, canine cutaneous tissue can serve as a model for human samples.\\u00a0\\u00a0prouteau et al. have published an extensive comparison of the two species especially for cutaneous tumors and include homologies between canine and human oncology regarding \\\"clinical and histological appearance, biological behavior, tumor genetics, molecular pathways and targets, and response to therapies\\\" [1]. ranieri et al. highlight that pet dogs and humans share many environmental risk factors and show the highest risk for cancer development at similar points of time respective to their life spans [2]. both, ranieri et al. and pinho et al. highlight the potential of using insights from experiments on canine samples for developing human cancer treatments [2,3]. from a technical perspective, aubreville et al. have shown that canine samples can be used to aid human cancer research through the use of transfer learning methods [4].potential users of the dataset can load the sqlite database into their custom installation of sliderunner and adapt or extend the database with custom annotations. furthermore, we converted the annotations to the coco json format, which is commonly used by computer scientists for training neural networks. its pixel-level annotations can be used for supervised segmentation algorithms as opposed to datasets that only provide clinical data on slide\\u00a0level.referencesprouteau, ana\\u00efs, and catherine andr\\u00e9. \\\"canine melanomas as models for human melanomas: clinical, histological, and genetic comparison.\\\" genes 10.7 (2019): 501. https:\\\/\\\/doi.org\\\/10.3390\\\/genes10070501ranieri, g., et al. \\\"a model of study for human cancer: spontaneous occurring tumors in dogs. biological features and translation for new anticancer therapies.\\\" critical reviews in oncology\\\/hematology 88.1 (2013): 187-197. https:\\\/\\\/doi.org\\\/10.1016\\\/j.critrevonc.2013.03.005pinho, salom\\u00e9 s., et al. \\\"canine tumors: a spontaneous animal model of human carcinogenesis.\\\" translational research 159.3 (2012): 165-172. https:\\\/\\\/doi.org\\\/10.1016\\\/j.trsl.2011.11.005aubreville, marc, et al. \\\"a completely annotated whole slide image dataset of canine breast cancer to aid human breast cancer research.\\\" scientific data 7.1 (2020): 1-10. https:\\\/\\\/doi.org\\\/10.1038\\\/s41597-020-00756-z skin cancer skin histopathology pathology detail other image analyses software\\\/source code icdc\"},{\"id\":42097,\"type\":\"collection\",\"slug\":\"cbis-ddsm\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cbis-ddsm\\\/\",\"title\":\"Curated Breast Imaging Subset of Digital Database for Screening Mammography\",\"short_title\":\"CBIS-DDSM\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.7O02S9CY\",\"summary\":\"This CBIS-DDSM (Curated Breast Imaging Subset of DDSM) is an updated and standardized version of the\\u00a0Digital Database for Screening Mammography (DDSM). \\u00a0The DDSM is a database of 2,620 scanned film mammography studies. It contains normal, benign, and malignant cases with verified pathology information. The scale of the database along with ground truth validation makes the DDSM a useful tool in the development and testing of decision support systems. The CBIS-DDSM collection includes a subset of the DDSM data selected and curated by a trained mammographer.\\u00a0 The images have been decompressed and converted to DICOM format.\\u00a0 Updated ROI segmentation and bounding boxes, and pathologic diagnosis for training data are also included.\\u00a0\\u00a0A manuscript describing how to use this dataset in detail is available at https:\\\/\\\/www.nature.com\\\/articles\\\/sdata2017177.Published research results from work in developing decision support systems in mammography are difficult to replicate due to the lack of a standard evaluation data set; most computer-aided diagnosis (CADx) and detection (CADe) algorithms for breast cancer in mammography are evaluated on private data sets or on unspecified subsets of public databases. Few well-curated public datasets have been provided for the mammography community. These include the DDSM, the Mammographic Imaging Analysis Society (MIAS) database, and the Image Retrieval in Medical Applications (IRMA) project. Although these public data sets are useful, they are limited in terms of data set size and accessibility.For example, most researchers using the DDSM do not leverage all its images for a variety of historical reasons. When the database was released in 1997, computational resources to process hundreds or thousands of images were not widely available. Additionally, the DDSM images are saved in non-standard compression files that require the use of decompression code that has not been updated or maintained for modern computers. Finally, the ROI annotations for the abnormalities in the DDSM were provided to indicate a general position of lesions, but not a precise segmentation for them. Therefore, many researchers must implement segmentation algorithms for accurate feature extraction. This causes an inability to directly compare the performance of methods or to replicate prior results. The CBIS-DDSM collection addresses that challenge by publicly releasing an curated and standardized version of the DDSM for evaluation of future CADx and CADe systems (sometimes referred to generally as CAD) research in mammography.Please note that the image data for this collection is structured such that each participant has multiple patient IDs.\\u00a0 For example, participant 00038 has 10 separate patient IDs which provide information about the scans within the IDs (e.g. Calc-Test_P_00038_LEFT_CC,\\u00a0Calc-Test_P_00038_RIGHT_CC_1).\\u00a0 This makes it appear as though there are 6,671 patients according to the DICOM metadata, but there are only\\u00a01,566 actual participants in the cohort.For scientific and other inquiries about this dataset, please contact TCIA's Helpdesk.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\",\"Non-Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MG\",\"Classification\",\"Radiomic Feature\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":1566,\"date_updated\":\"2017-09-14\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=CBIS-DDSM\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CBIS-DDSM-All-doiJNLP-zzWs5zfZ.tcia\",\"blob\":\"curated breast imaging subset of digital database for screening mammography cbis-ddsm this cbis-ddsm (curated breast imaging subset of ddsm) is an updated and standardized version of the\\u00a0digital database for screening mammography (ddsm). \\u00a0the ddsm is a database of 2,620 scanned film mammography studies. it contains normal, benign, and malignant cases with verified pathology information. the scale of the database along with ground truth validation makes the ddsm a useful tool in the development and testing of decision support systems. the cbis-ddsm collection includes a subset of the ddsm data selected and curated by a trained mammographer.\\u00a0 the images have been decompressed and converted to dicom format.\\u00a0 updated roi segmentation and bounding boxes, and pathologic diagnosis for training data are also included.\\u00a0\\u00a0a manuscript describing how to use this dataset in detail is available at https:\\\/\\\/www.nature.com\\\/articles\\\/sdata2017177.published research results from work in developing decision support systems in mammography are difficult to replicate due to the lack of a standard evaluation data set; most computer-aided diagnosis (cadx) and detection (cade) algorithms for breast cancer in mammography are evaluated on private data sets or on unspecified subsets of public databases. few well-curated public datasets have been provided for the mammography community. these include the ddsm, the mammographic imaging analysis society (mias) database, and the image retrieval in medical applications (irma) project. although these public data sets are useful, they are limited in terms of data set size and accessibility.for example, most researchers using the ddsm do not leverage all its images for a variety of historical reasons. when the database was released in 1997, computational resources to process hundreds or thousands of images were not widely available. additionally, the ddsm images are saved in non-standard compression files that require the use of decompression code that has not been updated or maintained for modern computers. finally, the roi annotations for the abnormalities in the ddsm were provided to indicate a general position of lesions, but not a precise segmentation for them. therefore, many researchers must implement segmentation algorithms for accurate feature extraction. this causes an inability to directly compare the performance of methods or to replicate prior results. the cbis-ddsm collection addresses that challenge by publicly releasing an curated and standardized version of the ddsm for evaluation of future cadx and cade systems (sometimes referred to generally as cad) research in mammography.please note that the image data for this collection is structured such that each participant has multiple patient ids.\\u00a0 for example, participant 00038 has 10 separate patient ids which provide information about the scans within the ids (e.g. calc-test_p_00038_left_cc,\\u00a0calc-test_p_00038_right_cc_1).\\u00a0 this makes it appear as though there are 6,671 patients according to the dicom metadata, but there are only\\u00a01,566 actual participants in the cohort.for scientific and other inquiries about this dataset, please contact tcia's helpdesk. breast cancer non-cancer breast mg classification radiomic feature image analyses community\"},{\"id\":42009,\"type\":\"collection\",\"slug\":\"cc-radiomics-phantom\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cc-radiomics-phantom\\\/\",\"title\":\"Credence Cartridge Radiomics Phantom CT Scans\",\"short_title\":\"CC-Radiomics-Phantom\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2017.zuzrml5b\",\"summary\":\"This collection consists of 17 CT scans of the Credence Cartridge Radiomics (CCR) phantom, which was designed for use in studies of texture feature robustness. The scans were acquired at four medical centers using each center\\u2019s chest protocol and were taken using GE (7 scans), Philips (5 scans), Siemens (2 scans), and Toshiba (3 scans) scanners. The CCR phantom has 10 cartridges, each with a unique texture, Fig 1. The first four cartridges are 3D printed ABS plastic with 20%, 30%, 40%, and 50% honeycomb fill, and they provide regular, periodic textures. The next three cartridges provide natural textures: sycamore wood, cork, and extra dense cork. A cartridges of shredded rubber particles provides textures similar to those of non-small cell lung cancer. The ninth cartridge is solid, homogenous acrylic and provides a minimal texture control. Finally, the tenth cartridge is 3D printed plaster with the highest electron density (400 \\u2013 600 HU) and is intended to be more similar to bone.In addition to the DICOM images for the 17 scans, this collection also contains two sets of contours as DICOM RT structure files. The first set provides 8x8x2 cm3 contours for each cartridge in each scan. The second set provides 16 adjacent 2x2x2 cm3 contours for each cartridge in each scan. Researchers studying radiomics will be able to evaluate features for robustness across a variety of scanners. Features can be calculated using the researchers own software or third party software such as IBEX (imaging biomarker explorer).Related publications:Mackin, D., Fave, X., Zhang, L., Fried, D., Yang, J., Taylor, B., Rodriguez-Rivera, E., Dodge, C., Jones, A. K., & Court, L. (2015). Measuring Computed Tomography Scanner Variability of Radiomics Features. In Investigative Radiology (Vol. 50, Issue 11, pp. 757\\u2013765). Ovid Technologies (Wolters Kluwer Health). https:\\\/\\\/doi.org\\\/10.1097\\\/rli.0000000000000180\\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0\\u00a0The following paper was generated on different imaging modalities but the same phantom, this is a related but independent paper with a different set of authors: Fave, X., Mackin, D., Yang, J., Zhang, J., Fried, D., Balter, P., Followill, D., Gomez, D., Kyle Jones, A., Stingo, F., Fontenot, J., & Court, L. (2015). Can radiomics features be reproducibly measured from CBCT images for patients with non\\u2010small cell lung cancer? In Medical Physics (Vol. 42, Issue 12, pp. 6784\\u20136797). Wiley. https:\\\/\\\/doi.org\\\/10.1118\\\/1.4934826\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/labeled_ccr.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Phantom\"],\"cancer_locations\":[\"Lung Phantom\"],\"data_types\":[\"CT\",\"RTSTRUCT\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":17,\"date_updated\":\"2017-07-28\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=CC-Radiomics-phantom\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CC-Radiomics-Phantom-7-28-2017-doiJNLP-2vcxNoRY.tcia\",\"blob\":\"credence cartridge radiomics phantom ct scans cc-radiomics-phantom this collection consists of 17 ct scans of the credence cartridge radiomics (ccr) phantom, which was designed for use in studies of texture feature robustness. the scans were acquired at four medical centers using each center\\u2019s chest protocol and were taken using ge (7 scans), philips (5 scans), siemens (2 scans), and toshiba (3 scans) scanners. the ccr phantom has 10 cartridges, each with a unique texture, fig 1. the first four cartridges are 3d printed abs plastic with 20%, 30%, 40%, and 50% honeycomb fill, and they provide regular, periodic textures. the next three cartridges provide natural textures: sycamore wood, cork, and extra dense cork. a cartridges of shredded rubber particles provides textures similar to those of non-small cell lung cancer. the ninth cartridge is solid, homogenous acrylic and provides a minimal texture control. finally, the tenth cartridge is 3d printed plaster with the highest electron density (400 \\u2013 600 hu) and is intended to be more similar to bone.in addition to the dicom images for the 17 scans, this collection also contains two sets of contours as dicom rt structure files. the first set provides 8x8x2 cm3 contours for each cartridge in each scan. the second set provides 16 adjacent 2x2x2 cm3 contours for each cartridge in each scan. researchers studying radiomics will be able to evaluate features for robustness across a variety of scanners. features can be calculated using the researchers own software or third party software such as ibex (imaging biomarker explorer).related publications:mackin, d., fave, x., zhang, l., fried, d., yang, j., taylor, b., rodriguez-rivera, e., dodge, c., jones, a. k., & court, l. (2015). measuring computed tomography scanner variability of radiomics features. in investigative radiology (vol. 50, issue 11, pp. 757\\u2013765). ovid technologies (wolters kluwer health). https:\\\/\\\/doi.org\\\/10.1097\\\/rli.0000000000000180\\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0\\u00a0the following paper was generated on different imaging modalities but the same phantom, this is a related but independent paper with a different set of authors: fave, x., mackin, d., yang, j., zhang, j., fried, d., balter, p., followill, d., gomez, d., kyle jones, a., stingo, f., fontenot, j., & court, l. (2015). can radiomics features be reproducibly measured from cbct images for patients with non\\u2010small cell lung cancer? in medical physics (vol. 42, issue 12, pp. 6784\\u20136797). wiley. https:\\\/\\\/doi.org\\\/10.1118\\\/1.4934826\\u00a0 phantom lung phantom ct rtstruct image analyses community\"},{\"id\":42017,\"type\":\"collection\",\"slug\":\"cc-radiomics-phantom-2\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cc-radiomics-phantom-2\\\/\",\"title\":\"Credence Cartridge Radiomics Phantom CT Scans with Controlled Scanning Approach\",\"short_title\":\"CC-Radiomics-Phantom-2\",\"doi\":\"10.7937\\\/TCIA.2019.4l24tz5g\",\"summary\":\"This collection consists of 251 CT scans of Credence Cartridge Radiomic (CCR) phantom. This texture phantom was developed to investigate the feature robustness in the emerging field of radiomics. This phantom dataset was acquired on 4-8 CT scanners using a set of imaging parameters (e.g., reconstruction Field of View, Slice thickness, reconstruction kernels, mAs, and Pitch). A controlled scanning approach was employed to assess the variability in radiomic features due to each imaging parameter. This dataset will be useful to radiomic research community to identify a subset of robust radiomic features and for establishing the ground truths for future clinical investigations.This Phantom dataset can be used for Feature variability assessment due to CT imaging parameters. These phantom scans can be used to identify a subset of robust radiomic features for future clinical investigations. Using this dataset, the numerical values of radiomic features can be cross-validated by other research groups using their own feature extraction tools.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Phantom\"],\"cancer_locations\":[\"Phantom\"],\"data_types\":[\"CT\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":251,\"date_updated\":\"2019-02-27\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=CC-Radiomics-Phantom-2\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CC-Radiomics-Phantom-2-NBIA-manifest.tcia\",\"blob\":\"credence cartridge radiomics phantom ct scans with controlled scanning approach cc-radiomics-phantom-2 this collection consists of 251 ct scans of credence cartridge radiomic (ccr) phantom. this texture phantom was developed to investigate the feature robustness in the emerging field of radiomics. this phantom dataset was acquired on 4-8 ct scanners using a set of imaging parameters (e.g., reconstruction field of view, slice thickness, reconstruction kernels, mas, and pitch). a controlled scanning approach was employed to assess the variability in radiomic features due to each imaging parameter. this dataset will be useful to radiomic research community to identify a subset of robust radiomic features and for establishing the ground truths for future clinical investigations.this phantom dataset can be used for feature variability assessment due to ct imaging parameters. these phantom scans can be used to identify a subset of robust radiomic features for future clinical investigations. using this dataset, the numerical values of radiomic features can be cross-validated by other research groups using their own feature extraction tools. phantom phantom ct community\"},{\"id\":42059,\"type\":\"collection\",\"slug\":\"cc-radiomics-phantom-3\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cc-radiomics-phantom-3\\\/\",\"title\":\"CT Phantom Scans for Head, Chest, and Controlled Protocols on 100 Scanners\",\"short_title\":\"CC-Radiomics-Phantom-3\",\"doi\":\"10.7937\\\/tcia.2019.j71i4fah\",\"summary\":\"There currently is a dearth of phantom scans on large samples.\\u00a0This data collection contains one physical phantom, imaged across three protocols, on 100 scanners. This provides population data that can be used to quantify inter-scanner variability. This data can be used to determine how robust specific radiomics or other quantitative imaging signatures are.ProtocolComputed tomography scans were acquired on 100 scanners at 35 clinics: 51 GE scanners, 20 Philips scanners, 11 Toshiba scanners, and 1 Philips and Neusoft Medical System scanner. The commonly used chest and head protocols of the local clinic were acquired without changing the protocol parameters. Additionally, a controlled protocol was acquired that was designed to minimize radiomics feature differences between manufacturers. The settings for the controlled protocol were: tube voltage, 120 kV(p); tube current, 200 mA\\u2219s; helical scan type; spiral pitch factor, 1.0; 50-cm display field of view; and image thickness, 3 mm (except for GE scanners, which used an image thickness of 2.5 mm). The convolution kernel was standard for GE; C for Philips; B31f, B31s for Siemens; and FC08 for Toshiba. However, the kernel used for the Toshiba scans switched from FC18 (six scanners) to FC08 (five scanners) halfway through owing to a study by Mackin et al. (Medical Physics, 2018) that found the FC08 kernel to match the GE standard kernel best. K-means clustering showed that the scanners did not cluster by kernel type, thus all Toshiba scanners were included.There were 94 scanners that had a controlled protocol scan that could be used: 48 GE, 18 Philips, 17 Siemens, and 11 Toshiba scanners; 93 scanners that had a local chest protocol scan that could be used: 47 GE, 19 Philips, 17 Siemens, and 10 Toshiba scanners; and 88 scanners that had a local head protocol scan that could be used: 46 GE, 18 Philips, 14 Siemens, and 10 Toshiba scanners. The various reasons that scans could not be used were as follows: the field of view did not encompass all the cartridges, the scan extent did not cover the length of the phantom, and the scan was acquired with variable image thickness.\\u00a0The featured image shows\\u00a0Axial views from a computed tomography scan of the radiomics phantom used. The cartridges are (a) 50% acrylonitrile butadiene styrene (ABS), 25% acrylic beads, and 25% polyvinyl chloride (PVC) pieces (percentages are by weight), (b) 50% ABS and 50% PVC pieces, (c) 50% ABS and 50% acrylic beads, (d) hemp seeds in polyurethane, (e) shredded rubber, and (f) dense cork. The high-density polystyrene buildup is seen outside the cartridges with dimensions of 28cm\\u00d721cm\\u00d722cm. The cartridges had a diameter of 10.8 cm. Window width: 1600, window level: -300.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/41598_2018_31509_Fig1.png\",\"program\":\"Community\",\"cancer_types\":[\"Phantom\"],\"cancer_locations\":[\"Head\",\"Chest\",\"Phantom\"],\"data_types\":[\"RTSTRUCT\",\"CT\",\"Protocol\",\"Other\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Phantom\"],\"subjects\":95,\"date_updated\":\"2019-05-08\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=CC-Radiomics-Phantom-3\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CC-Radiomics-Phantom-3-NBIA-manifest.tcia\",\"blob\":\"ct phantom scans for head, chest, and controlled protocols on 100 scanners cc-radiomics-phantom-3 there currently is a dearth of phantom scans on large samples.\\u00a0this data collection contains one physical phantom, imaged across three protocols, on 100 scanners. this provides population data that can be used to quantify inter-scanner variability. this data can be used to determine how robust specific radiomics or other quantitative imaging signatures are.protocolcomputed tomography scans were acquired on 100 scanners at 35 clinics: 51 ge scanners, 20 philips scanners, 11 toshiba scanners, and 1 philips and neusoft medical system scanner. the commonly used chest and head protocols of the local clinic were acquired without changing the protocol parameters. additionally, a controlled protocol was acquired that was designed to minimize radiomics feature differences between manufacturers. the settings for the controlled protocol were: tube voltage, 120 kv(p); tube current, 200 ma\\u2219s; helical scan type; spiral pitch factor, 1.0; 50-cm display field of view; and image thickness, 3 mm (except for ge scanners, which used an image thickness of 2.5 mm). the convolution kernel was standard for ge; c for philips; b31f, b31s for siemens; and fc08 for toshiba. however, the kernel used for the toshiba scans switched from fc18 (six scanners) to fc08 (five scanners) halfway through owing to a study by mackin et al. (medical physics, 2018) that found the fc08 kernel to match the ge standard kernel best. k-means clustering showed that the scanners did not cluster by kernel type, thus all toshiba scanners were included.there were 94 scanners that had a controlled protocol scan that could be used: 48 ge, 18 philips, 17 siemens, and 11 toshiba scanners; 93 scanners that had a local chest protocol scan that could be used: 47 ge, 19 philips, 17 siemens, and 10 toshiba scanners; and 88 scanners that had a local head protocol scan that could be used: 46 ge, 18 philips, 14 siemens, and 10 toshiba scanners. the various reasons that scans could not be used were as follows: the field of view did not encompass all the cartridges, the scan extent did not cover the length of the phantom, and the scan was acquired with variable image thickness.\\u00a0the featured image shows\\u00a0axial views from a computed tomography scan of the radiomics phantom used. the cartridges are (a) 50% acrylonitrile butadiene styrene (abs), 25% acrylic beads, and 25% polyvinyl chloride (pvc) pieces (percentages are by weight), (b) 50% abs and 50% pvc pieces, (c) 50% abs and 50% acrylic beads, (d) hemp seeds in polyurethane, (e) shredded rubber, and (f) dense cork. the high-density polystyrene buildup is seen outside the cartridges with dimensions of 28cm\\u00d721cm\\u00d722cm. the cartridges had a diameter of 10.8 cm. window width: 1600, window level: -300. phantom head chest phantom rtstruct ct protocol other image analyses community\"},{\"id\":41813,\"type\":\"collection\",\"slug\":\"cc-tumor-heterogeneity\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cc-tumor-heterogeneity\\\/\",\"title\":\"Cervical Cancer \\u2013 Tumor Heterogeneity: Serial Functional and Molecular Imaging Across the Radiation Therapy Course in Advanced Cervical Cancer\",\"short_title\":\"CC-Tumor-Heterogeneity\",\"doi\":\"10.7937\\\/ERZ5-QZ59\",\"summary\":\"Background: The functional and biological properties of the tumor microenvironment are fundamentally important determinants of tumor response and therapy outcome in cancer. \\u00a0Oxygenation status and vascularity are known to influence radiation response, and molecular energy metabolism and proliferation, modulate risk of disease recurrence and metastatic progression. \\u00a0However, with the current standard clinical diagnostic approaches, such as biopsy or anatomic\\\/morphologic tumor imaging, assessment of the known intra-tumoral heterogeneity of functional and biological tumor properties is limited.\\u00a0 Specifically, the functional characteristics within the microenvironment throughout the entire tumor have been challenging to assess spatially for tumor heterogeneity and temporally for clinical correlation before and during treatment. \\u00a0While histologic tissue sampling is widely used clinically, sampling of the entire tumor with extensive biopsies, and biopsies at various time points during therapy for intra-treatment assessment are impractical and generally pose unacceptable clinical risk.\\u00a0\\nFunctional\\\/molecular imaging offers the opportunity to assess biological heterogeneity across the entire tumor volume (spatially) and longitudinally across the treatment course (temporally).\\u00a0 In advanced cervical cancer, the tumor gradually undergoes radiation\\\/chemotherapy induced functional and biological changes within its heterogeneous volume that can be assessed by sequential imaging before and during treatment. \\u00a0\\nAdvanced cervical cancer is an ideal disease to study \\u2013 in clinical patients \\u2013 the vascular, cellular and molecular tumor properties that can provide essential information to monitor therapeutic responsiveness, facilitate treatment planning and may provide early prediction of ultimate success or failure of an ongoing treatment.\\nAdvanced cervical cancer is treated with cytotoxic therapy: radiation and concurrent chemotherapy. \\u00a0It is a highly prevalent disease globally, and treatment failure is common.\\u00a0 The propensity of cervical cancer for hypoxia and poor vasculature within the often bulky heterogenous tumor volume is well-recognized. \\u00a0Because advanced cervical cancer is not surgically resected, functional\\\/molecular imaging provides unique opportunities for non-invasive assessment throughout the treatment course.\\nPurpose: \\u00a0The understanding of biological processes occurring within the tumor environment during the ongoing radiation therapy course remains a major knowledge gap in radiation oncology for cervical cancer and other malignancies.\\u00a0 The CCTH collection seeks to help fill this gap by providing functional\\\/molecular tumor imaging data sets \\u2013 spatially to assess tumor heterogeneity and temporally across the radiation therapy course in advanced cervical cancer patients.\\nCCTH Collection:\\u00a0 The CCTH collection shares functional\\\/molecular imaging data sets (performed on an NCI funded R01 award) that were prospectively acquired in clinical patients with advanced stage IB2 \\u2013 IVA cervical cancer, who were treated with standard combined radiation therapy with concurrent Cisplatin-based chemotherapy. \\u00a0Both functional MRI, consisting of T1- and T2-weighted, dynamic contrast enhanced (DCE), diffusion-weighted (DWI) and post-contrast MRI, and 18FDG PET\\\/CT were obtained in parallel and prospectively timed with the radiation therapy course.\\u00a0 Imaging was performed, according to a standardized multi-institutional protocol, at three time points\\\/radiation dose levels: before treatment start (dose 0), early during the treatment course (2-2.5 weeks after treatment start\\\/dose 20-25 Gy) and at mid-treatment (4-5 weeks after treatment start\\\/dose 45-50 Gy). \\u00a0Contours (regions of interest) of the tumor volumes, as defined by the gold standard of T2-weighetd MRI and coregistration with PET\\\/CT, are included in the CCTH for each case and each imaging time point.\\u00a0The resulting prospective multi-parametric imaging data sets, obtained from the various imaging modalities at different treatment time points, allow detailed study of the evolution of functional\\\/molecular tumor properties before and during radiation\\\/chemotherapy course. \\u00a0These include voxel-wise heterogeneity assessment of the structural properties (tumor volume on T2-weighted MRI) and, in parallel, the functional characteristics on DCE, DWI MRI and [18F]FDG PET.\\u00a0These intra-treatment functional phenomena from various functional imaging modalities may have the potential for clinical translation into important actionable early imaging prognosticators and predictors for long-term treatment outcome.\\u00a0 For example, vascular tumor properties (as reflected by DCE MRI) are highly significant for tumor oxygenation, which in turn profoundly influences radiation response. For example, our early investigations suggest that dynamic contrast enhancement (indicative of tumor microvasculature) improves early in the treatment course and heterogeneity decreases, particularly in responders, while FDG PET heterogeneity tends to show reduced metabolic activity, which commonly becomes evident later in the radiation treatment course1. \\u00a0Our prior data and results from a prior NCI\\\/NIH award that laid the foundation for this work 2-10, have also suggested that functional tumor properties early during treatment (2-4 weeks into treatment) have significant predictive value for ultimate tumor control and survival in cervical cancer 2- 4, 6.\\u00a0 If unfavorable tumor properties, which correlate with poor treatment outcome, can be identified in patients early during treatment 4, 6, the therapy regimen may be individually adapted accordingly to improve the outcome.\\u00a0\\nTranslation to clinical practice: The complex data sets, from multi-modality and multi-parametric imaging studies at various treatment time points, were designed to be shared with the scientific community with interest and related specialties to advance the functional imaging-based tumor heterogeneity assessment in cervical cancer patients. \\u00a0The current data set allows further extraction of radiomics data that may help optimize potential and efficacy of functional imaging that may serve to improve the management of the patients with advanced cervical cancer. \\u00a0\\nThe ultimate goal is to facilitate the clinical translation that is readily available to community settings in the U.S. and abroad. \\u00a0Therefore, the imaging sequences used in this research are widely available and applicable in community settings.\\u00a0 This is important because advanced cervical cancer is largely treated in the community settings and is highly prevalent cancer worldwide, particularly among women in underserved and economically disadvantaged regions.\\u00a0\\nFuture outlook: \\u00a0We thank the National Cancer Institute and National Institute of Health for their support of this imaging research; and the members of CIP and TCIA for their deep expertise and their help in establishing the CCTH collection.\\u00a0 While our initial studies have established important principles on the use of functional and molecular imaging in cervical cancer, it has been our intent and hope when donating this data, that the data sets of this collection may serve as a resource to investigators into the future as image analysis and radiomics techniques will continue to advance, well beyond the time course of our own studies. Because cervical cancer remains a common cancer in the women, particularly in low- and middle-income countries worldwide, future studies will need to address the feasibility and accessibility of imaging to these patients, such as affordable cost, availability of imaging modalities involved, technical processing requirements (radiomic feature extraction, data reduction and statistical analysis\\\/learning models) and education to investigators and clinicians. \\nReferences: Please see the Citations section.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_CCTH_Image_v2.png\",\"program\":\"Community\",\"cancer_types\":[\"Cervical Cancer\"],\"cancer_locations\":[\"Cervix\"],\"data_types\":[\"MR\",\"RTSTRUCT\",\"REG\",\"PT\",\"CT\",\"Follow-Up\",\"Classification\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":23,\"date_updated\":\"2023-02-23\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=CC-Tumor-Heterogeneity\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CC-Tumor-Heterogeneity-manifest.tcia\",\"blob\":\"cervical cancer \\u2013 tumor heterogeneity: serial functional and molecular imaging across the radiation therapy course in advanced cervical cancer cc-tumor-heterogeneity background: the functional and biological properties of the tumor microenvironment are fundamentally important determinants of tumor response and therapy outcome in cancer. \\u00a0oxygenation status and vascularity are known to influence radiation response, and molecular energy metabolism and proliferation, modulate risk of disease recurrence and metastatic progression. \\u00a0however, with the current standard clinical diagnostic approaches, such as biopsy or anatomic\\\/morphologic tumor imaging, assessment of the known intra-tumoral heterogeneity of functional and biological tumor properties is limited.\\u00a0 specifically, the functional characteristics within the microenvironment throughout the entire tumor have been challenging to assess spatially for tumor heterogeneity and temporally for clinical correlation before and during treatment. \\u00a0while histologic tissue sampling is widely used clinically, sampling of the entire tumor with extensive biopsies, and biopsies at various time points during therapy for intra-treatment assessment are impractical and generally pose unacceptable clinical risk.\\u00a0\\nfunctional\\\/molecular imaging offers the opportunity to assess biological heterogeneity across the entire tumor volume (spatially) and longitudinally across the treatment course (temporally).\\u00a0 in advanced cervical cancer, the tumor gradually undergoes radiation\\\/chemotherapy induced functional and biological changes within its heterogeneous volume that can be assessed by sequential imaging before and during treatment. \\u00a0\\nadvanced cervical cancer is an ideal disease to study \\u2013 in clinical patients \\u2013 the vascular, cellular and molecular tumor properties that can provide essential information to monitor therapeutic responsiveness, facilitate treatment planning and may provide early prediction of ultimate success or failure of an ongoing treatment.\\nadvanced cervical cancer is treated with cytotoxic therapy: radiation and concurrent chemotherapy. \\u00a0it is a highly prevalent disease globally, and treatment failure is common.\\u00a0 the propensity of cervical cancer for hypoxia and poor vasculature within the often bulky heterogenous tumor volume is well-recognized. \\u00a0because advanced cervical cancer is not surgically resected, functional\\\/molecular imaging provides unique opportunities for non-invasive assessment throughout the treatment course.\\npurpose: \\u00a0the understanding of biological processes occurring within the tumor environment during the ongoing radiation therapy course remains a major knowledge gap in radiation oncology for cervical cancer and other malignancies.\\u00a0 the ccth collection seeks to help fill this gap by providing functional\\\/molecular tumor imaging data sets \\u2013 spatially to assess tumor heterogeneity and temporally across the radiation therapy course in advanced cervical cancer patients.\\nccth collection:\\u00a0 the ccth collection shares functional\\\/molecular imaging data sets (performed on an nci funded r01 award) that were prospectively acquired in clinical patients with advanced stage ib2 \\u2013 iva cervical cancer, who were treated with standard combined radiation therapy with concurrent cisplatin-based chemotherapy. \\u00a0both functional mri, consisting of t1- and t2-weighted, dynamic contrast enhanced (dce), diffusion-weighted (dwi) and post-contrast mri, and 18fdg pet\\\/ct were obtained in parallel and prospectively timed with the radiation therapy course.\\u00a0 imaging was performed, according to a standardized multi-institutional protocol, at three time points\\\/radiation dose levels: before treatment start (dose 0), early during the treatment course (2-2.5 weeks after treatment start\\\/dose 20-25 gy) and at mid-treatment (4-5 weeks after treatment start\\\/dose 45-50 gy). \\u00a0contours (regions of interest) of the tumor volumes, as defined by the gold standard of t2-weighetd mri and coregistration with pet\\\/ct, are included in the ccth for each case and each imaging time point.\\u00a0the resulting prospective multi-parametric imaging data sets, obtained from the various imaging modalities at different treatment time points, allow detailed study of the evolution of functional\\\/molecular tumor properties before and during radiation\\\/chemotherapy course. \\u00a0these include voxel-wise heterogeneity assessment of the structural properties (tumor volume on t2-weighted mri) and, in parallel, the functional characteristics on dce, dwi mri and [18f]fdg pet.\\u00a0these intra-treatment functional phenomena from various functional imaging modalities may have the potential for clinical translation into important actionable early imaging prognosticators and predictors for long-term treatment outcome.\\u00a0 for example, vascular tumor properties (as reflected by dce mri) are highly significant for tumor oxygenation, which in turn profoundly influences radiation response. for example, our early investigations suggest that dynamic contrast enhancement (indicative of tumor microvasculature) improves early in the treatment course and heterogeneity decreases, particularly in responders, while fdg pet heterogeneity tends to show reduced metabolic activity, which commonly becomes evident later in the radiation treatment course1. \\u00a0our prior data and results from a prior nci\\\/nih award that laid the foundation for this work 2-10, have also suggested that functional tumor properties early during treatment (2-4 weeks into treatment) have significant predictive value for ultimate tumor control and survival in cervical cancer 2- 4, 6.\\u00a0 if unfavorable tumor properties, which correlate with poor treatment outcome, can be identified in patients early during treatment 4, 6, the therapy regimen may be individually adapted accordingly to improve the outcome.\\u00a0\\ntranslation to clinical practice: the complex data sets, from multi-modality and multi-parametric imaging studies at various treatment time points, were designed to be shared with the scientific community with interest and related specialties to advance the functional imaging-based tumor heterogeneity assessment in cervical cancer patients. \\u00a0the current data set allows further extraction of radiomics data that may help optimize potential and efficacy of functional imaging that may serve to improve the management of the patients with advanced cervical cancer. \\u00a0\\nthe ultimate goal is to facilitate the clinical translation that is readily available to community settings in the u.s. and abroad. \\u00a0therefore, the imaging sequences used in this research are widely available and applicable in community settings.\\u00a0 this is important because advanced cervical cancer is largely treated in the community settings and is highly prevalent cancer worldwide, particularly among women in underserved and economically disadvantaged regions.\\u00a0\\nfuture outlook: \\u00a0we thank the national cancer institute and national institute of health for their support of this imaging research; and the members of cip and tcia for their deep expertise and their help in establishing the ccth collection.\\u00a0 while our initial studies have established important principles on the use of functional and molecular imaging in cervical cancer, it has been our intent and hope when donating this data, that the data sets of this collection may serve as a resource to investigators into the future as image analysis and radiomics techniques will continue to advance, well beyond the time course of our own studies. because cervical cancer remains a common cancer in the women, particularly in low- and middle-income countries worldwide, future studies will need to address the feasibility and accessibility of imaging to these patients, such as affordable cost, availability of imaging modalities involved, technical processing requirements (radiomic feature extraction, data reduction and statistical analysis\\\/learning models) and education to investigators and clinicians. \\nreferences: please see the citations section. cervical cancer cervix mr rtstruct reg pt ct follow-up classification clinical community\"},{\"id\":41797,\"type\":\"collection\",\"slug\":\"cdd-cesm\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cdd-cesm\\\/\",\"title\":\"Categorized Digital Database for Low energy and Subtracted Contrast Enhanced Spectral Mammography images\",\"short_title\":\"CDD-CESM\",\"doi\":\"10.7937\\\/29kw-ae92\",\"summary\":\"Deep learning (DL) has a promising potential in reducing the workload of radiologists and helping them provide a more accurate diagnosis. However, fully annotated and large-sized datasets are required. This dataset is a collection of 2,006 high-resolution Contrast-enhanced spectral mammography (CESM) images with annotations and medical reports.\\u00a0Acquisition protocol:\\u00a0CESM is done using the standard digital mammography equipment, with additional software that performs dual-energy image acquisition. Two minutes after intravenously injecting the patient with non-ionic low-osmolar iodinated contrast material (dose: 1.5 mL\\\/kg), craniocaudal (CC) and mediolateral oblique (MLO) views are obtained. Each view comprises two exposures, one with low energy (peak kilo-voltage values ranging from 26 to 31kVp) and one with high energy (45 to 49 kVp). Low and high-energy images are then recombined and subtracted through appropriate image processing to suppress the background breast parenchyma. A complete examination is carried out in about 5-6 minutes.Image preprocessing:The images were converted from DICOM to JPEG using RadiAnt with best 100% image quality (lossless).\\u00a0 They have an average of 2355 x 1315 pixels.Supporting data:Full medical reports are also provided for each case (DOCX) along with manual segmentation annotation for the abnormal findings in each image (CSV file).\\u00a0\\u00a0Each image with its corresponding manual annotation (breast composition, mass shape, mass margin, mass density, architectural distortion, asymmetries, calcification type, calcification distribution, mass enhancement pattern, non-mass enhancement pattern, non-mass enhancement distribution, and overall BIRADS assessment) is compiled into 1 Excel file.\\u00a0https:\\\/\\\/www.robots.ox.ac.uk\\\/~vgg\\\/software\\\/via\\\/via.html was used for the segmentation annotation.\\u00a0 It can be used to show the annotations on the images by clicking on Annotation--> import annotations (from csv), and then proceeding to upload any image to view the annotations drawn over it.\\u00a0 Moreover, a helper repository is created to help with pre-processing, model training, model evaluation, and segmentation annotation loading: https:\\\/\\\/github.com\\\/omar-mohamed\\\/CDD-CESM-DatasetRegarding the tabs on the annotations Excel file, these are commonly used radiological descriptors as defined by the American College of Radiology 2013 lexicon.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/figure_CDD-CESM.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MG\",\"Classification\",\"Measurement\",\"Segmentation\",\"Demographic\",\"Follow-Up\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":326,\"date_updated\":\"2021-12-31\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjYyMSIsInBhc3Njb2RlIjoiNzMzZTViZmQyNDQ1ODhjODNkOWVlMzJlYjg2M2Q4MGMwOGQwM2I4NSIsInBhY2thZ2VfaWQiOiI2MjEiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"categorized digital database for low energy and subtracted contrast enhanced spectral mammography images cdd-cesm deep learning (dl) has a promising potential in reducing the workload of radiologists and helping them provide a more accurate diagnosis. however, fully annotated and large-sized datasets are required. this dataset is a collection of 2,006 high-resolution contrast-enhanced spectral mammography (cesm) images with annotations and medical reports.\\u00a0acquisition protocol:\\u00a0cesm is done using the standard digital mammography equipment, with additional software that performs dual-energy image acquisition. two minutes after intravenously injecting the patient with non-ionic low-osmolar iodinated contrast material (dose: 1.5 ml\\\/kg), craniocaudal (cc) and mediolateral oblique (mlo) views are obtained. each view comprises two exposures, one with low energy (peak kilo-voltage values ranging from 26 to 31kvp) and one with high energy (45 to 49 kvp). low and high-energy images are then recombined and subtracted through appropriate image processing to suppress the background breast parenchyma. a complete examination is carried out in about 5-6 minutes.image preprocessing:the images were converted from dicom to jpeg using radiant with best 100% image quality (lossless).\\u00a0 they have an average of 2355 x 1315 pixels.supporting data:full medical reports are also provided for each case (docx) along with manual segmentation annotation for the abnormal findings in each image (csv file).\\u00a0\\u00a0each image with its corresponding manual annotation (breast composition, mass shape, mass margin, mass density, architectural distortion, asymmetries, calcification type, calcification distribution, mass enhancement pattern, non-mass enhancement pattern, non-mass enhancement distribution, and overall birads assessment) is compiled into 1 excel file.\\u00a0https:\\\/\\\/www.robots.ox.ac.uk\\\/~vgg\\\/software\\\/via\\\/via.html was used for the segmentation annotation.\\u00a0 it can be used to show the annotations on the images by clicking on annotation--> import annotations (from csv), and then proceeding to upload any image to view the annotations drawn over it.\\u00a0 moreover, a helper repository is created to help with pre-processing, model training, model evaluation, and segmentation annotation loading: https:\\\/\\\/github.com\\\/omar-mohamed\\\/cdd-cesm-datasetregarding the tabs on the annotations excel file, these are commonly used radiological descriptors as defined by the american college of radiology 2013 lexicon. breast cancer breast mg classification measurement segmentation demographic follow-up clinical image analyses software\\\/source code community\"},{\"id\":41649,\"type\":\"collection\",\"slug\":\"cmb-aml\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cmb-aml\\\/\",\"title\":\"Cancer Moonshot Biobank - Acute Myeloid Leukemia Cancer Collection\",\"short_title\":\"CMB-AML\",\"doi\":\"10.7937\\\/PCTE-6M66\",\"summary\":\"The Cancer Moonshot Biobank is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the U.S. and receiving standard of care cancer treatment at multiple NCI Community Oncology Research Program (NCORP) sites.This collection contains de-identified radiology and histopathology imaging procured from subjects in NCI\\u2019s Cancer Moonshot Biobank - Acute Myeloid Leukemia Cancer (CMB-AML) cohort. Associated genomic, phenotypic and clinical data will be hosted by The Database of Genotypes and Phenotypes (dbGaP) and other NCI databases. \\u00a0A summary of Cancer Moonshot Biobank imaging efforts can be found on the\\u00a0Cancer Moonshot Biobank Imaging\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NIH-Cancer-Moonshot-logo.png\",\"program\":\"CMB\",\"cancer_types\":[\"Acute Myeloid Leukemia\"],\"cancer_locations\":[\"Blood\"],\"data_types\":[\"MR\",\"CT\",\"XA\",\"Histopathology\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":9,\"date_updated\":\"2024-12-19\",\"status\":\"Ongoing\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?saved-cart=nbia-79801734441181908\",\"download_url\":\"https:\\\/\\\/www.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CMB-AML_v04_20240828.tcia\",\"blob\":\"cancer moonshot biobank - acute myeloid leukemia cancer collection cmb-aml the cancer moonshot biobank is a national cancer institute initiative to support current and future investigations into drug resistance and sensitivity and other nci-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. during the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the u.s. and receiving standard of care cancer treatment at multiple nci community oncology research program (ncorp) sites.this collection contains de-identified radiology and histopathology imaging procured from subjects in nci\\u2019s cancer moonshot biobank - acute myeloid leukemia cancer (cmb-aml) cohort. associated genomic, phenotypic and clinical data will be hosted by the database of genotypes and phenotypes (dbgap) and other nci databases. \\u00a0a summary of cancer moonshot biobank imaging efforts can be found on the\\u00a0cancer moonshot biobank imaging\\u00a0page. acute myeloid leukemia blood mr ct xa histopathology clinical cmb\"},{\"id\":49063,\"type\":\"collection\",\"slug\":\"cmb-brca\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cmb-brca\\\/\",\"title\":\"Cancer Moonshot Biobank - Invasive Breast Carcinoma Cancer Collection\",\"short_title\":\"CMB-BRCA\",\"doi\":\"10.7937\\\/dx22-8j71\",\"summary\":\"The Cancer Moonshot Biobank is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the U.S. and receiving standard of care cancer treatment at multiple NCI Community Oncology Research Program (NCORP) sites.This collection contains de-identified radiology and histopathology imaging procured from subjects in NCI\\u2019s Cancer Moonshot Biobank - Invasive Breast Carcinoma Cancer (CMB-BRCA) cohort. Associated genomic, phenotypic and clinical data will be hosted by The Database of Genotypes and Phenotypes (dbGaP) and other NCI databases. \\u00a0A summary of Cancer Moonshot Biobank imaging efforts can be found on the\\u00a0Cancer Moonshot Biobank Imaging\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NIH-Cancer-Moonshot-logo.png\",\"program\":\"CMB\",\"cancer_types\":[\"Breast Invasive Carcinoma\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"CT\",\"Histopathology\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":26,\"date_updated\":\"2024-12-17\",\"status\":\"Ongoing\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?saved-cart=nbia-20431734126581117\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CMB-BRCA_v02_20241217.tcia\",\"blob\":\"cancer moonshot biobank - invasive breast carcinoma cancer collection cmb-brca the cancer moonshot biobank is a national cancer institute initiative to support current and future investigations into drug resistance and sensitivity and other nci-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. during the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the u.s. and receiving standard of care cancer treatment at multiple nci community oncology research program (ncorp) sites.this collection contains de-identified radiology and histopathology imaging procured from subjects in nci\\u2019s cancer moonshot biobank - invasive breast carcinoma cancer (cmb-brca) cohort. associated genomic, phenotypic and clinical data will be hosted by the database of genotypes and phenotypes (dbgap) and other nci databases. \\u00a0a summary of cancer moonshot biobank imaging efforts can be found on the\\u00a0cancer moonshot biobank imaging\\u00a0page. breast invasive carcinoma breast ct histopathology clinical cmb\"},{\"id\":41671,\"type\":\"collection\",\"slug\":\"cmb-crc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cmb-crc\\\/\",\"title\":\"Cancer Moonshot Biobank - Colorectal Cancer Collection\",\"short_title\":\"CMB-CRC\",\"doi\":\"10.7937\\\/DJG7-GZ87\",\"summary\":\"The Cancer Moonshot Biobank is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the U.S. and receiving standard of care cancer treatment at multiple NCI Community Oncology Research Program (NCORP) sites.This collection contains de-identified radiology and histopathology imaging procured from subjects in NCI\\u2019s Cancer Moonshot Biobank-Colorectal Cancer (CMB-CRC) cohort.\\u00a0Associated genomic, phenotypic and clinical data will be hosted by The Database of Genotypes and Phenotypes (dbGaP) and other NCI databases. \\u00a0A summary of Cancer Moonshot Biobank imaging efforts can be found on the  Cancer Moonshot Biobank Imaging\\u00a0page.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NIH-Cancer-Moonshot-logo.png\",\"program\":\"CMB\",\"cancer_types\":[\"Colorectal Cancer\"],\"cancer_locations\":[\"Colon\"],\"data_types\":[\"CT\",\"NM\",\"PT\",\"MR\",\"DX\",\"US\",\"XA\",\"Histopathology\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":54,\"date_updated\":\"2024-08-28\",\"status\":\"Ongoing\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?saved-cart=nbia-29251734441385509\",\"download_url\":\"https:\\\/\\\/www.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CMB-CRC_v07_20240828.tcia\",\"blob\":\"cancer moonshot biobank - colorectal cancer collection cmb-crc the cancer moonshot biobank is a national cancer institute initiative to support current and future investigations into drug resistance and sensitivity and other nci-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. during the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the u.s. and receiving standard of care cancer treatment at multiple nci community oncology research program (ncorp) sites.this collection contains de-identified radiology and histopathology imaging procured from subjects in nci\\u2019s cancer moonshot biobank-colorectal cancer (cmb-crc) cohort.\\u00a0associated genomic, phenotypic and clinical data will be hosted by the database of genotypes and phenotypes (dbgap) and other nci databases. \\u00a0a summary of cancer moonshot biobank imaging efforts can be found on the  cancer moonshot biobank imaging\\u00a0page.\\u00a0 colorectal cancer colon ct nm pt mr dx us xa histopathology clinical cmb\"},{\"id\":41681,\"type\":\"collection\",\"slug\":\"cmb-gec\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cmb-gec\\\/\",\"title\":\"Cancer Moonshot Biobank - Gastroesophageal Cancer Collection\",\"short_title\":\"CMB-GEC\",\"doi\":\"10.7937\\\/E7KH-R486\",\"summary\":\"The Cancer Moonshot Biobank is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the U.S. and receiving standard of care cancer treatment at multiple NCI Community Oncology Research Program (NCORP) sites.This collection contains de-identified radiology and histopathology imaging procured from subjects in NCI\\u2019s Cancer Moonshot Biobank - Gastroesophageal Cancer (CMB-GEC) cohort. Associated genomic, phenotypic and clinical data will be hosted by The Database of Genotypes and Phenotypes (dbGaP) and other NCI databases. \\u00a0A summary of Cancer Moonshot Biobank imaging efforts can be found on the\\u00a0Cancer Moonshot Biobank Imaging\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NIH-Cancer-Moonshot-logo.png\",\"program\":\"CMB\",\"cancer_types\":[\"Gastroesophageal Cancer\"],\"cancer_locations\":[\"Esophagus\"],\"data_types\":[\"CT\",\"PT\",\"MR\",\"Histopathology\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":12,\"date_updated\":\"2024-08-28\",\"status\":\"Ongoing\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?saved-cart=nbia-84821734441287042\",\"download_url\":\"https:\\\/\\\/www.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CMB-GEC_v05_20240828.tcia\",\"blob\":\"cancer moonshot biobank - gastroesophageal cancer collection cmb-gec the cancer moonshot biobank is a national cancer institute initiative to support current and future investigations into drug resistance and sensitivity and other nci-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. during the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the u.s. and receiving standard of care cancer treatment at multiple nci community oncology research program (ncorp) sites.this collection contains de-identified radiology and histopathology imaging procured from subjects in nci\\u2019s cancer moonshot biobank - gastroesophageal cancer (cmb-gec) cohort. associated genomic, phenotypic and clinical data will be hosted by the database of genotypes and phenotypes (dbgap) and other nci databases. \\u00a0a summary of cancer moonshot biobank imaging efforts can be found on the\\u00a0cancer moonshot biobank imaging\\u00a0page. gastroesophageal cancer esophagus ct pt mr histopathology clinical cmb\"},{\"id\":41703,\"type\":\"collection\",\"slug\":\"cmb-lca\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cmb-lca\\\/\",\"title\":\"Cancer Moonshot Biobank - Lung Cancer Collection\",\"short_title\":\"CMB-LCA\",\"doi\":\"10.7937\\\/3CX3-S132\",\"summary\":\"The Cancer Moonshot Biobank is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the U.S. and receiving standard of care cancer treatment at multiple NCI Community Oncology Research Program (NCORP) sites.This collection contains de-identified radiology and histopathology imaging procured from subjects in NCI\\u2019s Cancer Moonshot Biobank-Lung Cancer (CMB-LCA) cohort.\\u00a0Associated genomic, phenotypic and clinical data will be hosted by The Database of Genotypes and Phenotypes (dbGaP) and other NCI databases. A summary of Cancer Moonshot Biobank imaging efforts can be found on the\\u00a0Cancer Moonshot Biobank Imaging\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NIH-Cancer-Moonshot-logo.png\",\"program\":\"CMB\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"MR\",\"CT\",\"NM\",\"DX\",\"US\",\"PT\",\"Histopathology\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":97,\"date_updated\":\"2024-12-17\",\"status\":\"Ongoing\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?saved-cart=nbia-16321734440516836\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CMB-LCA_v08_20241217.tcia\",\"blob\":\"cancer moonshot biobank - lung cancer collection cmb-lca the cancer moonshot biobank is a national cancer institute initiative to support current and future investigations into drug resistance and sensitivity and other nci-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. during the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the u.s. and receiving standard of care cancer treatment at multiple nci community oncology research program (ncorp) sites.this collection contains de-identified radiology and histopathology imaging procured from subjects in nci\\u2019s cancer moonshot biobank-lung cancer (cmb-lca) cohort.\\u00a0associated genomic, phenotypic and clinical data will be hosted by the database of genotypes and phenotypes (dbgap) and other nci databases. a summary of cancer moonshot biobank imaging efforts can be found on the\\u00a0cancer moonshot biobank imaging\\u00a0page. lung cancer lung mr ct nm dx us pt histopathology clinical cmb\"},{\"id\":41725,\"type\":\"collection\",\"slug\":\"cmb-mel\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cmb-mel\\\/\",\"title\":\"Cancer Moonshot Biobank - Melanoma Collection\",\"short_title\":\"CMB-MEL\",\"doi\":\"10.7937\\\/GWSP-WH72\",\"summary\":\"The Cancer Moonshot Biobank is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the U.S. and receiving standard of care cancer treatment at multiple NCI Community Oncology Research Program (NCORP) sites.This collection contains de-identified radiology and histopathology imaging procured from subjects in NCI\\u2019s Cancer Moonshot Biobank-Melanoma (CMB-MEL) cohort.\\u00a0Associated genomic, phenotypic and clinical data will be hosted by The Database of Genotypes and Phenotypes (dbGaP) and other NCI databases. \\u00a0A summary of Cancer Moonshot Biobank imaging efforts can be found on the\\u00a0Cancer Moonshot Biobank Imaging\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NIH-Cancer-Moonshot-logo.png\",\"program\":\"CMB\",\"cancer_types\":[\"Melanoma\"],\"cancer_locations\":[\"Various\"],\"data_types\":[\"CT\",\"PT\",\"US\",\"MR\",\"Histopathology\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":53,\"date_updated\":\"2024-12-17\",\"status\":\"Ongoing\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?saved-cart=nbia-31271734440773738\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CMB-MEL_v08_20241217.tcia\",\"blob\":\"cancer moonshot biobank - melanoma collection cmb-mel the cancer moonshot biobank is a national cancer institute initiative to support current and future investigations into drug resistance and sensitivity and other nci-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. during the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the u.s. and receiving standard of care cancer treatment at multiple nci community oncology research program (ncorp) sites.this collection contains de-identified radiology and histopathology imaging procured from subjects in nci\\u2019s cancer moonshot biobank-melanoma (cmb-mel) cohort.\\u00a0associated genomic, phenotypic and clinical data will be hosted by the database of genotypes and phenotypes (dbgap) and other nci databases. \\u00a0a summary of cancer moonshot biobank imaging efforts can be found on the\\u00a0cancer moonshot biobank imaging\\u00a0page. melanoma various ct pt us mr histopathology clinical cmb\"},{\"id\":41741,\"type\":\"collection\",\"slug\":\"cmb-mml\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cmb-mml\\\/\",\"title\":\"Cancer Moonshot Biobank - Multiple Myeloma Collection\",\"short_title\":\"CMB-MML\",\"doi\":\"10.7937\\\/SZKB-SW39\",\"summary\":\"The Cancer Moonshot Biobank is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the U.S. and receiving standard of care cancer treatment at multiple NCI Community Oncology Research Program (NCORP) sites.This collection contains de-identified radiology and histopathology imaging procured from subjects in NCI\\u2019s Cancer Moonshot Biobank-Multiple Myeloma (CMB-MML) cohort.\\u00a0Associated genomic, phenotypic and clinical data will be hosted by The Database of Genotypes and Phenotypes (dbGaP) and other NCI databases. \\u00a0A summary of Cancer Moonshot Biobank imaging efforts can be found on the\\u00a0Cancer Moonshot Biobank Imaging\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NIH-Cancer-Moonshot-logo.png\",\"program\":\"CMB\",\"cancer_types\":[\"Multiple Myeloma\"],\"cancer_locations\":[\"Blood\",\"Bone\"],\"data_types\":[\"CT\",\"MR\",\"PT\",\"XA\",\"DX\",\"CR\",\"Histopathology\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":89,\"date_updated\":\"2024-12-17\",\"status\":\"Ongoing\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?saved-cart=nbia-55351734440990040\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CMB_MML_v07_20241217.tcia\",\"blob\":\"cancer moonshot biobank - multiple myeloma collection cmb-mml the cancer moonshot biobank is a national cancer institute initiative to support current and future investigations into drug resistance and sensitivity and other nci-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. during the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the u.s. and receiving standard of care cancer treatment at multiple nci community oncology research program (ncorp) sites.this collection contains de-identified radiology and histopathology imaging procured from subjects in nci\\u2019s cancer moonshot biobank-multiple myeloma (cmb-mml) cohort.\\u00a0associated genomic, phenotypic and clinical data will be hosted by the database of genotypes and phenotypes (dbgap) and other nci databases. \\u00a0a summary of cancer moonshot biobank imaging efforts can be found on the\\u00a0cancer moonshot biobank imaging\\u00a0page. multiple myeloma blood bone ct mr pt xa dx cr histopathology clinical cmb\"},{\"id\":49073,\"type\":\"collection\",\"slug\":\"cmb-ov\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cmb-ov\\\/\",\"title\":\"Cancer Moonshot Biobank - Ovarian Carcinoma Cancer Collection\",\"short_title\":\"CMB-OV\",\"doi\":\"10.7937\\\/4nx6-e061\",\"summary\":\"The Cancer Moonshot Biobank is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the U.S. and receiving standard of care cancer treatment at multiple NCI Community Oncology Research Program (NCORP) sites.This collection contains de-identified radiology and histopathology imaging procured from subjects in NCI\\u2019s Cancer Moonshot Biobank - Ovarian Carcinoma Cancer (CMB-OV) cohort. Associated genomic, phenotypic and clinical data will be hosted by The Database of Genotypes and Phenotypes (dbGaP) and other NCI databases. \\u00a0A summary of Cancer Moonshot Biobank imaging efforts can be found on the\\u00a0Cancer Moonshot Biobank Imaging\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NIH-Cancer-Moonshot-logo.png\",\"program\":\"CMB\",\"cancer_types\":[\"Ovarian Cancer\"],\"cancer_locations\":[\"Ovary\"],\"data_types\":[\"Histopathology\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":14,\"date_updated\":\"2024-08-28\",\"status\":\"Ongoing\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202208%2Fbiobank_metadata_page_first50_4.json&filterState=%5B%7B%22id%22%3A%22TCIA_Collection%22%2C%22title%22%3A%22Collection%22%2C%22field%22%3A%22TCIA_Collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22CMB-OV%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjkzNSIsInBhc3Njb2RlIjoiYTdiNmVhMjkzMmRiNjQzMjE1ZjFmZGQ1NmNkNDY1OGQxMTNiYzQ1YyIsInBhY2thZ2VfaWQiOiI5MzUiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"cancer moonshot biobank - ovarian carcinoma cancer collection cmb-ov the cancer moonshot biobank is a national cancer institute initiative to support current and future investigations into drug resistance and sensitivity and other nci-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. during the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the u.s. and receiving standard of care cancer treatment at multiple nci community oncology research program (ncorp) sites.this collection contains de-identified radiology and histopathology imaging procured from subjects in nci\\u2019s cancer moonshot biobank - ovarian carcinoma cancer (cmb-ov) cohort. associated genomic, phenotypic and clinical data will be hosted by the database of genotypes and phenotypes (dbgap) and other nci databases. \\u00a0a summary of cancer moonshot biobank imaging efforts can be found on the\\u00a0cancer moonshot biobank imaging\\u00a0page. ovarian cancer ovary histopathology clinical cmb\"},{\"id\":41763,\"type\":\"collection\",\"slug\":\"cmb-pca\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cmb-pca\\\/\",\"title\":\"Cancer Moonshot Biobank - Prostate Cancer Collection\",\"short_title\":\"CMB-PCA\",\"doi\":\"10.7937\\\/25T7-6Y12\",\"summary\":\"The Cancer Moonshot Biobank is a National Cancer Institute initiative to support current and future investigations into drug resistance and sensitivity and other NCI-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. During the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the U.S. and receiving standard of care cancer treatment at multiple NCI Community Oncology Research Program (NCORP) sites.This collection contains de-identified radiology and histopathology imaging procured from subjects in NCI\\u2019s Cancer Moonshot Biobank - Prostate Cancer (CMB-PCA) cohort. Associated genomic, phenotypic and clinical data will be hosted by The Database of Genotypes and Phenotypes (dbGaP) and other NCI databases. \\u00a0A summary of Cancer Moonshot Biobank imaging efforts can be found on the\\u00a0Cancer Moonshot Biobank Imaging\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NIH-Cancer-Moonshot-logo.png\",\"program\":\"CMB\",\"cancer_types\":[\"Prostate Cancer\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"MR\",\"NM\",\"CT\",\"RF\",\"DX\",\"PT\",\"Histopathology\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":30,\"date_updated\":\"2024-12-17\",\"status\":\"Ongoing\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?saved-cart=nbia-52091734441085040\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CMB-PCA_v08_20241217.tcia\",\"blob\":\"cancer moonshot biobank - prostate cancer collection cmb-pca the cancer moonshot biobank is a national cancer institute initiative to support current and future investigations into drug resistance and sensitivity and other nci-sponsored cancer research initiatives, with an aim of improving researchers' understanding of cancer and how to intervene in cancer initiation and progression. during the course of this study, biospecimens (blood and tissue removed during medical procedures) and associated data will be collected longitudinally from at least 1000 patients across at least 10 cancer types, who represent the demographic diversity of the u.s. and receiving standard of care cancer treatment at multiple nci community oncology research program (ncorp) sites.this collection contains de-identified radiology and histopathology imaging procured from subjects in nci\\u2019s cancer moonshot biobank - prostate cancer (cmb-pca) cohort. associated genomic, phenotypic and clinical data will be hosted by the database of genotypes and phenotypes (dbgap) and other nci databases. \\u00a0a summary of cancer moonshot biobank imaging efforts can be found on the\\u00a0cancer moonshot biobank imaging\\u00a0page. prostate cancer prostate mr nm ct rf dx pt histopathology clinical cmb\"},{\"id\":44389,\"type\":\"collection\",\"slug\":\"cmmd\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cmmd\\\/\",\"title\":\"The Chinese Mammography Database\",\"short_title\":\"CMMD\",\"doi\":\"10.7937\\\/tcia.eqde-4b16\",\"summary\":\"Breast carcinoma is the second largest cancer in the world among women. Early detection of breast cancer has been shown to increase the survival rate, thereby significantly increasing patients' lifespans. Mammography, a noninvasive imaging tool with low cost, is widely used to diagnose breast disease at an early stage due to its high sensitivity. The recent popularization of artificial intelligence in computer-aided diagnosis creates opportunities for advances in areas such as\\u00a0(1) Computer-aided detection for locating suspect lesions such as mass and microcalcification, leaving the classification to the radiologist; and (2) Computer-aided diagnosis for characterizing the suspicious region of lesion and\\\/or estimate its probability of onset; and (3) Findings of predictive image-based biomarkers by applying the computational methods to mine the potential relationships between image representation and molecular subtype, including luminal A, luminal B, HER2 positive, and Triple-negative.However, existing publicly available mammography databases are limited by small sample size, lack of diversity in patient populations, missing biopsy confirmations and unknown molecular sub-types.\\u00a0\\u00a0To help fill the gap, we built a database\\u00a0conducted on 1,775 patients from China with benign or malignant breast disease who underwent mammography examination between July 2012 and January 2016. The database consists of 3,728 mammographies from these 1,775 patients, with biopsy confirmed type of benign or malignant tumors. For 749 of these patients (1,498 mammographies) we also include patients' molecular subtypes. Image\\u00a0data were acquired on a GE Senographe DS mammography system.\\u00a0\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Inclusion_exclusion_criteria.png\",\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MG\",\"Classification\",\"Molecular Test\",\"Demographic\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":1775,\"date_updated\":\"2021-04-06\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=CMMD\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/The-Chinese-Mammography-Database.tcia\",\"blob\":\"the chinese mammography database cmmd breast carcinoma is the second largest cancer in the world among women. early detection of breast cancer has been shown to increase the survival rate, thereby significantly increasing patients' lifespans. mammography, a noninvasive imaging tool with low cost, is widely used to diagnose breast disease at an early stage due to its high sensitivity. the recent popularization of artificial intelligence in computer-aided diagnosis creates opportunities for advances in areas such as\\u00a0(1) computer-aided detection for locating suspect lesions such as mass and microcalcification, leaving the classification to the radiologist; and (2) computer-aided diagnosis for characterizing the suspicious region of lesion and\\\/or estimate its probability of onset; and (3) findings of predictive image-based biomarkers by applying the computational methods to mine the potential relationships between image representation and molecular subtype, including luminal a, luminal b, her2 positive, and triple-negative.however, existing publicly available mammography databases are limited by small sample size, lack of diversity in patient populations, missing biopsy confirmations and unknown molecular sub-types.\\u00a0\\u00a0to help fill the gap, we built a database\\u00a0conducted on 1,775 patients from china with benign or malignant breast disease who underwent mammography examination between july 2012 and january 2016. the database consists of 3,728 mammographies from these 1,775 patients, with biopsy confirmed type of benign or malignant tumors. for 749 of these patients (1,498 mammographies) we also include patients' molecular subtypes. image\\u00a0data were acquired on a ge senographe ds mammography system.\\u00a0\\u00a0 breast cancer breast mg classification molecular test demographic clinical community\"},{\"id\":42469,\"type\":\"collection\",\"slug\":\"codex-imaging-of-hcc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/codex-imaging-of-hcc\\\/\",\"title\":\"Highly multiplexed spatially resolved immune cell atlas of hepatocellular carcinoma\",\"short_title\":\"CODEX imaging of HCC\",\"doi\":\"10.7937\\\/BH0R-Y074\",\"summary\":\"Introduction:Hepatocellular Carcinoma (HCC) is a leading cause of cancer-related death and can be considered a prototype of inflammation-derived cancer arising from chronic liver injury. The cell composition of the HCC tumor immune microenvironment (TiME) has a major impact on cancer biology as the TiME can have divergent capacities on tumor initiation, progress, and response to therapy. Recent development of multi-omics and single-cell technologies help us to comprehensively quantify the cellular heterogeneity and spatial organization of the TiME and to further our understanding of antitumor immunity.Methods:Multiplexed immunofluorescence microscopy was used to analyze immune cell infiltration in primary human liver cancer samples. We developed and validated a comprehensive 37-plex antibody panel for immunofluorescence imaging of human fresh frozen HCC samples. We applied highly multiplexed co-detection by indexing (CODEX) technology to simultaneously profile in situ expression of 37 proteins at sub-cellular resolution in 15 HCC patient samples (as well as one spleen and one lymph node specimen from anonymous deceased donors for validation purposes) using whole slide scanning. We established an image analysis pipeline to quantify all major cell populations in the human liver using supervised manual gating and unsupervised clustering algorithms. This extremely high-dimensional dataset was generated to allow data-driven investigation in patho-physiological immune cell interactions in the context of HCC. Clinical metadata including TMN stage, sex, gender, ethnicity, pretreatment, and histopathological reports are available for all patient samples.Results:Using high-dimensional spatially resolved quantitative analysis of multiplexed immunofluorescence microscopy images, we generated a unique dataset and profiled the single-cell pathology landscape for human HCC. In situ phenotyping of 4,500,000 single cells (including 1,500,000 CD45+ immune cells) allowed for the quantification of cell phenotype clusters, differential analysis of activation markers and spatial features of each individual cell. CODEX imaging revealed detailed composition of the immune cell niche in human liver cancer tissue allowing for further distinct spatial Beyond that, whole slide imaging allowed for the identification of the tumor-to-liver interface as a unique site of immune cell inhibition.Discussion:Here, we demonstrate that spatially resolved, single-cell analysis of human liver cancer tissue allows for the in-depth characterization of the immune cell composition of HCC. This tool can be used for biomarker research, to determine cellular functional states in intact tissue and to spatially and functionally quantify interactions between immune cells in the context of hepatocarcinogenesis. We expect that making this dataset publicly available will stimulate broad research endeavors into the immune tumor microenvironment of HCC and allow computational scientists to discover new biomarkers and features. Further details on the study can be obtained in our paper.Glossary:The files are labeled according to the following system:\\u00a0Example: reg001_cyc002_ch002_CD56reg001: multiple regions can be imaged in one tissue. For this experiment all tissues were images as one region: reg001.Cycle: CODEX imaging is done in cycles: this indicates which cycle the signal was detected in.Channel: the tissue was imaged on four channels: this indicates what channel the particular fluorophore-conjugated oligonucleotide signal was imaged at. Ch001: DAPI (DAPI filter), Ch002: AF488 (FITC filter), Ch003: Atto 550 (CY3 filter) and Ch004: AF647 (CY5 filter).The last part of the file extension corresponds to the antigen name: e.g. CD56\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CODEX-imaging-of-HCC_thumbnail.png\",\"program\":\"Community\",\"cancer_types\":[\"Hepatocellular carcinoma\"],\"cancer_locations\":[\"Liver\"],\"data_types\":[\"Histopathology\",\"CODEX images\",\"Diagnosis\",\"Demographic\",\"Measurement\",\"Treatment\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":15,\"date_updated\":\"2023-05-30\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=\\\/system\\\/files\\\/collectionmetadata\\\/202405\\\/CODEX-imaging-of-HCC_02-28-2024.json\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc1MSIsInBhc3Njb2RlIjoiNWIyOGUzYWIwNDVkMTMzZDAyYzQ1ZWU1YmNmN2RhNDY1OTc0NzVlOCIsInBhY2thZ2VfaWQiOiI3NTEiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"highly multiplexed spatially resolved immune cell atlas of hepatocellular carcinoma codex imaging of hcc introduction:hepatocellular carcinoma (hcc) is a leading cause of cancer-related death and can be considered a prototype of inflammation-derived cancer arising from chronic liver injury. the cell composition of the hcc tumor immune microenvironment (time) has a major impact on cancer biology as the time can have divergent capacities on tumor initiation, progress, and response to therapy. recent development of multi-omics and single-cell technologies help us to comprehensively quantify the cellular heterogeneity and spatial organization of the time and to further our understanding of antitumor immunity.methods:multiplexed immunofluorescence microscopy was used to analyze immune cell infiltration in primary human liver cancer samples. we developed and validated a comprehensive 37-plex antibody panel for immunofluorescence imaging of human fresh frozen hcc samples. we applied highly multiplexed co-detection by indexing (codex) technology to simultaneously profile in situ expression of 37 proteins at sub-cellular resolution in 15 hcc patient samples (as well as one spleen and one lymph node specimen from anonymous deceased donors for validation purposes) using whole slide scanning. we established an image analysis pipeline to quantify all major cell populations in the human liver using supervised manual gating and unsupervised clustering algorithms. this extremely high-dimensional dataset was generated to allow data-driven investigation in patho-physiological immune cell interactions in the context of hcc. clinical metadata including tmn stage, sex, gender, ethnicity, pretreatment, and histopathological reports are available for all patient samples.results:using high-dimensional spatially resolved quantitative analysis of multiplexed immunofluorescence microscopy images, we generated a unique dataset and profiled the single-cell pathology landscape for human hcc. in situ phenotyping of 4,500,000 single cells (including 1,500,000 cd45+ immune cells) allowed for the quantification of cell phenotype clusters, differential analysis of activation markers and spatial features of each individual cell. codex imaging revealed detailed composition of the immune cell niche in human liver cancer tissue allowing for further distinct spatial beyond that, whole slide imaging allowed for the identification of the tumor-to-liver interface as a unique site of immune cell inhibition.discussion:here, we demonstrate that spatially resolved, single-cell analysis of human liver cancer tissue allows for the in-depth characterization of the immune cell composition of hcc. this tool can be used for biomarker research, to determine cellular functional states in intact tissue and to spatially and functionally quantify interactions between immune cells in the context of hepatocarcinogenesis. we expect that making this dataset publicly available will stimulate broad research endeavors into the immune tumor microenvironment of hcc and allow computational scientists to discover new biomarkers and features. further details on the study can be obtained in our paper.glossary:the files are labeled according to the following system:\\u00a0example: reg001_cyc002_ch002_cd56reg001: multiple regions can be imaged in one tissue. for this experiment all tissues were images as one region: reg001.cycle: codex imaging is done in cycles: this indicates which cycle the signal was detected in.channel: the tissue was imaged on four channels: this indicates what channel the particular fluorophore-conjugated oligonucleotide signal was imaged at. ch001: dapi (dapi filter), ch002: af488 (fitc filter), ch003: atto 550 (cy3 filter) and ch004: af647 (cy5 filter).the last part of the file extension corresponds to the antigen name: e.g. cd56 hepatocellular carcinoma liver histopathology codex images diagnosis demographic measurement treatment clinical community\"},{\"id\":43151,\"type\":\"collection\",\"slug\":\"colorectal-liver-metastases\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/colorectal-liver-metastases\\\/\",\"title\":\"Preoperative CT and Survival Data for Patients Undergoing Resection of Colorectal Liver Metastases\",\"short_title\":\"Colorectal-Liver-Metastases\",\"doi\":\"10.7937\\\/QXK2-QG03\",\"summary\":\"This collection consists of DICOM images and DICOM Segmentation Objects (DSOs) for 197 patients with Colorectal Liver Metastases (CRLM). The collection consists of a large, single-institution consecutive series of patients that underwent resection of CRLM and matched preoperative computed tomography (CT) scans for quantitative image analysis. Inclusion criteria were (a) pathologically confirmed resected CRLM, (b) available data from pathologic analysis of the underlying non-tumoral liver parenchyma and hepatic tumor, (c) available preoperative conventional portal venous contrast-enhanced multi-detector computed tomography (MDCT) performed within 6 weeks of hepatic resection. Patients with 90-day mortality or that had less than 24 months of follow-up were excluded. Additionally, because pathologic and radiographic alterations of the non-tumoral liver parenchyma caused by hepatic artery infusion (HAI) of chemotherapy are not well described, any patient who received preoperative HAI was excluded. Finally, to obtain the most accurate future liver remnant (FLR), patients who underwent either local tumor ablation, more than 3 wedge resections, or had no visible tumor on preoperative imaging were excluded. The corresponding clinical dataset provides the variables collected including demographic, pathologic and survival data along with a data dictionary.The CT images were extracted from PACS and de-identified via institutional approved and a HIPAA compliant method. The liver, tumors, and vessels were semi-automatically segmented and a 3D model was generated using Scout Liver (Pathfinder Technologies Inc., TN, USA). Post-operative imaging and\\\/or the resection margin width from pathology analysis were used to determine the transection lines needed to generate the segmentation of the liver remnant after surgery. Our informatics team converted the native MHD segmentation files into DICOM-SEG format using the open-source dcmqi software. \\u00a0The segments in a given DICOM Segmentation Object (DSO) can be consistently identified using the Segment Label header, which follows the naming scheme described below. The SCT codes used to categorize the segments in the Segmented Property Type Code Sequence header may be updated in future releases to comply with best practices.Full details regarding the data collection and preparation process can be found in our data descriptor paper:Simpson, A. L., Peoples, J., Creasy, J. M., Fichtinger, G., Gangai, N., Keshavamurthy, K. N., Lasso, A., Shia, J., D\\u2019Angelica, M. I., & Do, R. K. G. (2024). Preoperative CT and survival data for patients undergoing resection of colorectal liver metastases. In Scientific Data (Vol. 11, Issue 1). Springer Science and Business Media LLC.\\u00a0https:\\\/\\\/doi.org\\\/10.1038\\\/s41597-024-02981-2Dataset: The dataset inside a subject folder is organized as follows:Comprised of Original DICOM CTs and Segmentations for each subject. The segmentations include \\\"Liver\\\", \\\"Liver_Remnant\\\" (liver that will remain after surgery based on a preoperative CT plan), \\\"Hepatic\\\" and \\\"Portal\\\" veins, and \\\"Tumor_x\\\", where \\\"x\\\" denotes the various tumor occurrences in the caseThe DSOs are verified for all cases. Case \\\"CRLM-CT-1074\\\" was scanned with the subject in a prone (laying on stomach) position, so the images are in a different orientation than usual.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/184_recurrence_preop_segmented.png\",\"program\":\"Community\",\"cancer_types\":[\"Colorectal Cancer\"],\"cancer_locations\":[\"Colon\"],\"data_types\":[\"CT\",\"SEG\",\"Demographic\",\"Measurement\",\"Treatment\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":197,\"date_updated\":\"2023-04-25\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Colorectal-Liver-Metastases\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Colorectal-Liver-Metastases-November-2022-manifest.tcia\",\"blob\":\"preoperative ct and survival data for patients undergoing resection of colorectal liver metastases colorectal-liver-metastases this collection consists of dicom images and dicom segmentation objects (dsos) for 197 patients with colorectal liver metastases (crlm). the collection consists of a large, single-institution consecutive series of patients that underwent resection of crlm and matched preoperative computed tomography (ct) scans for quantitative image analysis. inclusion criteria were (a) pathologically confirmed resected crlm, (b) available data from pathologic analysis of the underlying non-tumoral liver parenchyma and hepatic tumor, (c) available preoperative conventional portal venous contrast-enhanced multi-detector computed tomography (mdct) performed within 6 weeks of hepatic resection. patients with 90-day mortality or that had less than 24 months of follow-up were excluded. additionally, because pathologic and radiographic alterations of the non-tumoral liver parenchyma caused by hepatic artery infusion (hai) of chemotherapy are not well described, any patient who received preoperative hai was excluded. finally, to obtain the most accurate future liver remnant (flr), patients who underwent either local tumor ablation, more than 3 wedge resections, or had no visible tumor on preoperative imaging were excluded. the corresponding clinical dataset provides the variables collected including demographic, pathologic and survival data along with a data dictionary.the ct images were extracted from pacs and de-identified via institutional approved and a hipaa compliant method. the liver, tumors, and vessels were semi-automatically segmented and a 3d model was generated using scout liver (pathfinder technologies inc., tn, usa). post-operative imaging and\\\/or the resection margin width from pathology analysis were used to determine the transection lines needed to generate the segmentation of the liver remnant after surgery. our informatics team converted the native mhd segmentation files into dicom-seg format using the open-source dcmqi software. \\u00a0the segments in a given dicom segmentation object (dso) can be consistently identified using the segment label header, which follows the naming scheme described below. the sct codes used to categorize the segments in the segmented property type code sequence header may be updated in future releases to comply with best practices.full details regarding the data collection and preparation process can be found in our data descriptor paper:simpson, a. l., peoples, j., creasy, j. m., fichtinger, g., gangai, n., keshavamurthy, k. n., lasso, a., shia, j., d\\u2019angelica, m. i., & do, r. k. g. (2024). preoperative ct and survival data for patients undergoing resection of colorectal liver metastases. in scientific data (vol. 11, issue 1). springer science and business media llc.\\u00a0https:\\\/\\\/doi.org\\\/10.1038\\\/s41597-024-02981-2dataset: the dataset inside a subject folder is organized as follows:comprised of original dicom cts and segmentations for each subject. the segmentations include \\\"liver\\\", \\\"liver_remnant\\\" (liver that will remain after surgery based on a preoperative ct plan), \\\"hepatic\\\" and \\\"portal\\\" veins, and \\\"tumor_x\\\", where \\\"x\\\" denotes the various tumor occurrences in the casethe dsos are verified for all cases. case \\\"crlm-ct-1074\\\" was scanned with the subject in a prone (laying on stomach) position, so the images are in a different orientation than usual. colorectal cancer colon ct seg demographic measurement treatment clinical community\"},{\"id\":41835,\"type\":\"collection\",\"slug\":\"covid-19-ar\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/covid-19-ar\\\/\",\"title\":\"Chest Imaging with Clinical and Genomic Correlates Representing a Rural COVID-19 Positive Population\",\"short_title\":\"COVID-19-AR\",\"doi\":\"10.7937\\\/tcia.2020.py71-5978\",\"summary\":\"Radiology imaging\\u00a0 is playing an increasingly vital role in the diagnosis of COVID-19 patients and determining therapeutic options, patient care management and new research directions. Publicly available imaging data is essential to drive new research by permitting the creation of large multi-site cohorts for machine learning based analyses.\\u00a0 All too frequently rural populations are underrepresented in such public collections. In fact, the literature demonstrates there is very limited data on COVID-19 outcomes in rural populations, while it is well established that such populations have differentially high expression of key comorbidities.\\u00a0 Similarly, while the number of genomes of the SARS-COV-2 virus are rapidly growing in public repositories, few samples represent the variants expressed in rural populations.\\u00a0 This gap in available data is of particular importance given that the southern United States, as of July 2020, is the most rapidly expanding\\u00a0 COVID-19 hot spot on earth. We have published a collection of radiographic and CT imaging studies for patients who tested positive for COVID-19. Each patient is described by a limited set of clinical data correlates that includes demographics, comorbidities, selected lab data and key radiology findings.\\u00a0These data are cross-linked to SARS-COV-2 cDNA sequence data extracted from clinical isolates from the same population, uploaded to the Genbank repository. We believe this collection will help to define appropriate correlative data and contribute samples from this normally underrepresented population to the global research community.TCIA COVID-19 DatasetsAdditional datasets and information about TCIA efforts to support COVID-19 research can be found\\u00a0here.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/COVID-19-AR.jpg\",\"program\":\"COVID-19\",\"cancer_types\":[\"COVID-19 (non-cancer)\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"CR\",\"DX\",\"Demographic\",\"Diagnosis\",\"Other\"],\"supporting_data\":[\"Clinical\",\"Genomics\"],\"species\":[\"Human\"],\"subjects\":105,\"date_updated\":\"2020-07-13\",\"status\":\"Ongoing\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=COVID-19-AR\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/COVID-19-AR-July132020_NBIA-manifest.tcia\",\"blob\":\"chest imaging with clinical and genomic correlates representing a rural covid-19 positive population covid-19-ar radiology imaging\\u00a0 is playing an increasingly vital role in the diagnosis of covid-19 patients and determining therapeutic options, patient care management and new research directions. publicly available imaging data is essential to drive new research by permitting the creation of large multi-site cohorts for machine learning based analyses.\\u00a0 all too frequently rural populations are underrepresented in such public collections. in fact, the literature demonstrates there is very limited data on covid-19 outcomes in rural populations, while it is well established that such populations have differentially high expression of key comorbidities.\\u00a0 similarly, while the number of genomes of the sars-cov-2 virus are rapidly growing in public repositories, few samples represent the variants expressed in rural populations.\\u00a0 this gap in available data is of particular importance given that the southern united states, as of july 2020, is the most rapidly expanding\\u00a0 covid-19 hot spot on earth. we have published a collection of radiographic and ct imaging studies for patients who tested positive for covid-19. each patient is described by a limited set of clinical data correlates that includes demographics, comorbidities, selected lab data and key radiology findings.\\u00a0these data are cross-linked to sars-cov-2 cdna sequence data extracted from clinical isolates from the same population, uploaded to the genbank repository. we believe this collection will help to define appropriate correlative data and contribute samples from this normally underrepresented population to the global research community.tcia covid-19 datasetsadditional datasets and information about tcia efforts to support covid-19 research can be found\\u00a0here. covid-19 (non-cancer) lung ct cr dx demographic diagnosis other clinical genomics covid-19\"},{\"id\":43691,\"type\":\"collection\",\"slug\":\"covid-19-ny-sbu\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/covid-19-ny-sbu\\\/\",\"title\":\"Stony Brook University COVID-19 Positive Cases\",\"short_title\":\"COVID-19-NY-SBU\",\"doi\":\"10.7937\\\/TCIA.BBAG-2923\",\"summary\":\"This collection of cases was acquired at Stony Brook University from patients who tested positive for COVID-19.\\u00a0The collection\\u00a0includes images from\\u00a0different modalities and organ sites (chest radiographs, chest CTs, brain MRIs, etc.). Radiology imaging\\u00a0data is extremely important in COVID-19 from both a diagnostic and a monitoring perspective, given the crucial nature of\\u00a0COVID-19 pulmonary disease and its rapid phenotypic changes. The datasets are available for building AI systems for\\u00a0diagnostic\\u00a0and prognostic modeling.\\u00a0This collection also includes associated clinical data for each patient. The clinical data consists of diagnoses, procedures,\\u00a0lab tests,\\u00a0covid19 specific data values (e.g., intubation status, symptoms at admission)\\u00a0and a set of derived data elements,\\u00a0which were\\u00a0used in analyses of this data. The clinical data is stored as a set of csv files which comply with\\u00a0OMOP Common Data\\u00a0Model data elements.\\u00a0The images on the right show automated identification of regions of prognostic importance on baseline chest radiographs. The regions of highest prognostic importance (as determined by the AI algorithm) are observed primarily in lower lung regions, consistent with clinical findings on the corresponding CXRs.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image2021-8-9_13-0-15.png\",\"program\":\"COVID-19\",\"cancer_types\":[\"COVID-19 (non-cancer)\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"CR\",\"DX\",\"MR\",\"SR\",\"NM\",\"PT\",\"OT\",\"Follow-Up\",\"Treatment\",\"Exposure\",\"Molecular Test\",\"Other\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":1384,\"date_updated\":\"2021-08-11\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=COVID-19-NY-SBU\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/COVID-19-NY-SBU-manifest_20210810.tcia\",\"blob\":\"stony brook university covid-19 positive cases covid-19-ny-sbu this collection of cases was acquired at stony brook university from patients who tested positive for covid-19.\\u00a0the collection\\u00a0includes images from\\u00a0different modalities and organ sites (chest radiographs, chest cts, brain mris, etc.). radiology imaging\\u00a0data is extremely important in covid-19 from both a diagnostic and a monitoring perspective, given the crucial nature of\\u00a0covid-19 pulmonary disease and its rapid phenotypic changes. the datasets are available for building ai systems for\\u00a0diagnostic\\u00a0and prognostic modeling.\\u00a0this collection also includes associated clinical data for each patient. the clinical data consists of diagnoses, procedures,\\u00a0lab tests,\\u00a0covid19 specific data values (e.g., intubation status, symptoms at admission)\\u00a0and a set of derived data elements,\\u00a0which were\\u00a0used in analyses of this data. the clinical data is stored as a set of csv files which comply with\\u00a0omop common data\\u00a0model data elements.\\u00a0the images on the right show automated identification of regions of prognostic importance on baseline chest radiographs. the regions of highest prognostic importance (as determined by the ai algorithm) are observed primarily in lower lung regions, consistent with clinical findings on the corresponding cxrs. covid-19 (non-cancer) lung ct cr dx mr sr nm pt ot follow-up treatment exposure molecular test other clinical image analyses covid-19\"},{\"id\":44417,\"type\":\"collection\",\"slug\":\"cptac-aml\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cptac-aml\\\/\",\"title\":\"The Clinical Proteomic Tumor Analysis Consortium Acute Myeloid Leukemia Collection\",\"short_title\":\"CPTAC-AML\",\"doi\":\"10.7937\\\/TCIA.2019.B6FOE619\",\"summary\":\"This collection contains subjects from the National Cancer Institute\\u2019s Clinical Proteomic Tumor Analysis Consortium Acute Myeloid Leukemia (CPTAC-AML) cohort.\\u00a0CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name \\\"CPTAC-cancertype\\\". \\u00a0Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available. \\u00a0For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. Pathology imaging is collected as part of the CPTAC qualification workflow. \\u00a0All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicable. \\u00a0There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein in vivo. \\u00a0Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the CPTAC Imaging Proteomics page.\\u00a0CPTAC Imaging Special Interest GroupYou can join the CPTAC Imaging Special Interest Group\\u00a0to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 Artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0CPTAC SIG Webinars\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/cptac-logo-2.png\",\"program\":\"CPTAC\",\"cancer_types\":[\"Acute Myeloid Leukemia\"],\"cancer_locations\":[\"Marrow\",\"Blood\"],\"data_types\":[\"Histopathology\",\"Classification\",\"Follow-Up\",\"Demographic\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":88,\"date_updated\":\"2020-09-03\",\"status\":\"Ongoing\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202407%2Fcptac_metadata_07-09-2024.json&filterState=%5B%7B%22id%22%3A%22Tumor%22%2C%22title%22%3A%22Tumor%22%2C%22field%22%3A%22Tumor%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22AML%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjY2MiIsInBhc3Njb2RlIjoiOWVhNzM2Y2FlYjFhYzc2MzZjMDEwMjliODgwZGJlNzU5NmI5ZDJiMCIsInBhY2thZ2VfaWQiOiI2NjIiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"the clinical proteomic tumor analysis consortium acute myeloid leukemia collection cptac-aml this collection contains subjects from the national cancer institute\\u2019s clinical proteomic tumor analysis consortium acute myeloid leukemia (cptac-aml) cohort.\\u00a0cptac is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. radiology and pathology images from cptac patients are being collected and made publicly available by the cancer imaging archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.imaging from each cancer type will be contained in its own tcia collection, with the collection name \\\"cptac-cancertype\\\". \\u00a0radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available. \\u00a0for this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. pathology imaging is collected as part of the cptac qualification workflow. \\u00a0all cptac cohorts are released as either a single combined cohort, or split into discovery and confirmatory where applicable. \\u00a0there are two main types of proteomic studies: discovery proteomics and targeted proteomics. the term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. the term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein in vivo. \\u00a0once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. a summary of cptac imaging efforts can be found on the cptac imaging proteomics page.\\u00a0cptac imaging special interest groupyou can join the cptac imaging special interest group\\u00a0to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0cptac sig webinars\\u00a0page. acute myeloid leukemia marrow blood histopathology classification follow-up demographic clinical cptac\"},{\"id\":44425,\"type\":\"collection\",\"slug\":\"cptac-brca\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cptac-brca\\\/\",\"title\":\"The Clinical Proteomic Tumor Analysis Consortium Breast Invasive Carcinoma Collection\",\"short_title\":\"CPTAC-BRCA\",\"doi\":\"10.7937\\\/TCIA.CAEM-YS80\",\"summary\":\"This collection contains subjects from the National Cancer Institute\\u2019s\\u00a0Clinical Proteomic Tumor Analysis Consortium CPTAC Breast Invasive Carcinoma cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name \\\"CPTAC-cancertype\\\". \\u00a0Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0Pathology imaging is collected as part of the CPTAC qualification workflow. \\u00a0All CPTAC cohorts are released as either a single combined cohort, or split into\\u00a0Discovery\\u00a0and\\u00a0Confirmatory\\u00a0where applicable.\\u00a0 There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the\\u00a0CPTAC Imaging Proteomics\\u00a0page.\\u00a0CPTAC Imaging Special Interest GroupYou can join the\\u00a0CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 Artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0CPTAC SIG Webinars\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/cptac-logo-2.png\",\"program\":\"CPTAC\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"Histopathology\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Proteomics\"],\"species\":[\"Human\"],\"subjects\":134,\"date_updated\":\"2021-02-02\",\"status\":\"Ongoing\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22CPTAC-BRCA%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjU4MyIsInBhc3Njb2RlIjoiNTc5NmFiNGY2MzA1MzBlYTE2YTA3YjQxYzM5NmYzMDgzZDcwODkxOSIsInBhY2thZ2VfaWQiOiI1ODMiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"the clinical proteomic tumor analysis consortium breast invasive carcinoma collection cptac-brca this collection contains subjects from the national cancer institute\\u2019s\\u00a0clinical proteomic tumor analysis consortium cptac breast invasive carcinoma cohort. cptac is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. radiology and pathology images from cptac patients are being collected and made publicly available by the cancer imaging archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.imaging from each cancer type will be contained in its own tcia collection, with the collection name \\\"cptac-cancertype\\\". \\u00a0radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 for this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0pathology imaging is collected as part of the cptac qualification workflow. \\u00a0all cptac cohorts are released as either a single combined cohort, or split into\\u00a0discovery\\u00a0and\\u00a0confirmatory\\u00a0where applicable.\\u00a0 there are two main types of proteomic studies: discovery proteomics and targeted proteomics. the term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. the term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. a summary of cptac imaging efforts can be found on the\\u00a0cptac imaging proteomics\\u00a0page.\\u00a0cptac imaging special interest groupyou can join the\\u00a0cptac imaging special interest group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0cptac sig webinars\\u00a0page. breast cancer breast histopathology clinical genomics proteomics cptac\"},{\"id\":44523,\"type\":\"collection\",\"slug\":\"cptac-ccrcc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cptac-ccrcc\\\/\",\"title\":\"The Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma Collection\",\"short_title\":\"CPTAC-CCRCC\",\"doi\":\"10.7937\\\/k9\\\/tcia.2018.oblamn27\",\"summary\":\"This collection contains subjects from the National Cancer Institute\\u2019s Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma (CPTAC-CCRCC) cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name \\\"CPTAC-cancertype\\\". \\u00a0Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0Pathology imaging is collected as part of the CPTAC qualification workflow. \\u00a0All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicable.\\u00a0 There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the\\u00a0CPTAC Imaging Proteomics\\u00a0page.\\u00a0CPTAC Imaging Special Interest GroupYou can join the\\u00a0CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 Artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0CPTAC SIG Webinars\\u00a0page.\\u00a0On February 4, 2020\\u00a0Dr. David Clarke\\u00a0presented the consortium's proteogenomic analyses of the CPTAC-CCRCC discovery\\u00a0cohort.\\u00a0 This deep dive into the CCRCC genomic and proteomic datasets will help researchers better understand how these can be correlated with features derived from the imaging data.\\u00a0(Download the slides)\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/cptac-logo-2.png\",\"program\":\"CPTAC\",\"cancer_types\":[\"Clear Cell Carcinoma\"],\"cancer_locations\":[\"Kidney\"],\"data_types\":[\"CT\",\"MR\",\"Histopathology\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Proteomics\"],\"species\":[\"Human\"],\"subjects\":262,\"date_updated\":\"2024-05-10\",\"status\":\"Ongoing\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&PatientCriteria=C3N-00310,C3N-00437,C3N-00305,C3N-00313,C3N-00312,C3N-00494,C3N-00832,C3N-00492,C3N-00491,C3N-01178,C3N-01361,C3N-01646,C3N-01651,C3N-01648,C3N-01902,C3N-01654,C3N-02262,C3N-01905,C3N-02263,C3N-02265,C3N-03019,C3N-03018,C3L-01352,C3L-01978,C3N-01989,C3L-01976,C3N-02332,C3L-00817,C3L-00799,C3L-00766,C3L-00610,C3L-00609,C3L-01954,C3L-00812,C3L-00800,C3N-00380,C3L-00815,C3L-00792,C3N-00435,C3L-01459,C3L-01464,C3L-01462,C3L-01467,C3L-01465,C3L-01958,C3N-01536,C3L-02202,C3L-01960,C3N-01524,C3N-00194,C3N-02063,C3N-00244,C3L-02841,C3N-00246,C3N-01828,C3N-02723,C3N-00953,C3N-01808,C3N-02015,C3N-01806,C3N-02811,C3N-02439,C3N-00733,C3L-01034,C3L-01463,C3L-00608&CollectionCriteria=CPTAC-CCRCC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA-CPTAC-CCRCC_v11_20230818.tcia\",\"blob\":\"the clinical proteomic tumor analysis consortium clear cell renal cell carcinoma collection cptac-ccrcc this collection contains subjects from the national cancer institute\\u2019s clinical proteomic tumor analysis consortium clear cell renal cell carcinoma (cptac-ccrcc) cohort. cptac is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. radiology and pathology images from cptac patients are being collected and made publicly available by the cancer imaging archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.imaging from each cancer type will be contained in its own tcia collection, with the collection name \\\"cptac-cancertype\\\". \\u00a0radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 for this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0pathology imaging is collected as part of the cptac qualification workflow. \\u00a0all cptac cohorts are released as either a single combined cohort, or split into discovery and confirmatory where applicable.\\u00a0 there are two main types of proteomic studies: discovery proteomics and targeted proteomics. the term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. the term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. a summary of cptac imaging efforts can be found on the\\u00a0cptac imaging proteomics\\u00a0page.\\u00a0cptac imaging special interest groupyou can join the\\u00a0cptac imaging special interest group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0cptac sig webinars\\u00a0page.\\u00a0on february 4, 2020\\u00a0dr. david clarke\\u00a0presented the consortium's proteogenomic analyses of the cptac-ccrcc discovery\\u00a0cohort.\\u00a0 this deep dive into the ccrcc genomic and proteomic datasets will help researchers better understand how these can be correlated with features derived from the imaging data.\\u00a0(download the slides) clear cell carcinoma kidney ct mr histopathology clinical genomics proteomics cptac\"},{\"id\":44599,\"type\":\"collection\",\"slug\":\"cptac-cm\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cptac-cm\\\/\",\"title\":\"The Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma Collection\",\"short_title\":\"CPTAC-CM\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2018.ODU24GZE\",\"summary\":\"\\u00a0This collection contains subjects from the National Cancer Institute\\u2019s Clinical Proteomic Tumor Analysis Consortium Cutaneous Melanoma (CPTAC-CM) cohort.\\u00a0CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.\\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0\\u00a0Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name \\\"CPTAC-cancertype\\\". \\u00a0Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0Pathology imaging is collected as part of the CPTAC qualification workflow. \\u00a0All CPTAC cohorts are released\\u00a0as either a single combined cohort, or split into\\u00a0Discovery\\u00a0and\\u00a0Confirmatory\\u00a0where applicable.\\u00a0 There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the\\u00a0CPTAC Imaging Proteomics\\u00a0page.\\u00a0CPTAC Imaging Special Interest GroupYou can join the\\u00a0CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 Artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0CPTAC SIG Webinars\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/cptac-logo-2.png\",\"program\":\"CPTAC\",\"cancer_types\":[\"Cutaneous Melanoma\"],\"cancer_locations\":[\"Skin\"],\"data_types\":[\"MR\",\"CT\",\"PT\",\"Histopathology\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":95,\"date_updated\":\"2024-04-29\",\"status\":\"Ongoing\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=CPTAC-CM\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA-CPTAC-CM_v11_20240429.tcia\",\"blob\":\"the clinical proteomic tumor analysis consortium cutaneous melanoma collection cptac-cm \\u00a0this collection contains subjects from the national cancer institute\\u2019s clinical proteomic tumor analysis consortium cutaneous melanoma (cptac-cm) cohort.\\u00a0cptac is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. radiology and pathology images from cptac patients are being collected and made publicly available by the cancer imaging archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.\\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0 \\u00a0\\u00a0imaging from each cancer type will be contained in its own tcia collection, with the collection name \\\"cptac-cancertype\\\". \\u00a0radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 for this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0pathology imaging is collected as part of the cptac qualification workflow. \\u00a0all cptac cohorts are released\\u00a0as either a single combined cohort, or split into\\u00a0discovery\\u00a0and\\u00a0confirmatory\\u00a0where applicable.\\u00a0 there are two main types of proteomic studies: discovery proteomics and targeted proteomics. the term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. the term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. a summary of cptac imaging efforts can be found on the\\u00a0cptac imaging proteomics\\u00a0page.\\u00a0cptac imaging special interest groupyou can join the\\u00a0cptac imaging special interest group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0cptac sig webinars\\u00a0page. cutaneous melanoma skin mr ct pt histopathology clinical cptac\"},{\"id\":44531,\"type\":\"collection\",\"slug\":\"cptac-coad\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cptac-coad\\\/\",\"title\":\"The Clinical Proteomic Tumor Analysis Consortium Colon Adenocarcinoma Collection\",\"short_title\":\"CPTAC-COAD\",\"doi\":\"10.7937\\\/TCIA.YZWQ-ZZ63\",\"summary\":\"\\u00a0This collection contains subjects from the National Cancer Institute\\u2019s Clinical Proteomic Tumor Analysis Consortium CPTAC\\u00a0Colon Adenocarcinoma cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name \\\"CPTAC-cancertype\\\". \\u00a0Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0Pathology imaging is collected as part of the CPTAC qualification workflow. \\u00a0All CPTAC cohorts are released as either a single combined cohort, or split into\\u00a0Discovery\\u00a0and\\u00a0Confirmatory\\u00a0where applicable.\\u00a0 There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the\\u00a0CPTAC Imaging Proteomics\\u00a0page.\\u00a0CPTAC Imaging Special Interest GroupYou can join the\\u00a0CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 Artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0CPTAC SIG Webinars\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/cptac-logo-2.png\",\"program\":\"CPTAC\",\"cancer_types\":[\"Colon Cancer\"],\"cancer_locations\":[\"Colon\"],\"data_types\":[\"Histopathology\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Proteomics\"],\"species\":[\"Human\"],\"subjects\":106,\"date_updated\":\"2021-02-02\",\"status\":\"Ongoing\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22CPTAC-COAD%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjU2MiIsInBhc3Njb2RlIjoiODhkZGVmZDAzMGEyODc0ZjRhOWM0OWM0MDhjOTQxM2MxMjIzNThiYiIsInBhY2thZ2VfaWQiOiI1NjIiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"the clinical proteomic tumor analysis consortium colon adenocarcinoma collection cptac-coad \\u00a0this collection contains subjects from the national cancer institute\\u2019s clinical proteomic tumor analysis consortium cptac\\u00a0colon adenocarcinoma cohort. cptac is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. radiology and pathology images from cptac patients are being collected and made publicly available by the cancer imaging archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.imaging from each cancer type will be contained in its own tcia collection, with the collection name \\\"cptac-cancertype\\\". \\u00a0radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 for this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0pathology imaging is collected as part of the cptac qualification workflow. \\u00a0all cptac cohorts are released as either a single combined cohort, or split into\\u00a0discovery\\u00a0and\\u00a0confirmatory\\u00a0where applicable.\\u00a0 there are two main types of proteomic studies: discovery proteomics and targeted proteomics. the term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. the term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. a summary of cptac imaging efforts can be found on the\\u00a0cptac imaging proteomics\\u00a0page.\\u00a0cptac imaging special interest groupyou can join the\\u00a0cptac imaging special interest group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0cptac sig webinars\\u00a0page. colon cancer colon histopathology clinical genomics proteomics cptac\"},{\"id\":44721,\"type\":\"collection\",\"slug\":\"cptac-gbm\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cptac-gbm\\\/\",\"title\":\"The Clinical Proteomic Tumor Analysis Consortium Glioblastoma Multiforme Collection\",\"short_title\":\"CPTAC-GBM\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2018.3RJE41Q1\",\"summary\":\"This collection contains subjects from the National Cancer Institute\\u2019s Clinical Proteomic Tumor Analysis Consortium\\u00a0Glioblastoma Multiforme (CPTAC-GBM) cohort. The GBM confirmatory cohort was supplemented with retrospective samples from CHOP (Children\\u2019s hospital of Philadelphia)-Upenn.\\u00a0CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name \\\"CPTAC-cancertype\\\". \\u00a0Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0Pathology imaging is collected as part of the CPTAC qualification workflow. \\u00a0All CPTAC cohorts are released as either a single combined cohort, or split into\\u00a0Discovery\\u00a0and\\u00a0Confirmatory\\u00a0where applicable.\\u00a0 There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the\\u00a0CPTAC Imaging Proteomics\\u00a0page.\\u00a0CPTAC Imaging Special Interest GroupYou can join the\\u00a0CPTAC Imaging Special Interest Group\\u00a0to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 Artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0CPTAC SIG Webinars\\u00a0page.On May 13, 2020 Liang-Bo Wang and Runyu Hong presented the consortium's proteogenomic analyses of the\\u00a0CPTAC Glioblastoma (GBM)\\u00a0cohort.\\u00a0 This deep dive into the GBM genomic and proteomic datasets will help researchers better understand how these can be correlated with features derived from the imaging data.\\u00a0 (Download the slides)\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/cptac-logo-2.png\",\"program\":\"CPTAC\",\"cancer_types\":[\"Glioblastoma Multiforme\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"CT\",\"Histopathology\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Proteomics\"],\"species\":[\"Human\"],\"subjects\":200,\"date_updated\":\"2024-07-08\",\"status\":\"Ongoing\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=CPTAC-GBM\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA-CPTAC-GBM_v16_20240708.tcia\",\"blob\":\"the clinical proteomic tumor analysis consortium glioblastoma multiforme collection cptac-gbm this collection contains subjects from the national cancer institute\\u2019s clinical proteomic tumor analysis consortium\\u00a0glioblastoma multiforme (cptac-gbm) cohort. the gbm confirmatory cohort was supplemented with retrospective samples from chop (children\\u2019s hospital of philadelphia)-upenn.\\u00a0cptac is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. radiology and pathology images from cptac patients are being collected and made publicly available by the cancer imaging archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.imaging from each cancer type will be contained in its own tcia collection, with the collection name \\\"cptac-cancertype\\\". \\u00a0radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 for this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0pathology imaging is collected as part of the cptac qualification workflow. \\u00a0all cptac cohorts are released as either a single combined cohort, or split into\\u00a0discovery\\u00a0and\\u00a0confirmatory\\u00a0where applicable.\\u00a0 there are two main types of proteomic studies: discovery proteomics and targeted proteomics. the term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. the term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. a summary of cptac imaging efforts can be found on the\\u00a0cptac imaging proteomics\\u00a0page.\\u00a0cptac imaging special interest groupyou can join the\\u00a0cptac imaging special interest group\\u00a0to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0cptac sig webinars\\u00a0page.on may 13, 2020 liang-bo wang and runyu hong presented the consortium's proteogenomic analyses of the\\u00a0cptac glioblastoma (gbm)\\u00a0cohort.\\u00a0 this deep dive into the gbm genomic and proteomic datasets will help researchers better understand how these can be correlated with features derived from the imaging data.\\u00a0 (download the slides) glioblastoma multiforme brain mr ct histopathology clinical genomics proteomics cptac\"},{\"id\":44829,\"type\":\"collection\",\"slug\":\"cptac-hnscc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cptac-hnscc\\\/\",\"title\":\"The Clinical Proteomic Tumor Analysis Consortium Head and Neck Squamous Cell Carcinoma Collection\",\"short_title\":\"CPTAC-HNSCC\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2018.UW45NH81\",\"summary\":\"This collection contains subjects from the National Cancer Institute\\u2019s Clinical Proteomic Tumor Analysis Consortium Head-and-Neck cancer (CPTAC-HNSCC) cohort.\\u00a0CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name \\\"CPTAC-cancertype\\\". \\u00a0Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0Pathology imaging is collected as part of the CPTAC qualification workflow. \\u00a0All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicable.\\u00a0 There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the\\u00a0CPTAC Imaging Proteomics\\u00a0page.\\u00a0CPTAC Imaging Special Interest GroupYou can join the\\u00a0CPTAC Imaging Special Interest Group\\u00a0to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 Artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0CPTAC SIG Webinars\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/cptac-logo-2.png\",\"program\":\"CPTAC\",\"cancer_types\":[\"Head and Neck Cancer\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"CT\",\"MR\",\"PT\",\"Histopathology\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Proteomics\",\"Microarray\"],\"species\":[\"Human\"],\"subjects\":207,\"date_updated\":\"2025-02-03\",\"status\":\"Ongoing\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=CPTAC-HNSCC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA-CPTAC-HNSCC_v18_20250203.tcia\",\"blob\":\"the clinical proteomic tumor analysis consortium head and neck squamous cell carcinoma collection cptac-hnscc this collection contains subjects from the national cancer institute\\u2019s clinical proteomic tumor analysis consortium head-and-neck cancer (cptac-hnscc) cohort.\\u00a0cptac is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. radiology and pathology images from cptac patients are being collected and made publicly available by the cancer imaging archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.imaging from each cancer type will be contained in its own tcia collection, with the collection name \\\"cptac-cancertype\\\". \\u00a0radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 for this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0pathology imaging is collected as part of the cptac qualification workflow. \\u00a0all cptac cohorts are released as either a single combined cohort, or split into discovery and confirmatory where applicable.\\u00a0 there are two main types of proteomic studies: discovery proteomics and targeted proteomics. the term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. the term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. a summary of cptac imaging efforts can be found on the\\u00a0cptac imaging proteomics\\u00a0page.\\u00a0cptac imaging special interest groupyou can join the\\u00a0cptac imaging special interest group\\u00a0to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0cptac sig webinars\\u00a0page. head and neck cancer head-neck ct mr pt histopathology clinical genomics proteomics microarray cptac\"},{\"id\":45043,\"type\":\"collection\",\"slug\":\"cptac-lscc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cptac-lscc\\\/\",\"title\":\"The Clinical Proteomic Tumor Analysis Consortium Lung Squamous Cell Carcinoma Collection\",\"short_title\":\"CPTAC-LSCC\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2018.6EMUB5L2\",\"summary\":\"This collection contains subjects from the National Cancer Institute\\u2019s Clinical Proteomic Tumor Analysis Consortium Lung Squamous Cell Carcinoma (CPTAC-LSCC) cohort.\\u00a0CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name \\\"CPTAC-cancertype\\\". \\u00a0Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0Pathology imaging is collected as part of the CPTAC qualification workflow. \\u00a0All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicable.\\u00a0 There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the\\u00a0CPTAC Imaging Proteomics\\u00a0page.\\u00a0CPTAC Imaging Special Interest GroupYou can join the\\u00a0CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 Artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0CPTAC SIG Webinars\\u00a0page.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/cptac-logo-2.png\",\"program\":\"CPTAC\",\"cancer_types\":[\"Squamous Cell Carcinoma\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"PT\",\"Histopathology\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Proteomics\"],\"species\":[\"Human\"],\"subjects\":212,\"date_updated\":\"2024-04-05\",\"status\":\"Ongoing\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=CPTAC-LSCC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA-CPTAC-LSCC_v15.tcia\",\"blob\":\"the clinical proteomic tumor analysis consortium lung squamous cell carcinoma collection cptac-lscc this collection contains subjects from the national cancer institute\\u2019s clinical proteomic tumor analysis consortium lung squamous cell carcinoma (cptac-lscc) cohort.\\u00a0cptac is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. radiology and pathology images from cptac patients are being collected and made publicly available by the cancer imaging archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.imaging from each cancer type will be contained in its own tcia collection, with the collection name \\\"cptac-cancertype\\\". \\u00a0radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 for this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0pathology imaging is collected as part of the cptac qualification workflow. \\u00a0all cptac cohorts are released as either a single combined cohort, or split into discovery and confirmatory where applicable.\\u00a0 there are two main types of proteomic studies: discovery proteomics and targeted proteomics. the term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. the term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. a summary of cptac imaging efforts can be found on the\\u00a0cptac imaging proteomics\\u00a0page.\\u00a0cptac imaging special interest groupyou can join the\\u00a0cptac imaging special interest group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0cptac sig webinars\\u00a0page.\\u00a0 squamous cell carcinoma lung ct pt histopathology clinical genomics proteomics cptac\"},{\"id\":44929,\"type\":\"collection\",\"slug\":\"cptac-luad\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cptac-luad\\\/\",\"title\":\"The Clinical Proteomic Tumor Analysis Consortium Lung Adenocarcinoma Collection\",\"short_title\":\"CPTAC-LUAD\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2018.PAT12TBS\",\"summary\":\"This collection contains subjects from the National Cancer Institute\\u2019s Clinical Proteomic Tumor Analysis Consortium Lung Adenocarcinoma (CPTAC-LUAD) cohort.\\u00a0CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name \\\"CPTAC-cancertype\\\". \\u00a0Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0Pathology imaging is collected as part of the CPTAC qualification workflow. \\u00a0All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicable.\\u00a0 There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the\\u00a0CPTAC Imaging Proteomics\\u00a0page.\\u00a0CPTAC Imaging Special Interest GroupYou can join the\\u00a0CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 Artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0CPTAC SIG Webinars\\u00a0page.On July 22, 2020\\u00a0Shankha Satpathy presented about the consortium's proteogenomic analyses of CPTAC-LUAD.\\u00a0 This deep dive into the LUAD genomic and proteomic datasets will help researchers better understand how these can be correlated with features derived from the imaging data.\\u00a0(Download the slides)\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/cptac-logo-2.png\",\"program\":\"CPTAC\",\"cancer_types\":[\"Lung Adenocarcinoma\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"MR\",\"PT\",\"Histopathology\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Proteomics\"],\"species\":[\"Human\"],\"subjects\":244,\"date_updated\":\"2023-02-24\",\"status\":\"Ongoing\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=CPTAC-LUAD\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA-CPTAC-LUAD_v12_20230224.tcia\",\"blob\":\"the clinical proteomic tumor analysis consortium lung adenocarcinoma collection cptac-luad this collection contains subjects from the national cancer institute\\u2019s clinical proteomic tumor analysis consortium lung adenocarcinoma (cptac-luad) cohort.\\u00a0cptac is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. radiology and pathology images from cptac patients are being collected and made publicly available by the cancer imaging archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.imaging from each cancer type will be contained in its own tcia collection, with the collection name \\\"cptac-cancertype\\\". \\u00a0radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 for this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0pathology imaging is collected as part of the cptac qualification workflow. \\u00a0all cptac cohorts are released as either a single combined cohort, or split into discovery and confirmatory where applicable.\\u00a0 there are two main types of proteomic studies: discovery proteomics and targeted proteomics. the term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. the term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. a summary of cptac imaging efforts can be found on the\\u00a0cptac imaging proteomics\\u00a0page.\\u00a0cptac imaging special interest groupyou can join the\\u00a0cptac imaging special interest group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0cptac sig webinars\\u00a0page.on july 22, 2020\\u00a0shankha satpathy presented about the consortium's proteogenomic analyses of cptac-luad.\\u00a0 this deep dive into the luad genomic and proteomic datasets will help researchers better understand how these can be correlated with features derived from the imaging data.\\u00a0(download the slides) lung adenocarcinoma lung ct mr pt histopathology clinical genomics proteomics cptac\"},{\"id\":45051,\"type\":\"collection\",\"slug\":\"cptac-ov\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cptac-ov\\\/\",\"title\":\"The Clinical Proteomic Tumor Analysis Consortium Ovarian Serous Cystadenocarcinoma Collection\",\"short_title\":\"CPTAC-OV\",\"doi\":\"10.7937\\\/TCIA.ZS4A-JD58\",\"summary\":\"\\u00a0This collection contains subjects from the National Cancer Institute\\u2019s Clinical Proteomic Tumor Analysis Consortium CPTAC\\u00a0Ovarian Serous Cystadenocarcinoma cohort. CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name \\\"CPTAC-cancertype\\\". \\u00a0Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0Pathology imaging is collected as part of the CPTAC qualification workflow. \\u00a0All CPTAC cohorts are released as either a single combined cohort, or split into Discovery and Confirmatory where applicable.\\u00a0 There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the\\u00a0CPTAC Imaging Proteomics\\u00a0page.\\u00a0CPTAC Imaging Special Interest GroupYou can join the\\u00a0CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 Artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0CPTAC SIG Webinars\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/cptac-logo-2.png\",\"program\":\"CPTAC\",\"cancer_types\":[\"Ovarian Cancer\"],\"cancer_locations\":[\"Ovary\"],\"data_types\":[\"Histopathology\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Proteomics\"],\"species\":[\"Human\"],\"subjects\":102,\"date_updated\":\"2021-02-02\",\"status\":\"Ongoing\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22CPTAC-OV%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjY3NCIsInBhc3Njb2RlIjoiNTkzNTE1YTBjZTdmODY1YTJkMmNiYzI2N2ZmYmQxODdmMzk4ZTIyMCIsInBhY2thZ2VfaWQiOiI2NzQiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"the clinical proteomic tumor analysis consortium ovarian serous cystadenocarcinoma collection cptac-ov \\u00a0this collection contains subjects from the national cancer institute\\u2019s clinical proteomic tumor analysis consortium cptac\\u00a0ovarian serous cystadenocarcinoma cohort. cptac is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. radiology and pathology images from cptac patients are being collected and made publicly available by the cancer imaging archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.imaging from each cancer type will be contained in its own tcia collection, with the collection name \\\"cptac-cancertype\\\". \\u00a0radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 for this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0pathology imaging is collected as part of the cptac qualification workflow. \\u00a0all cptac cohorts are released as either a single combined cohort, or split into discovery and confirmatory where applicable.\\u00a0 there are two main types of proteomic studies: discovery proteomics and targeted proteomics. the term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. the term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. a summary of cptac imaging efforts can be found on the\\u00a0cptac imaging proteomics\\u00a0page.\\u00a0cptac imaging special interest groupyou can join the\\u00a0cptac imaging special interest group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0cptac sig webinars\\u00a0page. ovarian cancer ovary histopathology clinical genomics proteomics cptac\"},{\"id\":45161,\"type\":\"collection\",\"slug\":\"cptac-pda\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cptac-pda\\\/\",\"title\":\"The Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma Collection\",\"short_title\":\"CPTAC-PDA\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2018.SC20FO18\",\"summary\":\"This collection contains subjects from the National Cancer Institute\\u2019s Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma (CPTAC-PDA) cohort.\\u00a0CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name \\\"CPTAC-cancertype\\\". \\u00a0Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0Pathology imaging is collected as part of the CPTAC qualification workflow. \\u00a0All CPTAC cohorts are released as either a single combined cohort, or split into\\u00a0Discovery\\u00a0and\\u00a0Confirmatory\\u00a0where applicable.\\u00a0 There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the\\u00a0CPTAC Imaging Proteomics\\u00a0page.\\u00a0CPTAC Imaging Special Interest GroupYou can join the\\u00a0CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses! Artifacts from previous webinars such as slide decks and video recordings can be found on the CPTAC SIG Webinars\\u00a0page.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/cptac-logo-2.png\",\"program\":\"CPTAC\",\"cancer_types\":[\"Ductal Adenocarcinoma\"],\"cancer_locations\":[\"Pancreas\"],\"data_types\":[\"MR\",\"CT\",\"US\",\"PT\",\"Histopathology\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Proteomics\"],\"species\":[\"Human\"],\"subjects\":168,\"date_updated\":\"2023-08-18\",\"status\":\"Ongoing\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=CPTAC-PDA\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA-CPTAC-PDA_v14_20230818.tcia\",\"blob\":\"the clinical proteomic tumor analysis consortium pancreatic ductal adenocarcinoma collection cptac-pda this collection contains subjects from the national cancer institute\\u2019s clinical proteomic tumor analysis consortium pancreatic ductal adenocarcinoma (cptac-pda) cohort.\\u00a0cptac is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. radiology and pathology images from cptac patients are being collected and made publicly available by the cancer imaging archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.imaging from each cancer type will be contained in its own tcia collection, with the collection name \\\"cptac-cancertype\\\". \\u00a0radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 for this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0pathology imaging is collected as part of the cptac qualification workflow. \\u00a0all cptac cohorts are released as either a single combined cohort, or split into\\u00a0discovery\\u00a0and\\u00a0confirmatory\\u00a0where applicable.\\u00a0 there are two main types of proteomic studies: discovery proteomics and targeted proteomics. the term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. the term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. a summary of cptac imaging efforts can be found on the\\u00a0cptac imaging proteomics\\u00a0page.\\u00a0cptac imaging special interest groupyou can join the\\u00a0cptac imaging special interest group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses! artifacts from previous webinars such as slide decks and video recordings can be found on the cptac sig webinars\\u00a0page.\\u00a0 ductal adenocarcinoma pancreas mr ct us pt histopathology clinical genomics proteomics cptac\"},{\"id\":45249,\"type\":\"collection\",\"slug\":\"cptac-sar\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cptac-sar\\\/\",\"title\":\"The Clinical Proteomic Tumor Analysis Consortium Sarcomas Collection\",\"short_title\":\"CPTAC-SAR\",\"doi\":\"10.7937\\\/tcia.2019.9bt23r95\",\"summary\":\"This collection contains subjects from the National Cancer Institute\\u2019s  Clinical Proteomic Tumor Analysis Consortium \\u00a0Sarcomas (CPTAC-SAR) cohort.\\u00a0CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name \\\"CPTAC-cancertype\\\". \\u00a0Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0Pathology imaging is collected as part of the CPTAC qualification workflow. \\u00a0All CPTAC cohorts are released\\u00a0as either a single combined cohort, or split into\\u00a0Discovery\\u00a0and\\u00a0Confirmatory\\u00a0where applicable.\\u00a0 There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the\\u00a0CPTAC Imaging Proteomics\\u00a0page.\\u00a0CPTAC Imaging Special Interest GroupYou can join the\\u00a0CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 Artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0CPTAC SIG Webinars\\u00a0page.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/cptac-logo-2.png\",\"program\":\"CPTAC\",\"cancer_types\":[\"Sarcomas\"],\"cancer_locations\":[\"Abdomen\",\"Arm\",\"Bladder\",\"Chest\",\"Head-Neck\",\"Kidney\",\"Leg\",\"Retroperitoneum\",\"Stomach\",\"Uterus\"],\"data_types\":[\"MR\",\"CT\",\"US\",\"PT\",\"Histopathology\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":88,\"date_updated\":\"2023-05-10\",\"status\":\"Ongoing\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=CPTAC-SAR\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA-CPTAC-SAR_v9_20230224.tcia\",\"blob\":\"the clinical proteomic tumor analysis consortium sarcomas collection cptac-sar this collection contains subjects from the national cancer institute\\u2019s  clinical proteomic tumor analysis consortium \\u00a0sarcomas (cptac-sar) cohort.\\u00a0cptac is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. radiology and pathology images from cptac patients are being collected and made publicly available by the cancer imaging archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.imaging from each cancer type will be contained in its own tcia collection, with the collection name \\\"cptac-cancertype\\\". \\u00a0radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 for this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0pathology imaging is collected as part of the cptac qualification workflow. \\u00a0all cptac cohorts are released\\u00a0as either a single combined cohort, or split into\\u00a0discovery\\u00a0and\\u00a0confirmatory\\u00a0where applicable.\\u00a0 there are two main types of proteomic studies: discovery proteomics and targeted proteomics. the term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. the term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. a summary of cptac imaging efforts can be found on the\\u00a0cptac imaging proteomics\\u00a0page.\\u00a0cptac imaging special interest groupyou can join the\\u00a0cptac imaging special interest group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0cptac sig webinars\\u00a0page. sarcomas abdomen arm bladder chest head-neck kidney leg retroperitoneum stomach uterus mr ct us pt histopathology clinical cptac\"},{\"id\":46845,\"type\":\"collection\",\"slug\":\"cptac-ucec\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/cptac-ucec\\\/\",\"title\":\"The Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma Collection\",\"short_title\":\"CPTAC-UCEC\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2018.3R3JUISW\",\"summary\":\"This collection contains subjects from the National Cancer Institute\\u2019s Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma (CPTAC-UCEC) cohort.\\u00a0CPTAC is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. Radiology and pathology images from CPTAC patients are being collected and made publicly available by The Cancer Imaging Archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.Imaging from each cancer type will be contained in its own TCIA Collection, with the collection name \\\"CPTAC-cancertype\\\". \\u00a0Radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 For this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0Pathology imaging is collected as part of the CPTAC qualification workflow. \\u00a0All CPTAC cohorts are released as either a single combined cohort, or split into\\u00a0Discovery\\u00a0and\\u00a0Confirmatory\\u00a0where applicable.\\u00a0 There are two main types of proteomic studies: discovery proteomics and targeted proteomics. The term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. The term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. Commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0Once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. A summary of CPTAC imaging efforts can be found on the\\u00a0CPTAC Imaging Proteomics\\u00a0page.\\u00a0CPTAC Imaging Special Interest GroupYou can join the\\u00a0CPTAC Imaging Special Interest Group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 Artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0CPTAC SIG Webinars page.On January 14, 2020\\u00a0Emily Kawaler presented the consortium's proteogenomic analyses of the CPTAC-UCEC.\\u00a0 This deep dive into the UCEC genomic and proteomic datasets will help researchers better understand how they can be correlated with features derived from the imaging data.\\u00a0(Download the slides)\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/cptac-logo-2.png\",\"program\":\"CPTAC\",\"cancer_types\":[\"Corpus Endometrial Carcinoma\"],\"cancer_locations\":[\"Uterus\"],\"data_types\":[\"CT\",\"RTSTRUCT\",\"MR\",\"PT\",\"US\",\"Histopathology\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Proteomics\"],\"species\":[\"Human\"],\"subjects\":250,\"date_updated\":\"2024-04-05\",\"status\":\"Ongoing\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=CPTAC-UCEC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA-CPTAC-UCEC_v12_20240405.tcia\",\"blob\":\"the clinical proteomic tumor analysis consortium uterine corpus endometrial carcinoma collection cptac-ucec this collection contains subjects from the national cancer institute\\u2019s clinical proteomic tumor analysis consortium uterine corpus endometrial carcinoma (cptac-ucec) cohort.\\u00a0cptac is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. radiology and pathology images from cptac patients are being collected and made publicly available by the cancer imaging archive to enable researchers to investigate cancer phenotypes which may correlate to corresponding proteomic, genomic and clinical data.imaging from each cancer type will be contained in its own tcia collection, with the collection name \\\"cptac-cancertype\\\". \\u00a0radiology imaging is collected from standard of care imaging performed on patients immediately before the pathological diagnosis, and from follow-up scans where available.\\u00a0 for this reason the radiology image data sets are heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols.\\u00a0pathology imaging is collected as part of the cptac qualification workflow. \\u00a0all cptac cohorts are released as either a single combined cohort, or split into\\u00a0discovery\\u00a0and\\u00a0confirmatory\\u00a0where applicable.\\u00a0 there are two main types of proteomic studies: discovery proteomics and targeted proteomics. the term \\\"discovery proteomics\\\" is in reference to \\\"untargeted\\\" identification and quantification of a maximal number of proteins in a biological or clinical sample. the term \\u201ctargeted proteomics\\u201d refers to quantitative measurements on a defined subset of total proteins in a biological or clinical sample, often following the completion of discovery proteomics studies to confirm interesting targets selected. commonly used proteomic technologies and platforms are different types of mass spectrometry and protein microarrays depending on the needs, throughput and sample input requirement of an analysis, with further development on nanotechnologies and automation in the pipeline in order to improve the detection of low abundance proteins, increase throughput, and selectively reach a target protein\\u00a0in vivo. \\u00a0once the protein targets of interest are identified, high-throughput targeted assays are developed for confirmatory studies: tests to affirm that the initial tests were accurate. a summary of cptac imaging efforts can be found on the\\u00a0cptac imaging proteomics\\u00a0page.\\u00a0cptac imaging special interest groupyou can join the\\u00a0cptac imaging special interest group to be notified of webinars & data releases, collaborate on common data wrangling tasks and seek out partners to explore research hypotheses!\\u00a0 artifacts from previous webinars such as slide decks and video recordings can be found on the\\u00a0cptac sig webinars page.on january 14, 2020\\u00a0emily kawaler presented the consortium's proteogenomic analyses of the cptac-ucec.\\u00a0 this deep dive into the ucec genomic and proteomic datasets will help researchers better understand how they can be correlated with features derived from the imaging data.\\u00a0(download the slides) corpus endometrial carcinoma uterus ct rtstruct mr pt us histopathology clinical genomics proteomics cptac\"},{\"id\":42461,\"type\":\"collection\",\"slug\":\"crc_ffpe-codex_cellneighs\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/crc_ffpe-codex_cellneighs\\\/\",\"title\":\"High-dimensional imaging of colorectal carcinoma and other tumors with 50+ markers\",\"short_title\":\"CRC_FFPE-CODEX_CellNeighs\",\"doi\":\"10.7937\\\/TCIA.2020.FQN0-0326\",\"summary\":\"We have used CODEX to image 56 proteins simultaneously in 140 tissue regions from the tumor invasive front of 35 advanced-stage colorectal cancer (CRC) patients (17 patients with Crohn's-like reaction (CLR) - leading to high amount of tertiary lymphoid structures (TLS); and 18 patients with diffuse inflammatory infiltration (DII) and no TLS). These patients were selected from an initial cohort of 715 CRC patients. Patients with low-stage CRC (pTNM 0-2), pre-operative chemotherapy, insufficient material, and low immune infiltration were excluded. The 35 resulting patients were matched for age, sex and tumor characteristics. CLR patients had a much better survival compared to DII patients.\\u00a0 We expect that making this dataset publicly available will stimulate broad research endeavors into the immune tumor microenvironment of colorectal cancer and allow computational scientists to discover new biomarkers and features.\\u00a0Further details on the study can be obtained in our paper here: https:\\\/\\\/www.cell.com\\\/cell\\\/fulltext\\\/S0092-8674(20)30870-9Details on image acquisition and processing:Automated imaging was performed on a Keyence BZ-X710 microscope using a CFI Plan Apo \\u03bb 20x\\\/0.75 objective (Nikon), in high-resolution mode, with a lateral resolution of 377.44 nm\\\/pixel. Processed images labeled with \\u201cmontage\\u201d only have half of that resolution, resulting in a 4x smaller image size (used for stitching of large tissue microarrays).\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Colorectal Cancer\"],\"cancer_locations\":[\"Colon\"],\"data_types\":[\"Histopathology\",\"CODEX images\",\"Diagnosis\",\"Follow-Up\",\"Demographic\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":35,\"date_updated\":\"2020-08-05\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=\\\/system\\\/files\\\/collectionmetadata\\\/202405\\\/CRC_FFPE-CODEX_CellNeighs_02-28-2024.json\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjY0NCIsInBhc3Njb2RlIjoiNzUyYjQ1ZThkYjVmN2RlZTJlMjBiNjgwZjEzNmZhM2MyNDUzNjY5MyIsInBhY2thZ2VfaWQiOiI2NDQiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"high-dimensional imaging of colorectal carcinoma and other tumors with 50+ markers crc_ffpe-codex_cellneighs we have used codex to image 56 proteins simultaneously in 140 tissue regions from the tumor invasive front of 35 advanced-stage colorectal cancer (crc) patients (17 patients with crohn's-like reaction (clr) - leading to high amount of tertiary lymphoid structures (tls); and 18 patients with diffuse inflammatory infiltration (dii) and no tls). these patients were selected from an initial cohort of 715 crc patients. patients with low-stage crc (ptnm 0-2), pre-operative chemotherapy, insufficient material, and low immune infiltration were excluded. the 35 resulting patients were matched for age, sex and tumor characteristics. clr patients had a much better survival compared to dii patients.\\u00a0 we expect that making this dataset publicly available will stimulate broad research endeavors into the immune tumor microenvironment of colorectal cancer and allow computational scientists to discover new biomarkers and features.\\u00a0further details on the study can be obtained in our paper here: https:\\\/\\\/www.cell.com\\\/cell\\\/fulltext\\\/s0092-8674(20)30870-9details on image acquisition and processing:automated imaging was performed on a keyence bz-x710 microscope using a cfi plan apo \\u03bb 20x\\\/0.75 objective (nikon), in high-resolution mode, with a lateral resolution of 377.44 nm\\\/pixel. processed images labeled with \\u201cmontage\\u201d only have half of that resolution, resulting in a 4x smaller image size (used for stitching of large tissue microarrays). colorectal cancer colon histopathology codex images diagnosis follow-up demographic clinical image analyses community\"},{\"id\":42033,\"type\":\"collection\",\"slug\":\"ct-colonography\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ct-colonography\\\/\",\"title\":\"ACRIN 6664\",\"short_title\":\"CT COLONOGRAPHY\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.NWTESAY1\",\"summary\":\"The National CT Colonography Trial (ACRIN 6664) collection contains 825 cases of CT colonography imaging with accompanying spreadsheets that provide polyp descriptions and their location within the colon segments.\\u00a0 Additional information about the trial is available in the Study Protocol and Case Report Forms.Main Objective:\\u00a0To clinically validate widespread use of computerized tomographic colonography (CTC) in a screening population for the detection of colorectal neoplasia.Participants:\\u00a0Male and female outpatients, aged 50 years or older, scheduled\\u00a0for screening colonoscopy, who have not had a colonoscopy in the past five years.Study Design Summary:\\u00a0The study addresses aspects of central importance to the clinical application of CTC in several interrelated but independent parts that will be conducted in parallel. In Part I, the clinical performance of the CTC examination will be prospectively compared in a blinded fashion to colonoscopy. In Part II, optimization of the CT technique will be performed in view of new technological advances in CT technology. In Part III, lesion detection will be\\u00a0optimized by studying the morphologic features of critical lesion types and in\\u00a0the development of a database for computer-assisted diagnosis. In Part IV,\\u00a0patient preferences and cost-effectiveness implications of observed performance\\u00a0outcomes will be evaluated using a predictive model.\",\"image\":false,\"program\":\"NCI Trials\",\"cancer_types\":[\"Colon Cancer\"],\"cancer_locations\":[\"Colon\"],\"data_types\":[\"CT\",\"Other\",\"Measurement\",\"Classification\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":825,\"date_updated\":\"2013-11-15\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=CT%20COLONOGRAPHY\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_CT_COLONOGRAPHY_06-22-2015.tcia\",\"blob\":\"acrin 6664 ct colonography the national ct colonography trial (acrin 6664) collection contains 825 cases of ct colonography imaging with accompanying spreadsheets that provide polyp descriptions and their location within the colon segments.\\u00a0 additional information about the trial is available in the study protocol and case report forms.main objective:\\u00a0to clinically validate widespread use of computerized tomographic colonography (ctc) in a screening population for the detection of colorectal neoplasia.participants:\\u00a0male and female outpatients, aged 50 years or older, scheduled\\u00a0for screening colonoscopy, who have not had a colonoscopy in the past five years.study design summary:\\u00a0the study addresses aspects of central importance to the clinical application of ctc in several interrelated but independent parts that will be conducted in parallel. in part i, the clinical performance of the ctc examination will be prospectively compared in a blinded fashion to colonoscopy. in part ii, optimization of the ct technique will be performed in view of new technological advances in ct technology. in part iii, lesion detection will be\\u00a0optimized by studying the morphologic features of critical lesion types and in\\u00a0the development of a database for computer-assisted diagnosis. in part iv,\\u00a0patient preferences and cost-effectiveness implications of observed performance\\u00a0outcomes will be evaluated using a predictive model. colon cancer colon ct other measurement classification image analyses nci trials\"},{\"id\":42049,\"type\":\"collection\",\"slug\":\"ct-images-in-covid-19\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ct-images-in-covid-19\\\/\",\"title\":\"CT Images in COVID-19\",\"short_title\":\"CT Images in COVID-19\",\"doi\":\"10.7937\\\/TCIA.2020.GQRY-NC81\",\"summary\":\"These retrospective NIfTI image datasets consists of unenhanced chest CTs:\\u00a0First dataset - from 632 patients\\u00a0with COVID-19 infections at initial point of care, andSecond dataset - a second set of 121\\u00a0CTs from 29 patients\\u00a0with COVID-19 infections with serial \\\/ sequential CTs.The initial images for both datasets were acquired\\u00a0at the point of care in an outbreak setting\\u00a0from patients with Reverse Transcription Polymerase Chain Reaction (RT-PCR) confirmation for the presence of SARS-CoV-2.Patients presented to a health care setting with a combination of symptoms, exposure to an infected patient, or travel history to an outbreak region. All patients had a positive RT-PCR for SARS-CoV-2 from a sample obtained within 1 day of the initial CT. CT exams were performed without intravenous contrast and with a soft tissue reconstruction algorithm.\\u00a0 The DICOM images were subsequently converted into NIfTI format.\\u00a0The second dataset also had other follow up CTs, in addition to the initial point of care CT.A multidisciplinary team trained several models using portions of the first dataset, along with additional CTs and manually annotated images from other sources. A classification model derived in part from the first dataset is described in a Nature Communications manuscript at:\\u00a0\\u00a0https:\\\/\\\/doi.org\\\/10.1038\\\/s41467-020-17971-2.\\u00a0 The\\u00a0NVIDIA-related frameworks and models specific to this publication are available at no cost as part of the NVIDIA Clara Train SDK at\\u00a0https:\\\/\\\/ngc.nvidia.com\\\/catalog\\\/containers\\\/nvidia:clara:ai-covid-19.\\u00a0This includes both inference-based pipelines for evaluation, as well as model weights for further training or fine tuning in outside institutions.\\u00a0The second data set of\\u00a0121 serial \\\/ sequential CTs in 29 patients is reported in a Scientific Reports manuscript at\\u00a0\\u00a0https:\\\/\\\/doi.org\\\/10.1038\\\/s41598-021-85694-5.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/COVID-TCIA1.jpg\",\"program\":\"COVID-19\",\"cancer_types\":[\"COVID-19 (non-cancer)\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":661,\"date_updated\":\"2021-05-25\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc1MyIsInBhc3Njb2RlIjoiNzNkNGM0NTMyMWI2NGI5MDlhMjE4YjllNTM1ZjY5Mjc4NGYyZGNhZSIsInBhY2thZ2VfaWQiOiI3NTMiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"ct images in covid-19 ct images in covid-19 these retrospective nifti image datasets consists of unenhanced chest cts:\\u00a0first dataset - from 632 patients\\u00a0with covid-19 infections at initial point of care, andsecond dataset - a second set of 121\\u00a0cts from 29 patients\\u00a0with covid-19 infections with serial \\\/ sequential cts.the initial images for both datasets were acquired\\u00a0at the point of care in an outbreak setting\\u00a0from patients with reverse transcription polymerase chain reaction (rt-pcr) confirmation for the presence of sars-cov-2.patients presented to a health care setting with a combination of symptoms, exposure to an infected patient, or travel history to an outbreak region. all patients had a positive rt-pcr for sars-cov-2 from a sample obtained within 1 day of the initial ct. ct exams were performed without intravenous contrast and with a soft tissue reconstruction algorithm.\\u00a0 the dicom images were subsequently converted into nifti format.\\u00a0the second dataset also had other follow up cts, in addition to the initial point of care ct.a multidisciplinary team trained several models using portions of the first dataset, along with additional cts and manually annotated images from other sources. a classification model derived in part from the first dataset is described in a nature communications manuscript at:\\u00a0\\u00a0https:\\\/\\\/doi.org\\\/10.1038\\\/s41467-020-17971-2.\\u00a0 the\\u00a0nvidia-related frameworks and models specific to this publication are available at no cost as part of the nvidia clara train sdk at\\u00a0https:\\\/\\\/ngc.nvidia.com\\\/catalog\\\/containers\\\/nvidia:clara:ai-covid-19.\\u00a0this includes both inference-based pipelines for evaluation, as well as model weights for further training or fine tuning in outside institutions.\\u00a0the second data set of\\u00a0121 serial \\\/ sequential cts in 29 patients is reported in a scientific reports manuscript at\\u00a0\\u00a0https:\\\/\\\/doi.org\\\/10.1038\\\/s41598-021-85694-5.\\u00a0 covid-19 (non-cancer) lung ct covid-19\"},{\"id\":41405,\"type\":\"collection\",\"slug\":\"ct-lymph-nodes\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ct-lymph-nodes\\\/\",\"title\":\"A new 2.5 D representation for lymph node detection in CT\",\"short_title\":\"CT Lymph Nodes\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.AQIIDCNM\",\"summary\":\"This collection consists of Computed Tomography (CT) images of the mediastinum and abdomen in which lymph node positions are marked by radiologists at the National Institutes of Health, Clinical Center. Radiologists at the   Imaging Biomarkers and Computer-Aided Diagnosis Laboratory   labeled a total of 388 mediastinal lymph nodes in CT images of 90 patients and a total of 595 abdominal lymph nodes in 86 patients.The collection is aimed at the medical image computing community for developing and assessing computer-aided detection methods. Automated detection of lymph nodes can be an important clinical diagnostic tool but is very challenging due to the low contrast of surrounding structures in CT and to their varying sizes, poses, shapes and sparsely distributed locations. This data set is made available to make direct comparison to other detection methods in order to advance the state of the art.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Lymphadenopathy (non-cancer)\"],\"cancer_locations\":[\"Abdomen\",\"Mediastinum\"],\"data_types\":[\"CT\",\"SEG\",\"Measurement\",\"Other\",\"Segmentation\"],\"supporting_data\":[\"Image Analyses\",\"Organ segmentations\"],\"species\":[\"Human\"],\"subjects\":176,\"date_updated\":\"2023-03-31\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=CT%20Lymph%20Nodes\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_CT_Lymph_Nodes_03-31-2023.tcia\",\"blob\":\"a new 2.5 d representation for lymph node detection in ct ct lymph nodes this collection consists of computed tomography (ct) images of the mediastinum and abdomen in which lymph node positions are marked by radiologists at the national institutes of health, clinical center. radiologists at the   imaging biomarkers and computer-aided diagnosis laboratory   labeled a total of 388 mediastinal lymph nodes in ct images of 90 patients and a total of 595 abdominal lymph nodes in 86 patients.the collection is aimed at the medical image computing community for developing and assessing computer-aided detection methods. automated detection of lymph nodes can be an important clinical diagnostic tool but is very challenging due to the low contrast of surrounding structures in ct and to their varying sizes, poses, shapes and sparsely distributed locations. this data set is made available to make direct comparison to other detection methods in order to advance the state of the art. lymphadenopathy (non-cancer) abdomen mediastinum ct seg measurement other segmentation image analyses organ segmentations community\"},{\"id\":42081,\"type\":\"collection\",\"slug\":\"ct-org\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ct-org\\\/\",\"title\":\"CT volumes with multiple organ segmentations\",\"short_title\":\"CT-ORG\",\"doi\":\"10.7937\\\/tcia.2019.tt7f4v7o\",\"summary\":\"This dataset consists of 140 computed tomography (CT) scans, each with five organs labeled in 3D: lung, bones, liver, kidneys and bladder. The brain is also labeled on the minority of scans which show it.Patients were included based on the presence of lesions in one or more of the labeled organs. Most of the images exhibit liver lesions, both benign and malignant. Some also exhibit metastatic disease in other organs such as bones and lungs.The images come from a wide variety of sources, including abdominal and full-body; contrast and non-contrast; low-dose and high-dose CT scans. 131 images are dedicated CTs, the remaining 9 are the CT component taken from PET-CT exams. This makes the dataset ideal for training and evaluating organ segmentation algorithms, which ought to perform well in a wide variety of imaging conditions.The dataset includes large and easily-located organs such as the lungs, as well as small and difficult ones like the bladder. We hope the dataset will enable widespread adoption of multi-class organ segmentation, as well as competitive benchmarking of algorithms for it.The data are divided into a testing set of 21 CT scans, and a training set of the remaining 119. For the training set, the lungs and bones were automatically segmented by morphological image processing. For the testing set, the lungs and bones were segmented manually. All other organs were segmented manually in both the training and testing sets. Manual segmentations were done with ITK-SNAP (https:\\\/\\\/www.itksnap.org), starting with semi-automatic active contour segmentation followed by manual clean-up. The source code for the morphological algorithms is available at:- https:\\\/\\\/github.com\\\/bbrister\\\/ctOrganSegmentation.gitMany images were borrowed from the Liver Tumor Segmentation (LiTS) challenge, which the organizers have generously allowed us to distribute. For more information, see the following website and paper:- https:\\\/\\\/lits-challenge.com (redirects to https:\\\/\\\/competitions.codalab.org\\\/competitions\\\/17094 )- Arxiv [1901.04056] The Liver Tumor Segmentation Benchmark (LiTS) (https:\\\/\\\/arxiv.org\\\/abs\\\/1901.04056)\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Liver\"],\"cancer_locations\":[\"Bladder\",\"Bone\",\"Brain\",\"Kidney\",\"Liver\",\"Lung\"],\"data_types\":[\"Segmentation\",\"CT\",\"Classification\",\"Other\"],\"supporting_data\":[\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":140,\"date_updated\":\"2020-01-09\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjU5MiIsInBhc3Njb2RlIjoiMDNkOWZmMzE2ZmE3NmQ3YzlmZmEyZWFhZDg2ZjkxNTJjZTFhY2FhNSIsInBhY2thZ2VfaWQiOiI1OTIiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"ct volumes with multiple organ segmentations ct-org this dataset consists of 140 computed tomography (ct) scans, each with five organs labeled in 3d: lung, bones, liver, kidneys and bladder. the brain is also labeled on the minority of scans which show it.patients were included based on the presence of lesions in one or more of the labeled organs. most of the images exhibit liver lesions, both benign and malignant. some also exhibit metastatic disease in other organs such as bones and lungs.the images come from a wide variety of sources, including abdominal and full-body; contrast and non-contrast; low-dose and high-dose ct scans. 131 images are dedicated cts, the remaining 9 are the ct component taken from pet-ct exams. this makes the dataset ideal for training and evaluating organ segmentation algorithms, which ought to perform well in a wide variety of imaging conditions.the dataset includes large and easily-located organs such as the lungs, as well as small and difficult ones like the bladder. we hope the dataset will enable widespread adoption of multi-class organ segmentation, as well as competitive benchmarking of algorithms for it.the data are divided into a testing set of 21 ct scans, and a training set of the remaining 119. for the training set, the lungs and bones were automatically segmented by morphological image processing. for the testing set, the lungs and bones were segmented manually. all other organs were segmented manually in both the training and testing sets. manual segmentations were done with itk-snap (https:\\\/\\\/www.itksnap.org), starting with semi-automatic active contour segmentation followed by manual clean-up. the source code for the morphological algorithms is available at:- https:\\\/\\\/github.com\\\/bbrister\\\/ctorgansegmentation.gitmany images were borrowed from the liver tumor segmentation (lits) challenge, which the organizers have generously allowed us to distribute. for more information, see the following website and paper:- https:\\\/\\\/lits-challenge.com (redirects to https:\\\/\\\/competitions.codalab.org\\\/competitions\\\/17094 )- arxiv [1901.04056] the liver tumor segmentation benchmark (lits) (https:\\\/\\\/arxiv.org\\\/abs\\\/1901.04056) liver bladder bone brain kidney liver lung segmentation ct classification other software\\\/source code community\"},{\"id\":43717,\"type\":\"collection\",\"slug\":\"ct-phantom4radiomics\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ct-phantom4radiomics\\\/\",\"title\":\"Task-Based Anthropomorphic CT Phantom for Radiomics Stability and Discriminatory Power Analyses\",\"short_title\":\"CT-Phantom4Radiomics\",\"doi\":\"10.7937\\\/a1v1-rc66\",\"summary\":\"The aims of this dataset are to determine the stability of radiomics features against computed tomography (CT) parameter variations and to study their discriminative power concerning tissue classification using a 3D-printed CT phantom based on real patient data. A radiopaque 3D phantom was developed using real patient data and a potassium iodide solution paper-printing technique. Normal liver tissue and 3 lesion types (benign cyst, hemangioma, and metastasis) were manually annotated in the phantom. 238 CT series with 8 parameter variations of reconstruction algorithms, reconstruction kernels, slice thickness, and slice spacing are available.In a\\u00a0preliminary study, we showed that the 8 CT parameter variation pairwise group comparisons had statistically significant differences on average in 78\\\/86 radiomics features. On the other hand, 84% of the univariate radiomics feature tests had a successful and statistically significant differentiation of the 4 classes of liver tissue. We concluded that the differences in radiomics feature values obtained from different types of liver tissue are generally greater than the intraclass differences resulting from CT parameter variations.The phantom was printed using the\\u00a0Phantom X solution\\u00a0detailed in the study, \\\"Radiopaque Three-dimensional Printing: A Method to Create Realistic CT Phantoms\\\". It relies on inkjet cartridges filled with potassium iodide solutions (600 mg\\\/mL) with prints realized on plain paper (80 g\\\/m2). Stacked paper sheets result in three-dimensional phantoms.\\u00a0 Because the printing process is very specific to the Phantom X company and no other printer can be used, we did not make the printer creation file publicly available.This dataset can serve as a reference to assess both the discriminative power and stability of radiomics features with CT variations. In addition, it can be used to develop efficient data harmonization techniques to improve robustness of (deep) radiomics features and models.\\u00a0 Additional information and source code related to the project can be found at\\u00a0https:\\\/\\\/qa4iqi.github.io\\\/.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CT-Phantom4Radiomics.png\",\"program\":\"Community\",\"cancer_types\":[\"Phantom\"],\"cancer_locations\":[\"Phantom\"],\"data_types\":[\"CT\",\"SEG\"],\"supporting_data\":[\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":1,\"date_updated\":\"2023-08-23\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=CT-Phantom4Radiomics\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CT-Phantom4Radiomicsv1.tcia\",\"blob\":\"task-based anthropomorphic ct phantom for radiomics stability and discriminatory power analyses ct-phantom4radiomics the aims of this dataset are to determine the stability of radiomics features against computed tomography (ct) parameter variations and to study their discriminative power concerning tissue classification using a 3d-printed ct phantom based on real patient data. a radiopaque 3d phantom was developed using real patient data and a potassium iodide solution paper-printing technique. normal liver tissue and 3 lesion types (benign cyst, hemangioma, and metastasis) were manually annotated in the phantom. 238 ct series with 8 parameter variations of reconstruction algorithms, reconstruction kernels, slice thickness, and slice spacing are available.in a\\u00a0preliminary study, we showed that the 8 ct parameter variation pairwise group comparisons had statistically significant differences on average in 78\\\/86 radiomics features. on the other hand, 84% of the univariate radiomics feature tests had a successful and statistically significant differentiation of the 4 classes of liver tissue. we concluded that the differences in radiomics feature values obtained from different types of liver tissue are generally greater than the intraclass differences resulting from ct parameter variations.the phantom was printed using the\\u00a0phantom x solution\\u00a0detailed in the study, \\\"radiopaque three-dimensional printing: a method to create realistic ct phantoms\\\". it relies on inkjet cartridges filled with potassium iodide solutions (600 mg\\\/ml) with prints realized on plain paper (80 g\\\/m2). stacked paper sheets result in three-dimensional phantoms.\\u00a0 because the printing process is very specific to the phantom x company and no other printer can be used, we did not make the printer creation file publicly available.this dataset can serve as a reference to assess both the discriminative power and stability of radiomics features with ct variations. in addition, it can be used to develop efficient data harmonization techniques to improve robustness of (deep) radiomics features and models.\\u00a0 additional information and source code related to the project can be found at\\u00a0https:\\\/\\\/qa4iqi.github.io\\\/.\\u00a0 phantom phantom ct seg image analyses software\\\/source code community\"},{\"id\":42069,\"type\":\"collection\",\"slug\":\"ct-vs-pet-ventilation-imaging\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ct-vs-pet-ventilation-imaging\\\/\",\"title\":\"CT Ventilation as a Functional Imaging Modality for Lung Cancer Radiotherapy\",\"short_title\":\"CT-vs-PET-Ventilation-Imaging\",\"doi\":\"10.7937\\\/3ppx-7s22\",\"summary\":\"For this study, 20 lung cancer patients underwent exhale\\\/inhale breath hold CT (BHCT), free-breathing four-dimensional CT (4DCT) and Galligas PET ventilation scans in a single session on a combined 4DPET\\\/CT scanner. The purpose of the study was to enable comparisons between: (i) CT ventilation images derived from exhale\\\/inhale BHCT scans, (ii) CT ventilation images derived from free-breathing 4DCT scans, and (iii) Galligas PET (nuclear medicine) ventilation scans.\\u00a0This dataset can build the international capacity for prototyping and evaluating new CT ventilation imaging technologies.All image acquisitions were performed on a Siemens Biograph mCT.S\\\/64 PET\\\/CT scanner (Siemens, Knoxville, USA) at the Royal North Shore Hospital between 2013 and 2015. A total of 20 4DCT scans, 20 inhale\\\/exhale BHCT scans, 20 Galligas PET scans and 19 attenuation CT scans (missing for CT-PET-VI-07) were successfully acquired for the 20 patients and included in this dataset. \\u00a0Real World Value Mapping files generated with the PET scans have been included. This DICOM file type is normally used to convert activity to estimated tissue uptake Standardized Uptake Values (SUVs) assuming tissue density of 1g\\\/mL. However, for this study radiotracer was not injected intravenously into tissue, but inhaled into the lung where this assumption does not hold, so these files should be used with cautionFor the exhale\\\/inhale BHCT scans, patients were instructed to hold their breath at approximately 80% of maximum inhalation and exhalation, with Audiovisual Biofeedback used to help guide the breath hold procedure. Settings for the BHCT image acquisitions were: 120 kVp, 120 mAs, 0.8 pitch with a breath-hold time of 10s. The field of view for the CT images was approximately 50 cm from the pharynx to the stomach. Meanwhile the 4DCT scans were acquired with the use of a respiratory motion sensor, the Anzai AZ-733V system (Anzai Medical Co., Tokyo, Japan) for retrospective sorting of CT slices into 10 respiratory phase bins; the exhale and inhale phase bins are provided with the present dataset. 4DCT scans were performed using a helical acquisition and tube settings 120 kVp, 80-200 mA, with 0.5s gantry rotation and 0.09 pitch. The Galligas PET scans (and corresponding attenuation correction CT) were acquired under free-breathing using a standard non-gated protocol. Galligas PET scans were acquired at 2 bed positions of 5 min each, with attenuation correction using a low dose CT (120 kVp; 0.8 pitch, 50 mAs).This study was a prospective single institution clinical trial approved by the health district ethics committee, (HREC\\\/12\\\/169) and registered with the Australian New Zealand Clinical Trials Registry (ACTRN12612000775819).\\u00a0The following are known limitations of this dataset:The PET scan for CT-PET-VI-15 truncates the inferior part of the lungThe 4DCT scans for CT-PET-VI-07 & CT-PET-VI-11 truncate the inferior part of the lungThe BHCT scans for CT-PET-VI-02 & CT-PET-VI-03 show very little motion between inhale and exhaleThe BHCT scans for CT-PET-VI-05 have a different number of slices between the inhale and exhale\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Figure-1.png\",\"program\":\"Community\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"PT\",\"RWV\",\"Measurement\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":20,\"date_updated\":\"2022-08-25\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=CT-vs-PET-Ventilation-Imaging\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CT-vs-PET-Ventilation-Imaging-manifest.tcia\",\"blob\":\"ct ventilation as a functional imaging modality for lung cancer radiotherapy ct-vs-pet-ventilation-imaging for this study, 20 lung cancer patients underwent exhale\\\/inhale breath hold ct (bhct), free-breathing four-dimensional ct (4dct) and galligas pet ventilation scans in a single session on a combined 4dpet\\\/ct scanner. the purpose of the study was to enable comparisons between: (i) ct ventilation images derived from exhale\\\/inhale bhct scans, (ii) ct ventilation images derived from free-breathing 4dct scans, and (iii) galligas pet (nuclear medicine) ventilation scans.\\u00a0this dataset can build the international capacity for prototyping and evaluating new ct ventilation imaging technologies.all image acquisitions were performed on a siemens biograph mct.s\\\/64 pet\\\/ct scanner (siemens, knoxville, usa) at the royal north shore hospital between 2013 and 2015. a total of 20 4dct scans, 20 inhale\\\/exhale bhct scans, 20 galligas pet scans and 19 attenuation ct scans (missing for ct-pet-vi-07) were successfully acquired for the 20 patients and included in this dataset. \\u00a0real world value mapping files generated with the pet scans have been included. this dicom file type is normally used to convert activity to estimated tissue uptake standardized uptake values (suvs) assuming tissue density of 1g\\\/ml. however, for this study radiotracer was not injected intravenously into tissue, but inhaled into the lung where this assumption does not hold, so these files should be used with cautionfor the exhale\\\/inhale bhct scans, patients were instructed to hold their breath at approximately 80% of maximum inhalation and exhalation, with audiovisual biofeedback used to help guide the breath hold procedure. settings for the bhct image acquisitions were: 120 kvp, 120 mas, 0.8 pitch with a breath-hold time of 10s. the field of view for the ct images was approximately 50 cm from the pharynx to the stomach. meanwhile the 4dct scans were acquired with the use of a respiratory motion sensor, the anzai az-733v system (anzai medical co., tokyo, japan) for retrospective sorting of ct slices into 10 respiratory phase bins; the exhale and inhale phase bins are provided with the present dataset. 4dct scans were performed using a helical acquisition and tube settings 120 kvp, 80-200 ma, with 0.5s gantry rotation and 0.09 pitch. the galligas pet scans (and corresponding attenuation correction ct) were acquired under free-breathing using a standard non-gated protocol. galligas pet scans were acquired at 2 bed positions of 5 min each, with attenuation correction using a low dose ct (120 kvp; 0.8 pitch, 50 mas).this study was a prospective single institution clinical trial approved by the health district ethics committee, (hrec\\\/12\\\/169) and registered with the australian new zealand clinical trials registry (actrn12612000775819).\\u00a0the following are known limitations of this dataset:the pet scan for ct-pet-vi-15 truncates the inferior part of the lungthe 4dct scans for ct-pet-vi-07 & ct-pet-vi-11 truncate the inferior part of the lungthe bhct scans for ct-pet-vi-02 & ct-pet-vi-03 show very little motion between inhale and exhalethe bhct scans for ct-pet-vi-05 have a different number of slices between the inhale and exhale lung cancer lung ct pt rwv measurement community\"},{\"id\":43135,\"type\":\"collection\",\"slug\":\"ctpred-sunitinib-pannet\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ctpred-sunitinib-pannet\\\/\",\"title\":\"Prediction of Sunitinib Efficacy using Computed Tomography in Patients with Pancreatic Neuroendocrine Tumors\",\"short_title\":\"CTpred-Sunitinib-panNET\",\"doi\":\"10.7937\\\/SPGK-0P94\",\"summary\":\"Clinically effective methods to predict the efficacy of sunitinib, for patients with metastatic or locally advanced pancreatic neuroendocrine tumors (panNET) are scarce, making precision treatment difficult. This study aimed to develop and validate a computed tomography (CT)-based method to predict the efficacy of sunitinib in patients with panNET. \\n\\nPretreatment CT images of 171 lesions from 38 patients with panNET were included. Clinical information including sex, age at diagnosis, progression-free survival of sunitnib treatment, ki-67 index, tumor grade and previous treatment before sunitinib were also collected.\\u00a0 CT value ratio (CT value of tumor\\\/CT value of abdominal aorta from the same patient) and radiomics features were extracted for model development. Receiver operating curve (ROC) with area under the curve (AUC) and decision curve analysis (DCA) were used to evaluate the proposed model. \\nTumor shrinkage of >10% at first follow-up after sunitinib treatment was significantly associated with longer progression-free survival (PFS; P<0.001) and was used as the major treatment outcome. The CT value ratio could predict tumor shrinkage with AUC of 0.759 (95% confidence interval [CI], 0.685\\u20130.833). We then developed a radiomics signature, which showed significantly higher AUC in training (0.915; 95% CI, 0.866\\u20130.964) and validation (0.770; 95% CI, 0.584\\u20130.956) sets than CT value ratio. DCA also confirmed the clinical utility of the model. Subgroup analysis showed that this radiomics signature had a high accuracy in predicting tumor shrinkage both for primary and metastatic tumors, and for treatment-naive and pretreated tumors. Survival analysis showed that radiomics signature correlated with PFS (P=0.020). The proposed radiomics-based model accurately predicted tumor shrinkage and PFS in patients with panNET receiving sunitinib and may help select patients suitable for sunitinib treatment.\\nPancreatic neuroendocrine tumors is a rare group of tumor. The dataset can be used to validate the findings of our study. More importantly, researchers can use this dataset to study the imaging characteristics of pancreatic neuroendocrine tumors.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/flowchart.png\",\"program\":\"Community\",\"cancer_types\":[\"Pancreas Cancer\"],\"cancer_locations\":[\"Pancreas\"],\"data_types\":[\"CT\",\"Demographic\",\"Classification\",\"Follow-Up\",\"Molecular Test\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":38,\"date_updated\":\"2022-09-12\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=CTpred-Sunitinib-panNET\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CTpred-Sunitinib-panNET-manifest.tcia\",\"blob\":\"prediction of sunitinib efficacy using computed tomography in patients with pancreatic neuroendocrine tumors ctpred-sunitinib-pannet clinically effective methods to predict the efficacy of sunitinib, for patients with metastatic or locally advanced pancreatic neuroendocrine tumors (pannet) are scarce, making precision treatment difficult. this study aimed to develop and validate a computed tomography (ct)-based method to predict the efficacy of sunitinib in patients with pannet. \\n\\npretreatment ct images of 171 lesions from 38 patients with pannet were included. clinical information including sex, age at diagnosis, progression-free survival of sunitnib treatment, ki-67 index, tumor grade and previous treatment before sunitinib were also collected.\\u00a0 ct value ratio (ct value of tumor\\\/ct value of abdominal aorta from the same patient) and radiomics features were extracted for model development. receiver operating curve (roc) with area under the curve (auc) and decision curve analysis (dca) were used to evaluate the proposed model. \\ntumor shrinkage of >10% at first follow-up after sunitinib treatment was significantly associated with longer progression-free survival (pfs; p<0.001) and was used as the major treatment outcome. the ct value ratio could predict tumor shrinkage with auc of 0.759 (95% confidence interval [ci], 0.685\\u20130.833). we then developed a radiomics signature, which showed significantly higher auc in training (0.915; 95% ci, 0.866\\u20130.964) and validation (0.770; 95% ci, 0.584\\u20130.956) sets than ct value ratio. dca also confirmed the clinical utility of the model. subgroup analysis showed that this radiomics signature had a high accuracy in predicting tumor shrinkage both for primary and metastatic tumors, and for treatment-naive and pretreated tumors. survival analysis showed that radiomics signature correlated with pfs (p=0.020). the proposed radiomics-based model accurately predicted tumor shrinkage and pfs in patients with pannet receiving sunitinib and may help select patients suitable for sunitinib treatment.\\npancreatic neuroendocrine tumors is a rare group of tumor. the dataset can be used to validate the findings of our study. more importantly, researchers can use this dataset to study the imaging characteristics of pancreatic neuroendocrine tumors. pancreas cancer pancreas ct demographic classification follow-up molecular test community\"},{\"id\":48455,\"type\":\"collection\",\"slug\":\"dfci-bch-bwh-peds-hgg\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/dfci-bch-bwh-peds-hgg\\\/\",\"title\":\"MR imaging of pediatric subjects with high-grade gliomas\",\"short_title\":\"DFCI-BCH-BWH-PEDs-HGG\",\"doi\":\"10.7937\\\/v8h6-bg25\",\"summary\":\"Background:\\u00a0Pediatric tumors of the central nervous system are the leading cause of cancer-related death among children. High-grade gliomas (HGGs) in children have a five-year survival rate of less than 20%. Due to their rarity, diagnosis is often delayed, treatment strategies rely on historical protocols, and clinical trials necessitate collaboration across multiple institutions.Summary:\\u00a0The DFCI-BCH-BWH-PEDs-HGG 2023 dataset is a comprehensive database of pediatric high-grade gliomas. This dataset includes a retrospective, multi-institutional cohort of conventional\\\/structural magnetic resonance imaging (MRI) sequences, featuring pre- and post-gadolinium T1-weighted (labeled as T1 and T1CE), T2-weighted (T2), and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR) images. These data were collected from Boston Children\\u2019s Hospital, Dana-Farber Cancer Institute, and Brigham and Women\\u2019s Hospital, encompassing a total of 61 cases. The DFCI-BCH-BWH-PEDs-HGG 2023\\u00a0dataset is a subset of the BraTS-PEDs 2023 challenge\\u00a0as described in this manuscript.Inclusion criteria consisted of (pediatric, adolescent, young adult) subjects with histologically-approved high-grade glioma, i.e., high-grade astrocytoma and diffuse midline glioma (DMG), including radiologically or histologically-proven diffuse intrinsic pontine glioma (DIPG), diagnostic or pretreatment imaging.Exclusion criteria consisted of (1) Images assessed to be of low quality or with artifacts that would not allow for reliable tumor segmentation, (2) Infants younger than one month of age.Defacing was done using the Pydeface Python algorithm.The corresponding T1, T1CE, T2, and T2-FLAIR expert segmentations will be released as part of a future publication from the BRATS-PEDs organizers.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"High Grade Glioma\",\"Diffuse Midline Glioma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":61,\"date_updated\":\"2024-07-16\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjkyNiIsInBhc3Njb2RlIjoiNjZjYmMzYzBkYjU2YTViZmNkMzMwYzc1ZDZkMTE0YjI2Y2E1OTgzNyIsInBhY2thZ2VfaWQiOiI5MjYiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"mr imaging of pediatric subjects with high-grade gliomas dfci-bch-bwh-peds-hgg background:\\u00a0pediatric tumors of the central nervous system are the leading cause of cancer-related death among children. high-grade gliomas (hggs) in children have a five-year survival rate of less than 20%. due to their rarity, diagnosis is often delayed, treatment strategies rely on historical protocols, and clinical trials necessitate collaboration across multiple institutions.summary:\\u00a0the dfci-bch-bwh-peds-hgg 2023 dataset is a comprehensive database of pediatric high-grade gliomas. this dataset includes a retrospective, multi-institutional cohort of conventional\\\/structural magnetic resonance imaging (mri) sequences, featuring pre- and post-gadolinium t1-weighted (labeled as t1 and t1ce), t2-weighted (t2), and t2-weighted fluid-attenuated inversion recovery (t2-flair) images. these data were collected from boston children\\u2019s hospital, dana-farber cancer institute, and brigham and women\\u2019s hospital, encompassing a total of 61 cases. the dfci-bch-bwh-peds-hgg 2023\\u00a0dataset is a subset of the brats-peds 2023 challenge\\u00a0as described in this manuscript.inclusion criteria consisted of (pediatric, adolescent, young adult) subjects with histologically-approved high-grade glioma, i.e., high-grade astrocytoma and diffuse midline glioma (dmg), including radiologically or histologically-proven diffuse intrinsic pontine glioma (dipg), diagnostic or pretreatment imaging.exclusion criteria consisted of (1) images assessed to be of low quality or with artifacts that would not allow for reliable tumor segmentation, (2) infants younger than one month of age.defacing was done using the pydeface python algorithm.the corresponding t1, t1ce, t2, and t2-flair expert segmentations will be released as part of a future publication from the brats-peds organizers. high grade glioma diffuse midline glioma brain mr other community\"},{\"id\":42427,\"type\":\"collection\",\"slug\":\"dlbcl-morphology\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/dlbcl-morphology\\\/\",\"title\":\"H&E and immunohistochemical stain images of 209 cases of diffuse large B-cell lymphoma linked with cytogenetic features and clinical outcomes\",\"short_title\":\"DLBCL-Morphology\",\"doi\":\"10.7937\\\/NVA3-N783\",\"summary\":\"\\u00a0Diffuse Large B-Cell Lymphoma (DLBCL) is the most common non-Hodgkin lymphoma worldwide. DLBCL is fatal without treatment, but early detection and therapy can cure up to 70% of patients. The current best prognostic classification, the National Comprehensive Cancer Network International Prognostic Index, is insufficient to guide therapeutic decision-making for individual patients. No tumor-intrinsic prognostication method is currently available.\\u00a0The DLBCL-Morph dataset contains 42 digital high-magnification scans of tissue microarrays (TMAs) containing tissue cores from 209 DLBCL cases at Stanford Hospital. Each DLBCL case is accompanied by survival data, follow-up status and a wide variety of clinical and cytogenetic variables. The TMAs are stained for H&E, which shows cell morphology, as well as for the expression of several prognostically relevant proteins: CD10, BCL6, MUM1, BCL2, and MYC. The TMAs are accompanied by pathologist-annotated regions of interest (ROIs) that specify areas of tissue representative of DLBCL. We used deep learning to segment out cancerous nuclei from the ROIs, and computed several geometric features for each cancerous nucleus, which are provided as part of our dataset. These\\u00a0geometric features quantify several morphologic properties of a nucleus, such as size and elongation, and can be used as input for automated prognostic models to predict survival. In addition, DLBCL-Morph contains 204 digital high-magnification whole-slide images (WSIs) from 149 DLBCL cases, stained for H&E.A total of 152,194 patches (240x240 pixels each) were extracted from the H&E stained ROIs and a HoVer-Net model was used to segment tumor nuclei (1,035,909 binary masks).\\u00a0\\u00a0Geometric descriptors were computed for each segmented nucleus and a Cox proportional hazards model was evaluated using A) only clinical features, B) only morphologic features, or C) both sets of features. The Cox model achieved a concordance index of A) 0.703 (p = 0.005) B) 0.645 (p = 0.07), and C) 0.723 (p < 0.001) on a randomly sampled validation set of 51 patients. Our findings suggest that a risk calculator based on both clinical and morphologic data could yield improved prognostic value for DLBCL without the need for additional diagnostic testing.Several studies have thus far failed to conclusively demonstrate that morphologic classification can predict outcomes in DLBCL. Automated medical imaging methods on whole slide images (WSI) could potentially identify novel, prognostically significant morphological or immunohistochemical biomarkers. The ability of automated methods to identify prognostically relevant features on H&E sections that have eluded pathologists has been demonstrated (Beck et al Science Translational Med). Furthermore, if successful, automated image analysis could potentially be scaled up into a cost-effective alternative to current classification methods which are typically costly and\\\/or labor intensive. A critical requirement for the development of such deep learning models is the availability of datasets containing WSIs appropriately stained to show cell morphology and oncogene expression, with accompanying prognostic outcome data.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/dlbcl-morph-layout.png\",\"program\":\"Community\",\"cancer_types\":[\"Diffuse Large B-Cell Lymphoma\"],\"cancer_locations\":[\"Lymph Node\"],\"data_types\":[\"Histopathology\",\"Follow-Up\",\"Molecular Test\",\"Immunohistochemistry\",\"Diagnosis\",\"Other\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":209,\"date_updated\":\"2022-03-25\",\"status\":\"Complete\",\"license\":\"CC BY-NC 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22DLBCL-Morphology%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjYxNSIsInBhc3Njb2RlIjoiNDU1OWE3ZmJlMWJhMzkzOTQyZDJkNDFkYzRmYmFkMWYyOWY3ZjhmNyIsInBhY2thZ2VfaWQiOiI2MTUiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"h&e and immunohistochemical stain images of 209 cases of diffuse large b-cell lymphoma linked with cytogenetic features and clinical outcomes dlbcl-morphology \\u00a0diffuse large b-cell lymphoma (dlbcl) is the most common non-hodgkin lymphoma worldwide. dlbcl is fatal without treatment, but early detection and therapy can cure up to 70% of patients. the current best prognostic classification, the national comprehensive cancer network international prognostic index, is insufficient to guide therapeutic decision-making for individual patients. no tumor-intrinsic prognostication method is currently available.\\u00a0the dlbcl-morph dataset contains 42 digital high-magnification scans of tissue microarrays (tmas) containing tissue cores from 209 dlbcl cases at stanford hospital. each dlbcl case is accompanied by survival data, follow-up status and a wide variety of clinical and cytogenetic variables. the tmas are stained for h&e, which shows cell morphology, as well as for the expression of several prognostically relevant proteins: cd10, bcl6, mum1, bcl2, and myc. the tmas are accompanied by pathologist-annotated regions of interest (rois) that specify areas of tissue representative of dlbcl. we used deep learning to segment out cancerous nuclei from the rois, and computed several geometric features for each cancerous nucleus, which are provided as part of our dataset. these\\u00a0geometric features quantify several morphologic properties of a nucleus, such as size and elongation, and can be used as input for automated prognostic models to predict survival. in addition, dlbcl-morph contains 204 digital high-magnification whole-slide images (wsis) from 149 dlbcl cases, stained for h&e.a total of 152,194 patches (240x240 pixels each) were extracted from the h&e stained rois and a hover-net model was used to segment tumor nuclei (1,035,909 binary masks).\\u00a0\\u00a0geometric descriptors were computed for each segmented nucleus and a cox proportional hazards model was evaluated using a) only clinical features, b) only morphologic features, or c) both sets of features. the cox model achieved a concordance index of a) 0.703 (p = 0.005) b) 0.645 (p = 0.07), and c) 0.723 (p < 0.001) on a randomly sampled validation set of 51 patients. our findings suggest that a risk calculator based on both clinical and morphologic data could yield improved prognostic value for dlbcl without the need for additional diagnostic testing.several studies have thus far failed to conclusively demonstrate that morphologic classification can predict outcomes in dlbcl. automated medical imaging methods on whole slide images (wsi) could potentially identify novel, prognostically significant morphological or immunohistochemical biomarkers. the ability of automated methods to identify prognostically relevant features on h&e sections that have eluded pathologists has been demonstrated (beck et al science translational med). furthermore, if successful, automated image analysis could potentially be scaled up into a cost-effective alternative to current classification methods which are typically costly and\\\/or labor intensive. a critical requirement for the development of such deep learning models is the availability of datasets containing wsis appropriately stained to show cell morphology and oncogene expression, with accompanying prognostic outcome data. diffuse large b-cell lymphoma lymph node histopathology follow-up molecular test immunohistochemistry diagnosis other clinical image analyses community\"},{\"id\":43681,\"type\":\"collection\",\"slug\":\"dro-toolkit\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/dro-toolkit\\\/\",\"title\":\"Stanford DRO Toolkit: Digital Reference Objects for Standardization of Radiomic Features\",\"short_title\":\"DRO-Toolkit\",\"doi\":\"10.7937\\\/T062-8262\",\"summary\":\"This is a sample collection of synthetic 3D Digital Reference Objects (DROs) intended for standardization of quantitative imaging feature extraction pipelines. We have developed a software toolkit for the creation of DROs with customizable size, shape, intensity, texture, and margin sharpness values. Using user-supplied input parameters, these objects are defined mathematically as continuous functions, discretized, and then saved as DICOM objects. This collection includes objects with a range of values for the various feature categories and many combinations of these categories.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Phantom\"],\"cancer_locations\":[\"Phantom\"],\"data_types\":[\"CT\",\"SEG\",\"Segmentation\"],\"supporting_data\":[\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":32,\"date_updated\":\"2020-04-09\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=DRO-Toolkit\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/DRO-Toolkit-April-2020-TCIA-manifest.tcia\",\"blob\":\"stanford dro toolkit: digital reference objects for standardization of radiomic features dro-toolkit this is a sample collection of synthetic 3d digital reference objects (dros) intended for standardization of quantitative imaging feature extraction pipelines. we have developed a software toolkit for the creation of dros with customizable size, shape, intensity, texture, and margin sharpness values. using user-supplied input parameters, these objects are defined mathematically as continuous functions, discretized, and then saved as dicom objects. this collection includes objects with a range of values for the various feature categories and many combinations of these categories. phantom phantom ct seg segmentation software\\\/source code community\"},{\"id\":42255,\"type\":\"collection\",\"slug\":\"duke-breast-cancer-mri\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/duke-breast-cancer-mri\\\/\",\"title\":\"Dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations\",\"short_title\":\"Duke-Breast-Cancer-MRI\",\"doi\":\"10.7937\\\/TCIA.e3sv-re93\",\"summary\":\"Breast MRI is a common image modality to assess the extent of disease in breast cancer patients. Recent studies show that MRI has a potential in prognosis of patients\\u2019 short and long-term outcomes as well as predicting pathological and genomic features of the tumors. However, large, well annotated datasets are needed to make further progress in the field. We share such a dataset here.In terms of design, the dataset is a single-institutional, retrospective collection of 922 biopsy-confirmed invasive breast cancer patients, over a decade, having the following data components:Demographic, clinical, pathology, treatment, outcomes, and genomic data: Collected from a variety of sources including clinical notes, radiology report, and pathology reports and has served as a source for multiple published papers on radiogenomics, outcomes prediction, and other areas.Pre-operative dynamic contrast enhanced (DCE)-MRI:\\u00a0Downloaded from PACS systems and de-identified for The Cancer Imaging Archive (TCIA) release. These include axial breast MRI images acquired by 1.5T or 3T scanners in the prone positions. Following MRI sequences are shared in DICOM format: a non-fat saturated T1-weighted sequence, a fat-saturated gradient echo T1-weighted pre-contrast sequence, and mostly three to four post-contrast sequences.Locations of lesions in DCE-MRI:\\u00a0Annotations on the DCE-MRI images by radiologists.Imaging features from DCE-MRI:\\u00a0A set of 529 computer-extracted imaging features by inhouse software. These features represent a variety of imaging characteristics including size, shape, texture, and enhancement of both the tumor and the surrounding tissue, which is combined of features commonly published in the literature, as well as the features developed in our lab.If you use this dataset, please cite the following publication:Publication CitationSaha, A., Harowicz, M.R., Grimm, L.J., Kim, C.E., Ghate, S.V., Walsh, R. and Mazurowski, M.A., 2018. A machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 DCE-MRI features. British journal of cancer, 119(4), pp.508-516.\\u00a0 \\u00a0(A free version of this paper is available here: PMC6134102)For more information see this site\\u00a0https:\\\/\\\/sites.duke.edu\\\/mazurowski\\\/resources\\\/breast-cancer-mri-dataset\\\/, the related publication, or contact TCIA Helpdesk at\\u00a0help@cancerimagingarchive.net. \\u00a0Please visit this discussion forum for any questions related to the data:\\u00a0https:\\\/\\\/www.reddit.com\\\/r\\\/DukeDCEMRIData\\\/.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MR\",\"SEG\",\"Other\",\"Classification\",\"Follow-Up\",\"Radiomic Feature\",\"Treatment\",\"Measurement\",\"Segmentation\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":922,\"date_updated\":\"2022-08-01\",\"status\":\"Complete\",\"license\":\"CC BY-NC 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Duke-Breast-Cancer-MRI\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Duke-Breast-Cancer-MRI_v2_20220609.tcia\",\"blob\":\"dynamic contrast-enhanced magnetic resonance images of breast cancer patients with tumor locations duke-breast-cancer-mri breast mri is a common image modality to assess the extent of disease in breast cancer patients. recent studies show that mri has a potential in prognosis of patients\\u2019 short and long-term outcomes as well as predicting pathological and genomic features of the tumors. however, large, well annotated datasets are needed to make further progress in the field. we share such a dataset here.in terms of design, the dataset is a single-institutional, retrospective collection of 922 biopsy-confirmed invasive breast cancer patients, over a decade, having the following data components:demographic, clinical, pathology, treatment, outcomes, and genomic data: collected from a variety of sources including clinical notes, radiology report, and pathology reports and has served as a source for multiple published papers on radiogenomics, outcomes prediction, and other areas.pre-operative dynamic contrast enhanced (dce)-mri:\\u00a0downloaded from pacs systems and de-identified for the cancer imaging archive (tcia) release. these include axial breast mri images acquired by 1.5t or 3t scanners in the prone positions. following mri sequences are shared in dicom format: a non-fat saturated t1-weighted sequence, a fat-saturated gradient echo t1-weighted pre-contrast sequence, and mostly three to four post-contrast sequences.locations of lesions in dce-mri:\\u00a0annotations on the dce-mri images by radiologists.imaging features from dce-mri:\\u00a0a set of 529 computer-extracted imaging features by inhouse software. these features represent a variety of imaging characteristics including size, shape, texture, and enhancement of both the tumor and the surrounding tissue, which is combined of features commonly published in the literature, as well as the features developed in our lab.if you use this dataset, please cite the following publication:publication citationsaha, a., harowicz, m.r., grimm, l.j., kim, c.e., ghate, s.v., walsh, r. and mazurowski, m.a., 2018. a machine learning approach to radiogenomics of breast cancer: a study of 922 subjects and 529 dce-mri features. british journal of cancer, 119(4), pp.508-516.\\u00a0 \\u00a0(a free version of this paper is available here: pmc6134102)for more information see this site\\u00a0https:\\\/\\\/sites.duke.edu\\\/mazurowski\\\/resources\\\/breast-cancer-mri-dataset\\\/, the related publication, or contact tcia helpdesk at\\u00a0help@cancerimagingarchive.net. \\u00a0please visit this discussion forum for any questions related to the data:\\u00a0https:\\\/\\\/www.reddit.com\\\/r\\\/dukedcemridata\\\/. breast cancer breast mr seg other classification follow-up radiomic feature treatment measurement segmentation clinical genomics image analyses community\"},{\"id\":41151,\"type\":\"collection\",\"slug\":\"ea1141\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ea1141\\\/\",\"title\":\"Abbreviated Breast MRI and Digital Tomosynthesis Mammography in Screening Women With Dense Breasts\",\"short_title\":\"EA1141\",\"doi\":\"10.7937\\\/2BAS-HR33\",\"summary\":\"This collection contains data from the Eastern Cooperative Oncology Group (ECOG) Clinical Trial\\u00a0NCT02933489, \\u201cAbbreviated Breast MRI and Digital Tomosynthesis Mammography in Screening Women With Dense Breasts\\\". Principal Investigator: Christopher Comstock.\\u00a0\\u00a0It was sponsored by NCI and performed by the Eastern Cooperative Oncology Group under study number EA1141. This randomized phase II trial enrolled 1,516 subjects to study how well abbreviated breast magnetic resonance imaging (MRI) and digital tomosynthesis mammography work in detecting cancer in women with dense breasts. Imaging and clinical data from 500 of those subjects are being made available through this collection.Trial OutcomesResults of the trial have been reported in the following publication:Comstock CE, Gatsonis C, Newstead GM, Snyder BS, Gareen IF, Bergin JT, Rahbar H, Sung JS, Jacobs C, Harvey JA, Nicholson MH, Ward RC, Holt J, Prather A, Miller KD, Schnall MD, Kuhl CK. Comparison of Abbreviated Breast MRI vs Digital Breast Tomosynthesis for Breast Cancer Detection Among Women With Dense Breasts Undergoing Screening. JAMA. 2020 Feb 25;323(8):746-756. doi: 10.1001\\\/jama.2020.0572. Erratum in: JAMA. 2020 Mar 24;323(12):1194.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image-2023-4-11_16-13-2.png\",\"program\":\"NCTN\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MR\",\"MG\",\"CT\",\"Follow-Up\",\"Demographic\",\"Other\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":500,\"date_updated\":\"2023-10-02\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=EA1141\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/EA1141-October-2023-manifest.tcia\",\"blob\":\"abbreviated breast mri and digital tomosynthesis mammography in screening women with dense breasts ea1141 this collection contains data from the eastern cooperative oncology group (ecog) clinical trial\\u00a0nct02933489, \\u201cabbreviated breast mri and digital tomosynthesis mammography in screening women with dense breasts\\\". principal investigator: christopher comstock.\\u00a0\\u00a0it was sponsored by nci and performed by the eastern cooperative oncology group under study number ea1141. this randomized phase ii trial enrolled 1,516 subjects to study how well abbreviated breast magnetic resonance imaging (mri) and digital tomosynthesis mammography work in detecting cancer in women with dense breasts. imaging and clinical data from 500 of those subjects are being made available through this collection.trial outcomesresults of the trial have been reported in the following publication:comstock ce, gatsonis c, newstead gm, snyder bs, gareen if, bergin jt, rahbar h, sung js, jacobs c, harvey ja, nicholson mh, ward rc, holt j, prather a, miller kd, schnall md, kuhl ck. comparison of abbreviated breast mri vs digital breast tomosynthesis for breast cancer detection among women with dense breasts undergoing screening. jama. 2020 feb 25;323(8):746-756. doi: 10.1001\\\/jama.2020.0572. erratum in: jama. 2020 mar 24;323(12):1194.\\u00a0 breast cancer breast mr mg ct follow-up demographic other clinical nctn\"},{\"id\":42265,\"type\":\"collection\",\"slug\":\"exact\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/exact\\\/\",\"title\":\"Expert Anal Cancer Consensus Staging\",\"short_title\":\"ExACT\",\"doi\":\"10.7937\\\/2023.na2x-j031\",\"summary\":\"Accurate pre-therapeutic imaging of the tumour and regional lymph nodes, is - in addition to clinical examination and assessment for distant metastases -essential for prognostication and guiding treatment. Nodal staging has been validated as an important independent prognostic factor [EORTC22861; RTOG9811] and the nodal stage as well as the precise location of involved lymph nodes (mesorectum, presacral space, internal iliac lymph nodes, external iliac lymph nodes, ischiorectal fossa, inguinal lymph nodes) informs the planning of radiotherapy treatment especially when using more conformal techniques such as intensity modulated radiotherapy. It is therefore essential to optimize and standardize pre-treatment imaging to provide accurate and reproducible information to guide treatment decision, development of radiotherapy atlases and clinical trial design.Whereas there currently exist a lack of consensus as to which staging modality should be considered as the gold standard for local staging of the tumour (T) and detection of lymph node involvement (N), the use of multi-parametric Magnetic Resonance Imaging (MRI) of the pelvis has been endorsed by international guidelines (NCCN, ESMO\\\/ESTRO).An expert panel of radiologist and radiation oncologists with expertise in anal cancer diagnosis and treatment identified by the Anal Staging Subgroup of the International Rare Cancer Incentive (IRCI) will be invited to participate. The panelists will be assigned into groups based on the geographical location and local practice. Each panelist will be provided with access to a series of anonymized MRI datasets of patients with newly diagnosed anal cancer and asked to provide a TNM stage as well as to identify sites of involved nodes (using a standardised proforma \\u2013 Appendix1).\\u00a0 Each case was selected for the presence of MRI sequences like these:T2w TSE sagittal [T2-w sagittal turbo spin echo (TSE) sequence covering the pelvis]T1w TSE axial [sequence for pelvic nodal detection]\\u00a0T2-w TSE axial [sequence of the pelvis]T2w TSE sFOV [high-resolution small-field-of-view T2-w TSE sequences perpendicular and parallel to the anal canal]DWI [single shot spin echo-echo planar imaging DWI]ADC parametric map [apparent diffusion coefficient (ADC) maps]\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Anal Cancer\"],\"cancer_locations\":[\"Anus\"],\"data_types\":[\"MR\",\"Diagnosis\",\"Other\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":30,\"date_updated\":\"2023-03-30\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=ExACT\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ExACT-January-2023.tcia\",\"blob\":\"expert anal cancer consensus staging exact accurate pre-therapeutic imaging of the tumour and regional lymph nodes, is - in addition to clinical examination and assessment for distant metastases -essential for prognostication and guiding treatment. nodal staging has been validated as an important independent prognostic factor [eortc22861; rtog9811] and the nodal stage as well as the precise location of involved lymph nodes (mesorectum, presacral space, internal iliac lymph nodes, external iliac lymph nodes, ischiorectal fossa, inguinal lymph nodes) informs the planning of radiotherapy treatment especially when using more conformal techniques such as intensity modulated radiotherapy. it is therefore essential to optimize and standardize pre-treatment imaging to provide accurate and reproducible information to guide treatment decision, development of radiotherapy atlases and clinical trial design.whereas there currently exist a lack of consensus as to which staging modality should be considered as the gold standard for local staging of the tumour (t) and detection of lymph node involvement (n), the use of multi-parametric magnetic resonance imaging (mri) of the pelvis has been endorsed by international guidelines (nccn, esmo\\\/estro).an expert panel of radiologist and radiation oncologists with expertise in anal cancer diagnosis and treatment identified by the anal staging subgroup of the international rare cancer incentive (irci) will be invited to participate. the panelists will be assigned into groups based on the geographical location and local practice. each panelist will be provided with access to a series of anonymized mri datasets of patients with newly diagnosed anal cancer and asked to provide a tnm stage as well as to identify sites of involved nodes (using a standardised proforma \\u2013 appendix1).\\u00a0 each case was selected for the presence of mri sequences like these:t2w tse sagittal [t2-w sagittal turbo spin echo (tse) sequence covering the pelvis]t1w tse axial [sequence for pelvic nodal detection]\\u00a0t2-w tse axial [sequence of the pelvis]t2w tse sfov [high-resolution small-field-of-view t2-w tse sequences perpendicular and parallel to the anal canal]dwi [single shot spin echo-echo planar imaging dwi]adc parametric map [apparent diffusion coefficient (adc) maps] anal cancer anus mr diagnosis other clinical community\"},{\"id\":41513,\"type\":\"collection\",\"slug\":\"fdg-pet-ct-lesions\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/fdg-pet-ct-lesions\\\/\",\"title\":\"A whole-body FDG-PET\\\/CT dataset with manually annotated tumor lesions\",\"short_title\":\"FDG-PET-CT-Lesions\",\"doi\":\"10.7937\\\/gkr0-xv29\",\"summary\":\"Purpose: To provide an annotated data set of oncologic PET\\\/CT studies for the development and training of machine learning methods and to help address the limited availability of publicly available high-quality training data for PET\\\/CT image analysis projects.\\u00a0 This data can also be used for machine learning challenges, which is exemplified in the autoPET MICCAI 2022 competition: https:\\\/\\\/autopet.grand-challenge.org\\\/.\\u00a0\\u00a0Data: The anonymized publication of data was approved by the local ethics committee and data protection officer. 501 consecutive whole body FDG-PET\\\/CT data sets of patients with malignant lymphoma, melanoma and non small cell lung cancer (NSCLC) as well as 513 data sets without PET-positive malignant lesions (negative controls) examined between 2014 and 2018 at the University Hospital T\\u00fcbingen were included. All examinations were acquired on a single, state-of-the-art PET\\\/CT scanner (Siemens Biograph mCT). The imaging protocol consists of a diagnostic CT scan (mainly from skull base to mid-thigh level) with intravenous contrast enhancement in most cases, except for patients with contraindications. The following CT parameters were used: reference dose of 200 mAs, tube voltage of 120 kV, iterative reconstruction with a slice thickness of 2 - 3 mm. In addition, a whole-body FDG-PET scan was acquired 60 minutes after I.V. injection of 300-350 MBq 18F-FDG. PET data were reconstructed using an ordered-subset expectation maximization (OSEM) algorithm with 21 subsets and 2 iterations and a gaussian kernel of 2 mm and a matrix size of 400 x 400.All data sets were analyzed in a clinical setting by a radiologist and nuclear medicine physician in consensus identifying primary tumors and metastases in each data set. All FDG-avid lesions identified as malignant based on patient history and prior examinations were manually segmented on PET images in a slice-per-slice manner by a single reader using dedicated software (NORA imaging platform, University of Freiburg, Germany).We provide the anonymized original DICOM files of all studies as well as the DICOM segmentation masks. Primary diagnosis, age and sex are provided as non-imaging information (csv). In addition, we provide links to code for you to make a preprocessed version of the data with resampled and aligned PET, CT, and masks as a NIfTI file and in the hdf5 format ready to use in machine learning projects.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_figure.png\",\"program\":\"Community\",\"cancer_types\":[\"Lymphoma\",\"Melanoma\",\"Non-small Cell Lung Cancer\"],\"cancer_locations\":[\"Lung\",\"Lymph\",\"Skin\"],\"data_types\":[\"CT\",\"PT\",\"SEG\",\"Demographic\",\"Diagnosis\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":900,\"date_updated\":\"2022-06-02\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=FDG-PET-CT-Lesions\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_FDG-PET-CT-Lesions_v1.tcia\",\"blob\":\"a whole-body fdg-pet\\\/ct dataset with manually annotated tumor lesions fdg-pet-ct-lesions purpose: to provide an annotated data set of oncologic pet\\\/ct studies for the development and training of machine learning methods and to help address the limited availability of publicly available high-quality training data for pet\\\/ct image analysis projects.\\u00a0 this data can also be used for machine learning challenges, which is exemplified in the autopet miccai 2022 competition: https:\\\/\\\/autopet.grand-challenge.org\\\/.\\u00a0\\u00a0data: the anonymized publication of data was approved by the local ethics committee and data protection officer. 501 consecutive whole body fdg-pet\\\/ct data sets of patients with malignant lymphoma, melanoma and non small cell lung cancer (nsclc) as well as 513 data sets without pet-positive malignant lesions (negative controls) examined between 2014 and 2018 at the university hospital t\\u00fcbingen were included. all examinations were acquired on a single, state-of-the-art pet\\\/ct scanner (siemens biograph mct). the imaging protocol consists of a diagnostic ct scan (mainly from skull base to mid-thigh level) with intravenous contrast enhancement in most cases, except for patients with contraindications. the following ct parameters were used: reference dose of 200 mas, tube voltage of 120 kv, iterative reconstruction with a slice thickness of 2 - 3 mm. in addition, a whole-body fdg-pet scan was acquired 60 minutes after i.v. injection of 300-350 mbq 18f-fdg. pet data were reconstructed using an ordered-subset expectation maximization (osem) algorithm with 21 subsets and 2 iterations and a gaussian kernel of 2 mm and a matrix size of 400 x 400.all data sets were analyzed in a clinical setting by a radiologist and nuclear medicine physician in consensus identifying primary tumors and metastases in each data set. all fdg-avid lesions identified as malignant based on patient history and prior examinations were manually segmented on pet images in a slice-per-slice manner by a single reader using dedicated software (nora imaging platform, university of freiburg, germany).we provide the anonymized original dicom files of all studies as well as the dicom segmentation masks. primary diagnosis, age and sex are provided as non-imaging information (csv). in addition, we provide links to code for you to make a preprocessed version of the data with resampled and aligned pet, ct, and masks as a nifti file and in the hdf5 format ready to use in machine learning projects.\\u00a0 lymphoma melanoma non-small cell lung cancer lung lymph skin ct pt seg demographic diagnosis clinical image analyses software\\\/source code community\"},{\"id\":42313,\"type\":\"collection\",\"slug\":\"gammaknife-hippocampal\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/gammaknife-hippocampal\\\/\",\"title\":\"Gamma Knife MR\\\/CT\\\/RTSTRUCT Sets With Hippocampal Contours\",\"short_title\":\"GammaKnife-Hippocampal\",\"doi\":\"10.7937\\\/Q967-X166\",\"summary\":\"This collection is comprised of 390 patients who presented with vestibular schwannoma (VS, n=73), trigeminal neuralgia (TGN, n=119) or metastatic disease (M, n=198, 4-26 metastases) and were subsequently treated with Gamma Knife (Elekta AB, Stockholm, Sweden) stereotactic radiosurgery. For each patient, the treatment indication is designated with a suffix on their patient ID (VS, TGN or M).All patients are provided with at least one high-resolution (1 mm slice thickness) T1 FLASH trans-axial MR imaging study and their corresponding high-resolution axial planning CT. When available, treatment planning data (struct, dose, plan), alternative MR sequences (FLAIR, T2 CISS, etc.) and follow-up MR imaging studies were collected. Each MR image used during treatment planning was registered to the CT frame of reference and is provided with the DICOM registration file and the aligned secondary image. Additionally, for each patient in the cohort, hippocampal contours generated by multiple institutional observers are provided in a separate structure set. In total, this dataset contains: CT (n=390), MR (n=3868), REG (n=872), DOSE (n=928), PLAN (n=928), planning STRUCT (n=931), and hippocampal research STRUCT (n=390).Planning DataGamma Knife planning studies are designated by \\u201cGamma Knife Planning Data\\u201d in the study description DICOM header tag. For each patient, MIM (MIM Software, Beachwood, OH) was used to generate a rigid registration between CT and each MR sequence and the registration accuracy was validated using the stereotactic frame fiducial markers. From each registration, a DICOM RTREG file and aligned secondary are provided with each aligned secondaries series indicated by \\u201c[original series description] Co-registered to CT\\u201d in the series description DICOM header tag. During export from GammaPlan (Elekta AB, Stockholm, Sweden), the treatment planning files are provided in duplicate for each imaging modality frame of reference (CT and each MR sequence). Treatment planning structure names were renamed to remove misspellings, synonyms, and identifiable information. Structures with the format Plan1[tgt#]#Gy denote each target volume (a, b, ...) and the dose to the specified target in Gray. For patients with multiple lesions, ROI structure names are given as the brain region followed by the relative anatomical directions (e.g. Lobe_Frontal_R, Lobe_Parietal_L). Any empty contours were preserved but are designated with '(Empty)' in the name.Follow-up DataWhen available, patient follow-up images were collected between the provided treatment date and either the next Gamma Knife treatment or up-to approximately two years following treatment. In total, 197 patients are provided with follow-up imaging studies, with a median of 2 (range 1-13) follow-up studies provided per patient. Each follow-up imaging study is designated as \\u201cFollow-up Image Set #\\u201d in the Study Description. Each follow-up study exists in a unique frame of reference and was not co-registered to the original treatment planning CT volume.Hippocampal ContoursThree independent observers generated hippocampal contours from the CT aligned-secondary of the T1-weighted MR image, with the resultant contours saved to the CT frame of reference. In total, 744 unique left, right contour pairs were generated (observer 1, n=390; observer 2, n=247; observer 3, n=107). In addition to hippocampal contours, the region grow tool was used to generate a head contour (ROI name \\u2018head\\u2019) to mask out the stereotactic frame and remove most of the reconstruction artifacts on the inferior extent of the image volume.Additional InformationAll MR images designated as \\u2018Co-registered to CT\\u2019 in the Series Description were interpolated to the CT frame of reference, potentially altering the field of view or voxel resolution. For all co-registered MR images, the original primary images and registration files are provided for use as needed.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/GammaKnifeHippocampalFigure.png\",\"program\":\"Community\",\"cancer_types\":[\"Various\",\"Non-Cancer\"],\"cancer_locations\":[\"Head\"],\"data_types\":[\"MR\",\"RTDOSE\",\"RTPLAN\",\"RTSTRUCT\",\"CT\",\"REG\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":390,\"date_updated\":\"2022-07-22\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=GammaKnife-Hippocampal\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/GammaKnife-Hippocampal-June-2022.tcia\",\"blob\":\"gamma knife mr\\\/ct\\\/rtstruct sets with hippocampal contours gammaknife-hippocampal this collection is comprised of 390 patients who presented with vestibular schwannoma (vs, n=73), trigeminal neuralgia (tgn, n=119) or metastatic disease (m, n=198, 4-26 metastases) and were subsequently treated with gamma knife (elekta ab, stockholm, sweden) stereotactic radiosurgery. for each patient, the treatment indication is designated with a suffix on their patient id (vs, tgn or m).all patients are provided with at least one high-resolution (1 mm slice thickness) t1 flash trans-axial mr imaging study and their corresponding high-resolution axial planning ct. when available, treatment planning data (struct, dose, plan), alternative mr sequences (flair, t2 ciss, etc.) and follow-up mr imaging studies were collected. each mr image used during treatment planning was registered to the ct frame of reference and is provided with the dicom registration file and the aligned secondary image. additionally, for each patient in the cohort, hippocampal contours generated by multiple institutional observers are provided in a separate structure set. in total, this dataset contains: ct (n=390), mr (n=3868), reg (n=872), dose (n=928), plan (n=928), planning struct (n=931), and hippocampal research struct (n=390).planning datagamma knife planning studies are designated by \\u201cgamma knife planning data\\u201d in the study description dicom header tag. for each patient, mim (mim software, beachwood, oh) was used to generate a rigid registration between ct and each mr sequence and the registration accuracy was validated using the stereotactic frame fiducial markers. from each registration, a dicom rtreg file and aligned secondary are provided with each aligned secondaries series indicated by \\u201c[original series description] co-registered to ct\\u201d in the series description dicom header tag. during export from gammaplan (elekta ab, stockholm, sweden), the treatment planning files are provided in duplicate for each imaging modality frame of reference (ct and each mr sequence). treatment planning structure names were renamed to remove misspellings, synonyms, and identifiable information. structures with the format plan1[tgt#]#gy denote each target volume (a, b, ...) and the dose to the specified target in gray. for patients with multiple lesions, roi structure names are given as the brain region followed by the relative anatomical directions (e.g. lobe_frontal_r, lobe_parietal_l). any empty contours were preserved but are designated with '(empty)' in the name.follow-up datawhen available, patient follow-up images were collected between the provided treatment date and either the next gamma knife treatment or up-to approximately two years following treatment. in total, 197 patients are provided with follow-up imaging studies, with a median of 2 (range 1-13) follow-up studies provided per patient. each follow-up imaging study is designated as \\u201cfollow-up image set #\\u201d in the study description. each follow-up study exists in a unique frame of reference and was not co-registered to the original treatment planning ct volume.hippocampal contoursthree independent observers generated hippocampal contours from the ct aligned-secondary of the t1-weighted mr image, with the resultant contours saved to the ct frame of reference. in total, 744 unique left, right contour pairs were generated (observer 1, n=390; observer 2, n=247; observer 3, n=107). in addition to hippocampal contours, the region grow tool was used to generate a head contour (roi name \\u2018head\\u2019) to mask out the stereotactic frame and remove most of the reconstruction artifacts on the inferior extent of the image volume.additional informationall mr images designated as \\u2018co-registered to ct\\u2019 in the series description were interpolated to the ct frame of reference, potentially altering the field of view or voxel resolution. for all co-registered mr images, the original primary images and registration files are provided for use as needed. various non-cancer head mr rtdose rtplan rtstruct ct reg community\"},{\"id\":42323,\"type\":\"collection\",\"slug\":\"gbm-dsc-mri-dro\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/gbm-dsc-mri-dro\\\/\",\"title\":\"GBM-DSC-MRI-DRO\",\"short_title\":\"GBM-DSC-MRI-DRO\",\"doi\":\"10.7937\\\/TCIA.2020.RMWVZWIX\",\"summary\":\"The standardization of dynamic susceptibility contrast (DSC)-magnetic resonance imaging (MRI) has been confounded by a lack of consensus on DSC-MRI methodology for preventing potential relative cerebral blood volume (CBV) inaccuracies, including the choice of acquisition protocols and postprocessing algorithms. Therefore, a digital reference object (DRO) was developed using physiological and kinetic parameters derived from a patient database, unique voxel-wise 3-dimensional tissue structures, and a validated MRI signal computational approach. The primary, intended use of the DRO is to validate image acquisition and analysis methods for accurately measuring relative cerebral blood volume in glioblastomas [1,2]. The DRO datasets have also been used as part of the QIN-challenge titled \\u201cDSC-DRO Challenge\\u201d to evaluate multisite rCBV consistency [3], and for systematic assessment of multi-echo DSC-MRI [4].\\nReferences:\\nSemmineh NB, Stokes AM, Bell LC, Boxerman JL, Quarles CC. A Population-Based Digital Reference Object (DRO) for Optimizing Dynamic Susceptibility Contrast (DSC)-MRI Methods for Clinical Trials. Tomography 2017;3:41\\u20139. doi:10.18383\\\/j.tom.2016.00286.Semmineh NB, Bell LC, Stokes AM, Hu LS, Boxerman JL, Quarles CC. Optimization of acquisition and analysis methods for clinical dynamic susceptibility contrast MRI using a population-based digital reference object. Am J Neuroradiol 2018. doi:10.3174\\\/ajnr.A5827.Bell LC, Semmineh N, An H, Eldeniz C, Wahl R, Schmainda KM, et al. Evaluating Multisite rCBV Consistency from DSC-MRI Imaging Protocols and Postprocessing Software Across the NCI Quantitative Imaging Network Sites Using a Digital Reference Object (DRO). Tomogr (Ann Arbor, Mich) 2019. doi:10.18383\\\/j.tom.2018.00041.Stokes AM, Semmineh NB, Nespodzany A, Bell LC, Quarles CC. Systematic assessment of multi-echo dynamic susceptibility contrast MRI using a digital reference object. Magn Reson Med 2020. doi:10.1002\\\/mrm.27914.\\nDRO development\\nTo achieve DSC-MRI signals representative of the temporal characteristics, magnitude, and distribution of contrast agent-induced T1 and T2 * changes observed across multiple glioblastomas, the DRO\\u2019s input parameters were trained using DSC-MRI data from 23 glioblastomas (40,000 voxels). The DRO\\u2019s ability to produce reliable signals across combinations of pulse sequence parameters and contrast agent dosing schemes unlike those in the training data set was validated by comparison with in vivo dual-echo DSC-MRI data acquired in a separate cohort of patients with glioblastomas.\\u00a0To achieve an excellent agreement between the DRO and in vivo data the training and validation process required a DRO consisting of 10 000 unique voxels.\\nApplication\\nUsers can use the DRO to investigate the influence of DSC-MRI acquisition and post-processing methods on CBV accuracy and as a benchmark for perfusion analysis algorithms.\\nGuide\\nThe DRO data is separated in to two collections corresponding to two magnetic field strengths (3T and 1.5T). Each collection contains 15 folders corresponding to three TRs (1s, 1.5s, 2s) and five contrast agent dosing schemes (pre none bolus full, pre quarter bolus three-quarter, pre half bolus half, pre quarter bolus full, and pre full bolus full). Each of the 15 folders contains 12 sub-folders representing a combination of three flip angles (30o, 60o, and 90o) and four echo times (20ms, 30ms, 40ms, and 50ms). Within each of the 12 sub-folders a single slice DSC-MRI signal time series of 3 minutes sampled at intervals of the corresponding TR values are given. The slice contains four ROIs described in figure 1. In addition to the DRO data masks for each of the four ROIs are provided.\\nFigure 1 (top right of page): (A) Represents the tumor region containing 10000 voxels. (B) The corresponding tumor region with no CA leakage contamination (to be used for expected calculations). (C) Representative normal appearing white matter (WM) containing 2000 voxels. (D) Region representing the arterial input function (AIF). For a given parameter combination (TR=1.5s, TE=30ms, Flip angle= 60o, CA dosing = pre none bolus full, and B0= 3T), Figure 2 demonstrate example signal time course for all four ROI.\\nFigure 2 (below): Example signal time course for a voxel within each of the four ROIs.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image2022-7-21_17-9-54.png\",\"program\":\"QIN\",\"cancer_types\":[\"Phantom\"],\"cancer_locations\":[\"Brain Phantom\"],\"data_types\":[\"MR\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":3,\"date_updated\":\"2022-07-21\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=GBM-DSC-MRI-DRO\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/GBM-DSC-MRI-DRO_full.tcia\",\"blob\":\"gbm-dsc-mri-dro gbm-dsc-mri-dro the standardization of dynamic susceptibility contrast (dsc)-magnetic resonance imaging (mri) has been confounded by a lack of consensus on dsc-mri methodology for preventing potential relative cerebral blood volume (cbv) inaccuracies, including the choice of acquisition protocols and postprocessing algorithms. therefore, a digital reference object (dro) was developed using physiological and kinetic parameters derived from a patient database, unique voxel-wise 3-dimensional tissue structures, and a validated mri signal computational approach. the primary, intended use of the dro is to validate image acquisition and analysis methods for accurately measuring relative cerebral blood volume in glioblastomas [1,2]. the dro datasets have also been used as part of the qin-challenge titled \\u201cdsc-dro challenge\\u201d to evaluate multisite rcbv consistency [3], and for systematic assessment of multi-echo dsc-mri [4].\\nreferences:\\nsemmineh nb, stokes am, bell lc, boxerman jl, quarles cc. a population-based digital reference object (dro) for optimizing dynamic susceptibility contrast (dsc)-mri methods for clinical trials. tomography 2017;3:41\\u20139. doi:10.18383\\\/j.tom.2016.00286.semmineh nb, bell lc, stokes am, hu ls, boxerman jl, quarles cc. optimization of acquisition and analysis methods for clinical dynamic susceptibility contrast mri using a population-based digital reference object. am j neuroradiol 2018. doi:10.3174\\\/ajnr.a5827.bell lc, semmineh n, an h, eldeniz c, wahl r, schmainda km, et al. evaluating multisite rcbv consistency from dsc-mri imaging protocols and postprocessing software across the nci quantitative imaging network sites using a digital reference object (dro). tomogr (ann arbor, mich) 2019. doi:10.18383\\\/j.tom.2018.00041.stokes am, semmineh nb, nespodzany a, bell lc, quarles cc. systematic assessment of multi-echo dynamic susceptibility contrast mri using a digital reference object. magn reson med 2020. doi:10.1002\\\/mrm.27914.\\ndro development\\nto achieve dsc-mri signals representative of the temporal characteristics, magnitude, and distribution of contrast agent-induced t1 and t2 * changes observed across multiple glioblastomas, the dro\\u2019s input parameters were trained using dsc-mri data from 23 glioblastomas (40,000 voxels). the dro\\u2019s ability to produce reliable signals across combinations of pulse sequence parameters and contrast agent dosing schemes unlike those in the training data set was validated by comparison with in vivo dual-echo dsc-mri data acquired in a separate cohort of patients with glioblastomas.\\u00a0to achieve an excellent agreement between the dro and in vivo data the training and validation process required a dro consisting of 10 000 unique voxels.\\napplication\\nusers can use the dro to investigate the influence of dsc-mri acquisition and post-processing methods on cbv accuracy and as a benchmark for perfusion analysis algorithms.\\nguide\\nthe dro data is separated in to two collections corresponding to two magnetic field strengths (3t and 1.5t). each collection contains 15 folders corresponding to three trs (1s, 1.5s, 2s) and five contrast agent dosing schemes (pre none bolus full, pre quarter bolus three-quarter, pre half bolus half, pre quarter bolus full, and pre full bolus full). each of the 15 folders contains 12 sub-folders representing a combination of three flip angles (30o, 60o, and 90o) and four echo times (20ms, 30ms, 40ms, and 50ms). within each of the 12 sub-folders a single slice dsc-mri signal time series of 3 minutes sampled at intervals of the corresponding tr values are given. the slice contains four rois described in figure 1. in addition to the dro data masks for each of the four rois are provided.\\nfigure 1 (top right of page): (a) represents the tumor region containing 10000 voxels. (b) the corresponding tumor region with no ca leakage contamination (to be used for expected calculations). (c) representative normal appearing white matter (wm) containing 2000 voxels. (d) region representing the arterial input function (aif). for a given parameter combination (tr=1.5s, te=30ms, flip angle= 60o, ca dosing = pre none bolus full, and b0= 3t), figure 2 demonstrate example signal time course for all four roi.\\nfigure 2 (below): example signal time course for a voxel within each of the four rois. phantom brain phantom mr other qin\"},{\"id\":42347,\"type\":\"collection\",\"slug\":\"glis-rt\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/glis-rt\\\/\",\"title\":\"Glioma Image Segmentation for Radiotherapy: RT targets, barriers to cancer spread, and organs at risk\",\"short_title\":\"GLIS-RT\",\"doi\":\"10.7937\\\/TCIA.T905-ZQ20\",\"summary\":\"This collection consists of 230 cases of glioblastoma and low-grade glioma patients treated with surgery and adjuvant radiotherapy at Massachusetts General Hospital. The patients underwent routine post-surgical MRI examination by acquiring two MR sequences, contrast enhanced 3D-T1 and 2D multislice-T2 FLAIR required to define target volumes for radiotherapy treatment. CT scans were acquired after diagnostic imaging to use in radiotherapy treatment planning. All cases in the image set are provided with the radiotherapy targets, gross tumor volume (GTV) and clinical target volume (CTV) manually delineated by the treating radiation oncologist. The set includes glioblastoma (GBM) - 198 cases, anaplastic astrocytoma (AAC) - 23 cases, astrocytoma (AC) - 5 cases, anaplastic oligodendroglioma (AODG) - 2 cases, and oligodendroglioma (ODG) - 2 case. These abbreviations are included in the case ID.\\u00a0For all cases, manual delineations are provided for the RT targets (GTV and CTV) and for organs at risk,\\u00a0 the brainstem, optic chiasm, optic nerves, eyes, cochleae, and lacrimal glands.\\u00a0A subset of these 230 cases consisting of 75 cases was used for the International Challenge \\u201cAnatomical Brain Barriers to Cancer Spread: Segmentation from CT and MR Images\\u201d, ABCs, organized in conjunction with the MICCAI 2020 conference (https:\\\/\\\/abcs.mgh.harvard.edu). For these cases, manual delineations are provided for the structures used for automated definition of the CTV: the falx cerebri, tentorium cerebelli, transverse and sagittal brain sinuses, ventricles, cerebellum.\\u00a0The images and manually delineated structures are to be used to develop methods for computer assisted radiotherapy target definition, algorithms for auto-delineation of the normal anatomical structures to be used for radiotherapy treatment plan optimization, and methods that utilize multi-modality images for deep learning-based image segmentation.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/wiki_figure.png\",\"program\":\"Community\",\"cancer_types\":[\"Glioblastoma\",\"Astrocytoma\",\"Low Grade Glioma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"CT\",\"REG\",\"RTSTRUCT\",\"MR\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":230,\"date_updated\":\"2021-12-20\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=GLIS-RT\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/GLIS-RT-manifest.tcia\",\"blob\":\"glioma image segmentation for radiotherapy: rt targets, barriers to cancer spread, and organs at risk glis-rt this collection consists of 230 cases of glioblastoma and low-grade glioma patients treated with surgery and adjuvant radiotherapy at massachusetts general hospital. the patients underwent routine post-surgical mri examination by acquiring two mr sequences, contrast enhanced 3d-t1 and 2d multislice-t2 flair required to define target volumes for radiotherapy treatment. ct scans were acquired after diagnostic imaging to use in radiotherapy treatment planning. all cases in the image set are provided with the radiotherapy targets, gross tumor volume (gtv) and clinical target volume (ctv) manually delineated by the treating radiation oncologist. the set includes glioblastoma (gbm) - 198 cases, anaplastic astrocytoma (aac) - 23 cases, astrocytoma (ac) - 5 cases, anaplastic oligodendroglioma (aodg) - 2 cases, and oligodendroglioma (odg) - 2 case. these abbreviations are included in the case id.\\u00a0for all cases, manual delineations are provided for the rt targets (gtv and ctv) and for organs at risk,\\u00a0 the brainstem, optic chiasm, optic nerves, eyes, cochleae, and lacrimal glands.\\u00a0a subset of these 230 cases consisting of 75 cases was used for the international challenge \\u201canatomical brain barriers to cancer spread: segmentation from ct and mr images\\u201d, abcs, organized in conjunction with the miccai 2020 conference (https:\\\/\\\/abcs.mgh.harvard.edu). for these cases, manual delineations are provided for the structures used for automated definition of the ctv: the falx cerebri, tentorium cerebelli, transverse and sagittal brain sinuses, ventricles, cerebellum.\\u00a0the images and manually delineated structures are to be used to develop methods for computer assisted radiotherapy target definition, algorithms for auto-delineation of the normal anatomical structures to be used for radiotherapy treatment plan optimization, and methods that utilize multi-modality images for deep learning-based image segmentation. glioblastoma astrocytoma low grade glioma brain ct reg rtstruct mr community\"},{\"id\":42871,\"type\":\"collection\",\"slug\":\"hcc-tace-seg\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/hcc-tace-seg\\\/\",\"title\":\"Multimodality annotated HCC cases with and without advanced imaging segmentation\",\"short_title\":\"HCC-TACE-Seg\",\"doi\":\"10.7937\\\/TCIA.5FNA-0924\",\"summary\":\"Hepatocellular carcinoma (HCC) is the most common primary liver cancer with incidences doubled over the past two decades due to increasing risk factors. Despite surveillance, the majority of HCC cases are diagnosed at advanced stages that can be treated only using (Transarterial chemoembolization) TACE, or systemic therapy. TACE failure can occur to 60% of patients receiving the procedure, with subsequent financial and emotional burden. Radiomics have emerged as a new tool capable of predicting tumor response to TACE from pre-procedural CT study.This retrospectively acquired data collection includes pre- and post-procedure CT imaging studies of 105\\u00a0confirmed HCC patients who underwent TACE between 2002 and 2012 with an available treatment outcome, in the form of time-to-progression and overall survival. Baseline imaging includes multiphasic contrast-enhanced CT with no image artifacts (e.g. surgical clip) and was obtained 1-12 weeks (average 3 weeks) prior to the first TACE session. Semiautomatic segmentation of liver, tumor, and blood vessels created using AMIRA was manually clinically curated. These segmentations of each pre-procedural CT study were done for the purpose of algorithm training for prediction and automatic liver tumor segmentation, and are provided here (NIfTI converted to DICOM-SEG format).\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Hepatocellular carcinoma\"],\"cancer_locations\":[\"Liver\"],\"data_types\":[\"CT\",\"SEG\",\"Follow-Up\",\"Molecular Test\",\"Demographic\",\"Exposure\",\"Measurement\",\"Treatment\",\"Classification\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":105,\"date_updated\":\"2022-08-17\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=HCC-TACE-Seg\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/HCC-TACE-Seg_v1_202201.tcia\",\"blob\":\"multimodality annotated hcc cases with and without advanced imaging segmentation hcc-tace-seg hepatocellular carcinoma (hcc) is the most common primary liver cancer with incidences doubled over the past two decades due to increasing risk factors. despite surveillance, the majority of hcc cases are diagnosed at advanced stages that can be treated only using (transarterial chemoembolization) tace, or systemic therapy. tace failure can occur to 60% of patients receiving the procedure, with subsequent financial and emotional burden. radiomics have emerged as a new tool capable of predicting tumor response to tace from pre-procedural ct study.this retrospectively acquired data collection includes pre- and post-procedure ct imaging studies of 105\\u00a0confirmed hcc patients who underwent tace between 2002 and 2012 with an available treatment outcome, in the form of time-to-progression and overall survival. baseline imaging includes multiphasic contrast-enhanced ct with no image artifacts (e.g. surgical clip) and was obtained 1-12 weeks (average 3 weeks) prior to the first tace session. semiautomatic segmentation of liver, tumor, and blood vessels created using amira was manually clinically curated. these segmentations of each pre-procedural ct study were done for the purpose of algorithm training for prediction and automatic liver tumor segmentation, and are provided here (nifti converted to dicom-seg format). hepatocellular carcinoma liver ct seg follow-up molecular test demographic exposure measurement treatment classification clinical image analyses software\\\/source code community\"},{\"id\":42861,\"type\":\"collection\",\"slug\":\"he-vs-mpm\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/he-vs-mpm\\\/\",\"title\":\"Multimodal imaging of ductal carcinoma in situ with microinvasion\",\"short_title\":\"HE-vs-MPM\",\"doi\":\"10.7937\\\/3fyc-ac78\",\"summary\":\"Ductal carcinoma in situ with microinvasion (DCISM) is a challenging subtype of breast cancer with controversial invasiveness and prognosis. Accurate diagnosis of DCISM from ductal carcinoma in situ (DCIS) is crucial for optimal treatment and improved clinical outcomes. This dataset provides histopathology images and paired CK5\\\/6 immunohistochemical staining images from patients with DCISM, as well as multiphoton microscopy images of suspicious regions. It offers multi-modal imaging data from various perspectives for analysis and diagnosis of microinvasive breast cancer by other researchers in the field.The dataset contains data from 12 breast cancer patients, including 10 cases of ductal carcinoma in situ with microinvasion (DCISM), 1 case of ductal carcinoma in situ (DCIS), and 1 case of invasive breast cancer.\\u00a0The magnification of the glass slide images is 40x. The pathology slide scanner used was created by the Sunny Optical Technology (group) Co., Ltd., and the pixel aspect ratio of the images is 1.\\u00a0 The dataset also includes multiphoton microscopy imaging of suspicious microinvasion areas.\\u00a0 The multiphoton imaging system was manufactured by Zeiss, and it also has a pixel aspect ratio of 1.Our database was specifically collected for the use of imaging methods in diagnosing DICSM. The suffixes in each case number indicate the patient's condition - \\\"DCISM\\\" for ductal carcinoma in situ with microinvasion, \\\"DCIS\\\" for ductal carcinoma in situ, and \\\"IDC\\\" for invasive ductal carcinoma. Apart from these labels, we have not collected any additional clinical information for these cases.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/HEvsMPM-FeaturedImage-Path.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"Histopathology\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":12,\"date_updated\":\"2023-12-08\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22HE-vs-MPM%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc1NiIsInBhc3Njb2RlIjoiYTQ1YTY5NWExMTk5MTM4NTA0NWVkZTg1MTE5MzZhYjU2ODAzMDM0NyIsInBhY2thZ2VfaWQiOiI3NTYiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"multimodal imaging of ductal carcinoma in situ with microinvasion he-vs-mpm ductal carcinoma in situ with microinvasion (dcism) is a challenging subtype of breast cancer with controversial invasiveness and prognosis. accurate diagnosis of dcism from ductal carcinoma in situ (dcis) is crucial for optimal treatment and improved clinical outcomes. this dataset provides histopathology images and paired ck5\\\/6 immunohistochemical staining images from patients with dcism, as well as multiphoton microscopy images of suspicious regions. it offers multi-modal imaging data from various perspectives for analysis and diagnosis of microinvasive breast cancer by other researchers in the field.the dataset contains data from 12 breast cancer patients, including 10 cases of ductal carcinoma in situ with microinvasion (dcism), 1 case of ductal carcinoma in situ (dcis), and 1 case of invasive breast cancer.\\u00a0the magnification of the glass slide images is 40x. the pathology slide scanner used was created by the sunny optical technology (group) co., ltd., and the pixel aspect ratio of the images is 1.\\u00a0 the dataset also includes multiphoton microscopy imaging of suspicious microinvasion areas.\\u00a0 the multiphoton imaging system was manufactured by zeiss, and it also has a pixel aspect ratio of 1.our database was specifically collected for the use of imaging methods in diagnosing dicsm. the suffixes in each case number indicate the patient's condition - \\\"dcism\\\" for ductal carcinoma in situ with microinvasion, \\\"dcis\\\" for ductal carcinoma in situ, and \\\"idc\\\" for invasive ductal carcinoma. apart from these labels, we have not collected any additional clinical information for these cases. breast cancer breast histopathology community\"},{\"id\":42371,\"type\":\"collection\",\"slug\":\"head-neck-cetuximab\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/head-neck-cetuximab\\\/\",\"title\":\"Head-Neck Cetuximab\",\"short_title\":\"Head-Neck Cetuximab\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.7AKGJUPZ\",\"summary\":\"This collection combines advanced molecular imaging treatment response assessment through pre- and post-treatment FDG PET\\\/CT scans with therapy of advanced head and neck cancer, including chemo-radiation therapy with and without addition of an EGFR inhibitor\\u00a0molecular targeted agent (Cetuximab).\\u00a0The Head-Neck Cetuximab collection consists of a subset of image data from RTOG 0522, which was randomized phase III Trial of Radiation Therapy and Chemotherapy for stage III and IV Head and Neck carcinomas. The RTOG 0522 protocols were activated in November 2005 and successfully completed accrual of 945 patients in 2009. As part of the RTOG 0522 trial, CT, Structures, RT Doses, RT Plans were collected by RTOG, and institutions had the option to join a related quantitative PET (PET\\\/CT) imaging study with ACRIN. The post-treatment FDG PET\\\/CT scan was performed 8-9 weeks after completion of treatment before any nodal dissection.\\u00a0For more information about the original aims of this trial please see https:\\\/\\\/clinicaltrials.gov\\\/ct2\\\/show\\\/results\\\/NCT00265941?term=rtog0522 and\\u00a0this PDF.\",\"image\":false,\"program\":\"NCI Trials\",\"cancer_types\":[\"Head and Neck Carcinomas\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"PT\",\"CT\",\"RTDOSE\",\"RTPLAN\",\"RTSTRUCT\",\"Protocol\",\"Other\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":111,\"date_updated\":\"2013-11-14\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=Head-Neck%20Cetuximab\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_Head-Neck_Cetuximab_06-22-2015.tcia\",\"blob\":\"head-neck cetuximab head-neck cetuximab this collection combines advanced molecular imaging treatment response assessment through pre- and post-treatment fdg pet\\\/ct scans with therapy of advanced head and neck cancer, including chemo-radiation therapy with and without addition of an egfr inhibitor\\u00a0molecular targeted agent (cetuximab).\\u00a0the head-neck cetuximab collection consists of a subset of image data from rtog 0522, which was randomized phase iii trial of radiation therapy and chemotherapy for stage iii and iv head and neck carcinomas. the rtog 0522 protocols were activated in november 2005 and successfully completed accrual of 945 patients in 2009. as part of the rtog 0522 trial, ct, structures, rt doses, rt plans were collected by rtog, and institutions had the option to join a related quantitative pet (pet\\\/ct) imaging study with acrin. the post-treatment fdg pet\\\/ct scan was performed 8-9 weeks after completion of treatment before any nodal dissection.\\u00a0for more information about the original aims of this trial please see https:\\\/\\\/clinicaltrials.gov\\\/ct2\\\/show\\\/results\\\/nct00265941?term=rtog0522 and\\u00a0this pdf. head and neck carcinomas head-neck pt ct rtdose rtplan rtstruct protocol other image analyses nci trials\"},{\"id\":42393,\"type\":\"collection\",\"slug\":\"head-neck-pet-ct\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/head-neck-pet-ct\\\/\",\"title\":\"Head-Neck-PET-CT\",\"short_title\":\"Head-Neck-PET-CT\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2017.8oje5q00\",\"summary\":\"This collection contains FDG-PET\\\/CT and radiotherapy planning CT imaging data of\\u00a0298 \\u00a0patients from four different institutions in Qu\\u00e9bec with histologically proven head-and-neck cancer (H&N) All patients had pre-treatment FDG-PET\\\/CT scans between April 2006 and November 2014, and within a median of 18 days (range: 6-66) before treatment. Dates in the TCIA images have been changed in the interest of de-identification; the same change was applied across all images, preserving the time intervals between serial scans.\\u00a0\\u00a0These patients were all part of a study described in further detail\\u00a0 (treatment, image scanning protocols, etc.) \\u00a0in the publication:Valli\\u00e8res, M. et al. Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. Sci Rep 7, 10117 (2017). doi: 10.1038\\\/s41598-017-10371-5\\u00a0\\u00a0 Note:  \\u00a0Subsequent to publishing this manuscript\\u00a0it was discovered images from\\u00a0 two patients included in the analysis\\u00a0had errors and should not be used in future studies.\\u00a0 Therefore these have not been included\\u00a0in this TCIA data set, leaving 298 patients of the original 300 analyzed.In the original study,\\u00a093 of the 300 patients (31 %), the radiotherapy contours were directly drawn on the CT of the FDG-PET\\\/CT scan by expert radiation oncologists and thereafter used for treatment planning. For 207 of the 300 patients (69 %), the radiotherapy contours were drawn on a different CT scan dedicated to treatment planning and were propagated\\\/resampled to the FDG-PET\\\/CT scan reference frame using intensity-based free-form deformable registration with the software MIM\\u00ae\\u00a0(MIM software Inc., Cleveland, OH).Patients with recurrent H&N cancer or with metastases at presentation, and patients receiving palliative treatment were excluded from the study. From the 300 patients, 48 received radiation alone (16 %) and 252 received chemo-radiation (84 %) with curative intent as part of treatment management. The median follow-up period of all patients was 43 months (range: 6-112). Patients that did not develop a locoregional recurrence or distant metastases during the follow-up period and that had a follow-up time smaller than 24 months were also excluded from the study. During the follow-up period, 45 patients developed a locoregional recurrence (15 %), 40 patients developed distant metastases (13 %) and 56 patients died (19 %).\\u00a0\\u00a0We analyzed the FDG-PET and CT images of the 300 patients from four different cohorts for the risk assessment of locoregional recurrences (LR) and distant metastases in H&N cancer. Prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. Independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (LR: AUC = 0.69 and CI = 0.67; DM: AUC = 0.86 and CI = 0.88). Furthermore, the results obtained via Kaplan-Meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Head and Neck Cancer\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"REG\",\"RTSTRUCT\",\"CT\",\"RTDOSE\",\"RTPLAN\",\"PT\",\"Demographic\",\"Diagnosis\",\"Follow-Up\",\"Treatment\",\"Other\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":298,\"date_updated\":\"2018-06-07\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=Head-Neck-PET-CT\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Head-Neck-PET-CT-v2-doiJNLP-7hWXTIJS.tcia\",\"blob\":\"head-neck-pet-ct head-neck-pet-ct this collection contains fdg-pet\\\/ct and radiotherapy planning ct imaging data of\\u00a0298 \\u00a0patients from four different institutions in qu\\u00e9bec with histologically proven head-and-neck cancer (h&n) all patients had pre-treatment fdg-pet\\\/ct scans between april 2006 and november 2014, and within a median of 18 days (range: 6-66) before treatment. dates in the tcia images have been changed in the interest of de-identification; the same change was applied across all images, preserving the time intervals between serial scans.\\u00a0\\u00a0these patients were all part of a study described in further detail\\u00a0 (treatment, image scanning protocols, etc.) \\u00a0in the publication:valli\\u00e8res, m. et al. radiomics strategies for risk assessment of tumour failure in head-and-neck cancer. sci rep 7, 10117 (2017). doi: 10.1038\\\/s41598-017-10371-5\\u00a0\\u00a0 note:  \\u00a0subsequent to publishing this manuscript\\u00a0it was discovered images from\\u00a0 two patients included in the analysis\\u00a0had errors and should not be used in future studies.\\u00a0 therefore these have not been included\\u00a0in this tcia data set, leaving 298 patients of the original 300 analyzed.in the original study,\\u00a093 of the 300 patients (31 %), the radiotherapy contours were directly drawn on the ct of the fdg-pet\\\/ct scan by expert radiation oncologists and thereafter used for treatment planning. for 207 of the 300 patients (69 %), the radiotherapy contours were drawn on a different ct scan dedicated to treatment planning and were propagated\\\/resampled to the fdg-pet\\\/ct scan reference frame using intensity-based free-form deformable registration with the software mim\\u00ae\\u00a0(mim software inc., cleveland, oh).patients with recurrent h&n cancer or with metastases at presentation, and patients receiving palliative treatment were excluded from the study. from the 300 patients, 48 received radiation alone (16 %) and 252 received chemo-radiation (84 %) with curative intent as part of treatment management. the median follow-up period of all patients was 43 months (range: 6-112). patients that did not develop a locoregional recurrence or distant metastases during the follow-up period and that had a follow-up time smaller than 24 months were also excluded from the study. during the follow-up period, 45 patients developed a locoregional recurrence (15 %), 40 patients developed distant metastases (13 %) and 56 patients died (19 %).\\u00a0\\u00a0we analyzed the fdg-pet and ct images of the 300 patients from four different cohorts for the risk assessment of locoregional recurrences (lr) and distant metastases in h&n cancer. prediction models combining radiomic and clinical variables were constructed via random forests and imbalance-adjustment strategies using two of the four cohorts. independent validation of the prediction and prognostic performance of the models was carried out on the other two cohorts (lr: auc = 0.69 and ci = 0.67; dm: auc = 0.86 and ci = 0.88). furthermore, the results obtained via kaplan-meier analysis demonstrated the potential of radiomics for assessing the risk of specific tumour outcomes using multiple stratification groups. head and neck cancer head-neck reg rtstruct ct rtdose rtplan pt demographic diagnosis follow-up treatment other clinical image analyses software\\\/source code community\"},{\"id\":42417,\"type\":\"collection\",\"slug\":\"head-neck-radiomics-hn1\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/head-neck-radiomics-hn1\\\/\",\"title\":\"HEAD-NECK-RADIOMICS-HN1\",\"short_title\":\"HEAD-NECK-RADIOMICS-HN1\",\"doi\":\"10.7937\\\/tcia.2019.8kap372n\",\"summary\":\"This collection contains clinical data and computed tomography (CT) from\\u00a0137\\u00a0head and neck squamous cell carcinoma (HNSCC) patients treated by radiotherapy. For these patients a pre-treatment CT scan was manual delineated by an experienced radiation oncologist of the 3D volume of the gross tumor volume.\\u00a0This dataset refers to the \\\"H&N1\\\" dataset of the study published in Nature Communications (http:\\\/\\\/doi.org\\\/10.1038\\\/ncomms5006).\\u00a0At time of previous publication, images of one subject had been unintentionally overlooked.\\u00a0In short, the publication used a radiomics approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer.Radiomics refers to the comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features. In the published analysis, 440 features quantifying tumor image intensity, shape, and texture were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumor heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.This dataset is provided as open access to support repeatability and reproducibility of research in radiomics. This dataset will be the subject of an upcoming article addressing FAIR radiomics practices to support transparency, harmonization and collaboration on radiomics.From version 2 (release date 09\\\/20\\\/2019) onwards we included the primary neoplasm gross tumour volume delineations in DICOM SEGMENTATION as well as DICOM RTSTRUCT files that accompanied the DICOM axial images. This dataset is provided as open access to support repeatability and reproducibility of research in radiomics. This dataset will be the subject of an upcoming article addressing FAIR radiomics practices to support transparency, harmonization and collaboration on radiomics.Other data sets in the Cancer Imaging Archive that were used in the same\\u00a0study published in Nature Communications: NSCLC-Radiomics, \\u00a0NSCLC-Radiomics-Genomics,\\u00a0NSCLC-Radiomics-Interobserver1,\\u00a0RIDER-LungCT-Seg.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NSCLC-RADIOMICS-GRAPHIC.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Head and Neck Cancer\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"CT\",\"SEG\",\"RTSTRUCT\",\"PT\",\"Demographic\",\"Follow-Up\",\"Other\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":137,\"date_updated\":\"2020-07-29\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=HEAD-NECK-RADIOMICS-HN1\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Head-Neck-Radiomics-HN1-Version-2-Sept-2019-NBIA-manifest.tcia\",\"blob\":\"head-neck-radiomics-hn1 head-neck-radiomics-hn1 this collection contains clinical data and computed tomography (ct) from\\u00a0137\\u00a0head and neck squamous cell carcinoma (hnscc) patients treated by radiotherapy. for these patients a pre-treatment ct scan was manual delineated by an experienced radiation oncologist of the 3d volume of the gross tumor volume.\\u00a0this dataset refers to the \\\"h&n1\\\" dataset of the study published in nature communications (http:\\\/\\\/doi.org\\\/10.1038\\\/ncomms5006).\\u00a0at time of previous publication, images of one subject had been unintentionally overlooked.\\u00a0in short, the publication used a radiomics approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer.radiomics refers to the comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features. in the published analysis, 440 features quantifying tumor image intensity, shape, and texture were extracted. we found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumor heterogeneity, was associated with underlying gene-expression patterns. these data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. this may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.this dataset is provided as open access to support repeatability and reproducibility of research in radiomics. this dataset will be the subject of an upcoming article addressing fair radiomics practices to support transparency, harmonization and collaboration on radiomics.from version 2 (release date 09\\\/20\\\/2019) onwards we included the primary neoplasm gross tumour volume delineations in dicom segmentation as well as dicom rtstruct files that accompanied the dicom axial images. this dataset is provided as open access to support repeatability and reproducibility of research in radiomics. this dataset will be the subject of an upcoming article addressing fair radiomics practices to support transparency, harmonization and collaboration on radiomics.other data sets in the cancer imaging archive that were used in the same\\u00a0study published in nature communications: nsclc-radiomics, \\u00a0nsclc-radiomics-genomics,\\u00a0nsclc-radiomics-interobserver1,\\u00a0rider-lungct-seg. head and neck cancer head-neck ct seg rtstruct pt demographic follow-up other clinical community\"},{\"id\":42745,\"type\":\"collection\",\"slug\":\"healthy-total-body-cts\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/healthy-total-body-cts\\\/\",\"title\":\"Low-Dose CT Images of Healthy Cohort\",\"short_title\":\"Healthy-Total-Body-CTs\",\"doi\":\"10.7937\\\/NC7Z-4F76\",\"summary\":\"This data set includes low-dose whole body CT images and tissue segmentations of thirty healthy adult research participants who underwent PET\\\/CT imaging on the uEXPLORER total-body PET\\\/CT system at UC Davis. Participants included in this study were healthy adults, 18 years of age or older, who were able to provide informed written consent. The participants' age, gender, weight, height, and body mass index are also provided.Fifteen participants underwent PET\\\/CT imaging at three timepoints during a 3-hour period (0 minutes, 90 minutes, and 180 minutes) after PET radiotracer injection, while the remaining 15 participants were imaged at six timepoints during a 12-hour period (additionally at 360 minutes, 540 minutes, and 720 minutes). The imaging timepoint is indicated in the Series Description DICOM tag, with a value of either 'dyn', '90min', '3hr', '6hr', '9hr', or '12hr', corresponding to the delay after PET tracer injection. CT images were acquired immediately before PET image acquisition. Currently, only CT images are included in the data set from either three or six timepoints. The tissue segmentations include 37 tissues consisting of 13 abdominal organs, 20 different bones, subcutaneous and visceral fat, skeletal and psoas muscle. Segmentations were automatically generated at the 90 minute timepoint for each participant using MOOSE, an AI segmentation tool for whole body data. The segmentations are provided in NIFTI format and may need to be re-oriented to correctly match the CT image data in DICOM format.The uEXPLORER CT scanner is an 80-row, 160 slice CT scanner typically used for anatomical imaging and attenuation correction for PET\\\/CT. The CT scan obtained at 90 minutes was performed with 140 kVp and an average of 50 mAs for all subjects. At all other time-points (0 minutes, 180 minutes, etc.) the CT scan was obtained with 140 kVp and an average of 5 mAs. CT images were reconstructed into a 512x512x828 image matrix with 0.9766x0.9766x2.344 mm3 voxel size.A key is provided along with the segmentations download in the Data Access table which details the organ values.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ct_seg_preview-e1702327637229.png\",\"program\":\"Community\",\"cancer_types\":[\"Non-Cancer\"],\"cancer_locations\":[\"Whole body\"],\"data_types\":[\"CT\",\"Segmentation\",\"Demographic\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":30,\"date_updated\":\"2024-09-27\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Healthy-Total-Body-CTs\",\"download_url\":\"https:\\\/\\\/www.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Healthy-Total-Body-Cts_v02_20240927.tcia\",\"blob\":\"low-dose ct images of healthy cohort healthy-total-body-cts this data set includes low-dose whole body ct images and tissue segmentations of thirty healthy adult research participants who underwent pet\\\/ct imaging on the uexplorer total-body pet\\\/ct system at uc davis. participants included in this study were healthy adults, 18 years of age or older, who were able to provide informed written consent. the participants' age, gender, weight, height, and body mass index are also provided.fifteen participants underwent pet\\\/ct imaging at three timepoints during a 3-hour period (0 minutes, 90 minutes, and 180 minutes) after pet radiotracer injection, while the remaining 15 participants were imaged at six timepoints during a 12-hour period (additionally at 360 minutes, 540 minutes, and 720 minutes). the imaging timepoint is indicated in the series description dicom tag, with a value of either 'dyn', '90min', '3hr', '6hr', '9hr', or '12hr', corresponding to the delay after pet tracer injection. ct images were acquired immediately before pet image acquisition. currently, only ct images are included in the data set from either three or six timepoints. the tissue segmentations include 37 tissues consisting of 13 abdominal organs, 20 different bones, subcutaneous and visceral fat, skeletal and psoas muscle. segmentations were automatically generated at the 90 minute timepoint for each participant using moose, an ai segmentation tool for whole body data. the segmentations are provided in nifti format and may need to be re-oriented to correctly match the ct image data in dicom format.the uexplorer ct scanner is an 80-row, 160 slice ct scanner typically used for anatomical imaging and attenuation correction for pet\\\/ct. the ct scan obtained at 90 minutes was performed with 140 kvp and an average of 50 mas for all subjects. at all other time-points (0 minutes, 180 minutes, etc.) the ct scan was obtained with 140 kvp and an average of 5 mas. ct images were reconstructed into a 512x512x828 image matrix with 0.9766x0.9766x2.344 mm3 voxel size.a key is provided along with the segmentations download in the data access table which details the organ values. non-cancer whole body ct segmentation demographic clinical image analyses community\"},{\"id\":42449,\"type\":\"collection\",\"slug\":\"her2-tumor-rois\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/her2-tumor-rois\\\/\",\"title\":\"HER2 and trastuzumab treatment response H&E slides with tumor ROI annotations\",\"short_title\":\"HER2 tumor ROIs\",\"doi\":\"10.7937\\\/E65C-AM96\",\"summary\":\"The current standard of care for many patients with HER2-positive breast cancer is neoadjuvant chemotherapy in combination with anti-HER2 agents, based on HER2 amplification as detected by in situ hybridization (ISH) or protein immunohistochemistry (IHC). However, hematoxylin & eosin (H&E) tumor stains are more commonly available, and accurate prediction of HER2 status and anti-HER2 treatment response from H&E would reduce costs and increase the speed of treatment selection. Computational algorithms for H&E have been effective in predicting a variety of cancer features and clinical outcomes, including moderate success in predicting HER2 status. We trained a CNN classifier on 188 H&E whole slide images (WSIs) manually annotated for tumor regions of interest (ROIs) by our pathology team. Our classifier achieved an area under the curve (AUC) of 0.90 in cross-validation of slide-level HER2 status and 0.81 on an independent TCGA test set. Moreover, we trained our classifier on pre-treatment samples from 187 HER2+ patients that subsequently received trastuzumab therapy. Our classifier achieved an AUC of 0.80 in a five-fold cross validation. Our work provides an H&E-based algorithm that can predict HER2 status and trastuzumab response in breast cancer at an accuracy that may benefit clinical evaluations. Here, we are providing the datasets used in the study to facilitate development of other HER2+ diagnosis and trastuzumab response applications.Data annotationAnnotation of digital slides was performed, circling areas of invasive carcinoma (Region of Interests, ROIs). The manual annotation of ROIs significantly enhances the prediction accuracy and reduces the need for extensively large datasets.\\u00a0 Regions of necrosis, in situ carcinoma or benign stroma and epithelium were excluded. The images were annotated with ROIs associated to HER2+\\\/- tumor area (TA) by a senior breast pathologist.\\u00a0 The annotations were marked tumor boundaries and annotated by Aperio ImageScope software. The annotations were exported from the Aperio software in The Extensible Markup Language (XML) format, including X and Y coordinates corresponding to the annotated regions. We used these coordinates for each slide image to tile these regions separately from the rest of the image, labeled as HER2+ or HER2- class.\\u00a0\\u00a0Data set descriptionsYale HER2 cohort:\\u00a0This dataset presents 192 cases of HER2 positive and negative invasive breast carcinomas H&E slides from the Yale Pathology electronic database. All tissues and data were retrieved under permission from the Yale Human Investigation Committee protocol #9505008219 to DLR. HER2 positive cases defined as those with 3+ score by immunohistochemistry (IHC) or an equivocal (2+) IHC score with subsequent amplification by fluorescence in situ hybridization (FISH) as defined by American Society of Clinical Oncology\\\/College of American Pathologists (ASCO\\\/CAP) clinical practice guidelines. H&E slides generated at Yale School of Medicine include 93 HER2+ and 99 HER2- slides. The slides were scanned at Yale Pathology Tissue Services and underwent a slide quality check before they went into the scanner. The tissue samples were scanned using Vectra Polaris by Perkin-Elmer scanner using bright field whole slides scanning at 20\\u00d7 magnification at Brady Memorial Laboratory Rimm\\u2019s lab.Yale trastuzumab response cohort:85\\u00a0response cohort cases were identified also by retrospective search of the Yale Pathology electronic database. Cases included those patients with a pre-treatment breast core biopsy with HER2 positive invasive breast carcinoma who then received neoadjuvant targeted therapy with trastuzumab +\\\/- pertuzumab prior to definitive surgery. HER2 positivity was defined as previously described for the HER2 negative\\\/positive cohort. The response to targeted therapy was obtained from the pathology reports of the surgical resection specimens and dichotomized into responders or non-responders. Those with a complete pathologic response, defined as no residual invasive, lymphovascular invasion or metastatic carcinoma, were designated as responders (n=36). Cases with only residual in situ carcinoma were included in the responder category. Those cases with any amount of residual invasive carcinoma, lymphovascular invasion or metastatic carcinoma were categorized as non-responders (n=49).TCGA HER2 cohort:\\u00a0A total of 668 TCGA-BRCA HER2+\\\/- samples with available HER2 status were downloaded from the GDC portal (see \\\"Additional Resources for this Dataset\\\" below). Slides were visually inspected by our pathology team to exclude low quality samples with tissue folding or those that appeared to be from frozen tissue. A total of 182 samples (90 HER2- and 92 HER2+) were retained for use as independent test set.\\u00a0 Information about which specific samples were retained can be found the\\u00a0TCGA_BRCA Filtered\\u00a0folder of the dataset.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/her2-roi.png\",\"program\":\"Community\",\"cancer_types\":[\"HER2+ Breast Cancer\",\"Metastatic disease\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"Histopathology\",\"Follow-Up\",\"Molecular Test\",\"Measurement\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":273,\"date_updated\":\"2022-08-01\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22HER2+tumor+ROIs%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjczOSIsInBhc3Njb2RlIjoiNzEwNmUzNDFjMDY4MjljNjBkMmM0ZjcxYTBhMTE1ODcxNGIzZWNjNSIsInBhY2thZ2VfaWQiOiI3MzkiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"her2 and trastuzumab treatment response h&e slides with tumor roi annotations her2 tumor rois the current standard of care for many patients with her2-positive breast cancer is neoadjuvant chemotherapy in combination with anti-her2 agents, based on her2 amplification as detected by in situ hybridization (ish) or protein immunohistochemistry (ihc). however, hematoxylin & eosin (h&e) tumor stains are more commonly available, and accurate prediction of her2 status and anti-her2 treatment response from h&e would reduce costs and increase the speed of treatment selection. computational algorithms for h&e have been effective in predicting a variety of cancer features and clinical outcomes, including moderate success in predicting her2 status. we trained a cnn classifier on 188 h&e whole slide images (wsis) manually annotated for tumor regions of interest (rois) by our pathology team. our classifier achieved an area under the curve (auc) of 0.90 in cross-validation of slide-level her2 status and 0.81 on an independent tcga test set. moreover, we trained our classifier on pre-treatment samples from 187 her2+ patients that subsequently received trastuzumab therapy. our classifier achieved an auc of 0.80 in a five-fold cross validation. our work provides an h&e-based algorithm that can predict her2 status and trastuzumab response in breast cancer at an accuracy that may benefit clinical evaluations. here, we are providing the datasets used in the study to facilitate development of other her2+ diagnosis and trastuzumab response applications.data annotationannotation of digital slides was performed, circling areas of invasive carcinoma (region of interests, rois). the manual annotation of rois significantly enhances the prediction accuracy and reduces the need for extensively large datasets.\\u00a0 regions of necrosis, in situ carcinoma or benign stroma and epithelium were excluded. the images were annotated with rois associated to her2+\\\/- tumor area (ta) by a senior breast pathologist.\\u00a0 the annotations were marked tumor boundaries and annotated by aperio imagescope software. the annotations were exported from the aperio software in the extensible markup language (xml) format, including x and y coordinates corresponding to the annotated regions. we used these coordinates for each slide image to tile these regions separately from the rest of the image, labeled as her2+ or her2- class.\\u00a0\\u00a0data set descriptionsyale her2 cohort:\\u00a0this dataset presents 192 cases of her2 positive and negative invasive breast carcinomas h&e slides from the yale pathology electronic database. all tissues and data were retrieved under permission from the yale human investigation committee protocol #9505008219 to dlr. her2 positive cases defined as those with 3+ score by immunohistochemistry (ihc) or an equivocal (2+) ihc score with subsequent amplification by fluorescence in situ hybridization (fish) as defined by american society of clinical oncology\\\/college of american pathologists (asco\\\/cap) clinical practice guidelines. h&e slides generated at yale school of medicine include 93 her2+ and 99 her2- slides. the slides were scanned at yale pathology tissue services and underwent a slide quality check before they went into the scanner. the tissue samples were scanned using vectra polaris by perkin-elmer scanner using bright field whole slides scanning at 20\\u00d7 magnification at brady memorial laboratory rimm\\u2019s lab.yale trastuzumab response cohort:85\\u00a0response cohort cases were identified also by retrospective search of the yale pathology electronic database. cases included those patients with a pre-treatment breast core biopsy with her2 positive invasive breast carcinoma who then received neoadjuvant targeted therapy with trastuzumab +\\\/- pertuzumab prior to definitive surgery. her2 positivity was defined as previously described for the her2 negative\\\/positive cohort. the response to targeted therapy was obtained from the pathology reports of the surgical resection specimens and dichotomized into responders or non-responders. those with a complete pathologic response, defined as no residual invasive, lymphovascular invasion or metastatic carcinoma, were designated as responders (n=36). cases with only residual in situ carcinoma were included in the responder category. those cases with any amount of residual invasive carcinoma, lymphovascular invasion or metastatic carcinoma were categorized as non-responders (n=49).tcga her2 cohort:\\u00a0a total of 668 tcga-brca her2+\\\/- samples with available her2 status were downloaded from the gdc portal (see \\\"additional resources for this dataset\\\" below). slides were visually inspected by our pathology team to exclude low quality samples with tissue folding or those that appeared to be from frozen tissue. a total of 182 samples (90 her2- and 92 her2+) were retained for use as independent test set.\\u00a0 information about which specific samples were retained can be found the\\u00a0tcga_brca filtered\\u00a0folder of the dataset. her2+ breast cancer metastatic disease breast histopathology follow-up molecular test measurement image analyses community\"},{\"id\":42513,\"type\":\"collection\",\"slug\":\"histologyhsi-gb\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/histologyhsi-gb\\\/\",\"title\":\"Hyperspectral Histological Images for Diagnosis of Human Glioblastoma\",\"short_title\":\"HistologyHSI-GB\",\"doi\":\"10.7937\\\/z1k6-vd17\",\"summary\":\"Hyperspectral imaging technology combines the main features of two existing technologies: conventional imaging and spectroscopy. Thus, hyperspectral cameras make it possible to analyze, at the same time and in a non-contact way, the morphological features and chemical composition of the objects captured. The information provided by hyperspectral imaging can be used to detect patterns, cells, or biomarkers to identify diseases. There are different alternatives for processing them and there is a lack of publicly available datasets of medical hyperspectral images. To the best of our knowledge, this is the first open access dataset containing histological hyperspectral images of glioblastoma brain tumors, which can be set as a benchmark for researchers to compare their approaches.This dataset includes 13 subjects.\\u00a0 Each subject has a single histological slide with multiple hyperspectral images captured from each slide where deemed relevant by the pathologists (this number varies for each slide). The database is composed of 469 annotated hyperspectral images from 13 histological slides (482 total images), having a spatial dimension of 800 \\u00d7 1004 pixels and a spectral dimension of 826 spectral channels. The format of the hyperspectral images is ENVI, the standard format for the storage of hyperspectral images. The ENVI format consists of a flat-binary raster file which may or may not have a file extension, accompanied by an ASCII header file (denoted as *.hdr). The data are stored in band-interleaved-by-line format. In addition, dark and white references were captured to perform a calibration of the raw image, which is a standard procedure in hyperspectral image processing.The slides were stained with hematoxylin and eosin and captured using a custom hyperspectral microscopic system at 20\\u00d7 magnification. The ground-truth annotation for this dataset is the diagnosis of the slides (tumor _T_ or not tumor _NT_ ) performed by skilled histopathologists after the visual examination of the stained slides, according to the World Health Organization classification of tumors of the nervous system. As far as we are concerned, there are no commercial hyperspectral whole slide scanners. Also, the availability of hyperspectral microscopes is still limited in the market.\\u00a0\\u00a0The microscope is an Olympus BX-53 (Olympus, Tokyo, Japan). The hyperspectral camera is a Hyperspec\\u00ae VNIR A-Series from HeadWall Photonics (Fitchburg, MA, USA), which is based on an imaging spectrometer coupled to a charge-coupled device sensor, the Adimec-1000m (Adimec, Eindhoven, Netherlands). This hyperspectral system works in the visual and near-infrared spectral range from 400 to 1000 nm with a spectral resolution of 2.8 nm, sampling 826 spectral channels, and 1004 spatial pixels. The push-broom camera performs a spatial scanning to acquire a hyperspectral cube with a mechanical stage (SCAN, M\\u00e4rzh\\u00e4user, Wetzlar, Germany) attached to the microscope, which provides an accurate movement of the slides. The objective lenses are from the LMPLFLN family (Olympus, Tokyo, Japan), optimized for infrared observations.More information about the dataset can be found in this manuscript.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/41597_2024_3510_Fig1_HTML.webp\",\"program\":\"Community\",\"cancer_types\":[\"Glioblastoma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"Histopathology\",\"Classification\"],\"supporting_data\":[\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":13,\"date_updated\":\"2024-05-24\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22HistologyHSI-GB%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc2NCIsInBhc3Njb2RlIjoiMGU0MzFhYTFjN2ZhOGMwZWJmNTRhZDVkZTQxOGY5NWVlNmYyMjhlNiIsInBhY2thZ2VfaWQiOiI3NjQiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"hyperspectral histological images for diagnosis of human glioblastoma histologyhsi-gb hyperspectral imaging technology combines the main features of two existing technologies: conventional imaging and spectroscopy. thus, hyperspectral cameras make it possible to analyze, at the same time and in a non-contact way, the morphological features and chemical composition of the objects captured. the information provided by hyperspectral imaging can be used to detect patterns, cells, or biomarkers to identify diseases. there are different alternatives for processing them and there is a lack of publicly available datasets of medical hyperspectral images. to the best of our knowledge, this is the first open access dataset containing histological hyperspectral images of glioblastoma brain tumors, which can be set as a benchmark for researchers to compare their approaches.this dataset includes 13 subjects.\\u00a0 each subject has a single histological slide with multiple hyperspectral images captured from each slide where deemed relevant by the pathologists (this number varies for each slide). the database is composed of 469 annotated hyperspectral images from 13 histological slides (482 total images), having a spatial dimension of 800 \\u00d7 1004 pixels and a spectral dimension of 826 spectral channels. the format of the hyperspectral images is envi, the standard format for the storage of hyperspectral images. the envi format consists of a flat-binary raster file which may or may not have a file extension, accompanied by an ascii header file (denoted as *.hdr). the data are stored in band-interleaved-by-line format. in addition, dark and white references were captured to perform a calibration of the raw image, which is a standard procedure in hyperspectral image processing.the slides were stained with hematoxylin and eosin and captured using a custom hyperspectral microscopic system at 20\\u00d7 magnification. the ground-truth annotation for this dataset is the diagnosis of the slides (tumor _t_ or not tumor _nt_ ) performed by skilled histopathologists after the visual examination of the stained slides, according to the world health organization classification of tumors of the nervous system. as far as we are concerned, there are no commercial hyperspectral whole slide scanners. also, the availability of hyperspectral microscopes is still limited in the market.\\u00a0\\u00a0the microscope is an olympus bx-53 (olympus, tokyo, japan). the hyperspectral camera is a hyperspec\\u00ae vnir a-series from headwall photonics (fitchburg, ma, usa), which is based on an imaging spectrometer coupled to a charge-coupled device sensor, the adimec-1000m (adimec, eindhoven, netherlands). this hyperspectral system works in the visual and near-infrared spectral range from 400 to 1000 nm with a spectral resolution of 2.8 nm, sampling 826 spectral channels, and 1004 spatial pixels. the push-broom camera performs a spatial scanning to acquire a hyperspectral cube with a mechanical stage (scan, m\\u00e4rzh\\u00e4user, wetzlar, germany) attached to the microscope, which provides an accurate movement of the slides. the objective lenses are from the lmplfln family (olympus, tokyo, japan), optimized for infrared observations.more information about the dataset can be found in this manuscript. glioblastoma brain histopathology classification software\\\/source code community\"},{\"id\":48465,\"type\":\"collection\",\"slug\":\"hnc-imrt-70-33\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/hnc-imrt-70-33\\\/\",\"title\":\"CT-RTSTRUCT-RTDOSE-RTPLAN Sets of Head and Neck Cancers Treated with Identical Prescriptions using IMRT: An Open Dataset for Deep Learning in Treatment Planning\",\"short_title\":\"HNC-IMRT-70-33\",\"doi\":\"10.7937\\\/ahqh-xc79\",\"summary\":\"This collection includes data from 211 patients who presented with head and neck cancer and were treated using radiation therapy at a single institution using 6 MV Intensity Modulated Radiation Therapy (IMRT) from a linear accelerator. All patients were prescribed identical prescriptions of 70 Gray (Gy) in 33 fractions for the primary planning target volume (PTV) and one, none, or a combination of integrated boost sub targets (PTV 54 Gy, PTV 56 Gy, PTV 57 Gy, PTV 60 Gy, PTV 63 Gy, PTV 66 Gy).\\u00a0 The data for each patient contains the minimum required information for radiation treatment planning. Each patient set contains the planning computed tomography (CT) image set from treatment simulation, the expert-defined radiotherapy structure set (RTSTRUCT), the delivered radiotherapy plan file (RTPLAN), and the calculated treatment dose (RTDOSE). All files are provided in DICOM format.\\u00a0CT Images: Planning CT images from treatment simulation and radiation dose calculation are included for each patient. Thermoplastic head and neck masks were used during image acquisition to achieve immobilization and ensure accurate and reproducible positioning during treatment. All images satisfied the resolution requirements for dose calculations and have an identical slice thicknesses of 2.5 mm. The provided CT files are those that match with the RTDOSE and RTSTRUCT files of the same patient.\\u00a0Target and Organ Contours: Each patient\\u2019s data contains an RTSTRUCT DICOM with manually defined target volume contours and organ at risk (OAR) contours. For efficient OAR extraction from the RTSTRUCT files, we renamed all OAR contours that could be identified to a uniform structure name. If a contour was not identified, it was not defined for treatment, or the nomenclature was ambiguous. All planning target volumes (PTV) are included in the structure set. Due to inconsistent documentation and naming conventions, the primary PTV is difficult to distinguish from other PTV structures. For example, some patients include multiple iterations of the same contour as a result of modifications during treatment planning our assisted planning methods from the TPS. In addition to organ and target structures, we chose to leave in planning structures created and used by the dosimetrists for generating the plan. For example, PTV expansions, ring structures, avoidance structures, and normal tissue volumes were left within the RTSTRUCT file. OAR contours for 26 unique normal tissue structures were renamed to the \\u2018Standardized Name\\u2019 in the RTSTRUCT DICOM file. If a structure was not identified, the structure was not created in the original structure set.\\u00a0Radiotherapy Dose and Treatment Plan: Each dose was planned by a dosimetrist and approved for treatment by the physician. Treatment plans were created using Pinnacle (Phillips Medical Systems, Fitchburg, WI). The dose was calculated on a 3 mm3 dose grid and plans were optimized for treatment on Elekta or Varian linear accelerators.Research for challenging tumor sites like head and neck cancers can benefit from publicly accessible radiotherapy treatment datasets such as the one offered here. In particular, this dataset provides consistent treatment plans with limited variance due to the uniformity in tumor site, consistent institutional standards, and identical prescriptions. In artificial intelligence research, the need for large and uniform datasets is crucial for testing and evaluating the performance of novel architectures. Treatments of a single site from a single institution and with identical prescriptions will have reduced variance of tumor coverage and normal tissue-sparing objectives in comparison to multi-site and multi-institutional studies. However, it can also be combined with other radiotherapy treatment datasets for use in more generalized studies.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Head and Neck Cancer\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"CT\",\"RTSTRUCT\",\"RTDOSE\",\"RTPLAN\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":211,\"date_updated\":\"2024-05-06\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=HNC-IMRT-70-33\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/HNC-IMRT-70-33_04-30-2024.tcia\",\"blob\":\"ct-rtstruct-rtdose-rtplan sets of head and neck cancers treated with identical prescriptions using imrt: an open dataset for deep learning in treatment planning hnc-imrt-70-33 this collection includes data from 211 patients who presented with head and neck cancer and were treated using radiation therapy at a single institution using 6 mv intensity modulated radiation therapy (imrt) from a linear accelerator. all patients were prescribed identical prescriptions of 70 gray (gy) in 33 fractions for the primary planning target volume (ptv) and one, none, or a combination of integrated boost sub targets (ptv 54 gy, ptv 56 gy, ptv 57 gy, ptv 60 gy, ptv 63 gy, ptv 66 gy).\\u00a0 the data for each patient contains the minimum required information for radiation treatment planning. each patient set contains the planning computed tomography (ct) image set from treatment simulation, the expert-defined radiotherapy structure set (rtstruct), the delivered radiotherapy plan file (rtplan), and the calculated treatment dose (rtdose). all files are provided in dicom format.\\u00a0ct images: planning ct images from treatment simulation and radiation dose calculation are included for each patient. thermoplastic head and neck masks were used during image acquisition to achieve immobilization and ensure accurate and reproducible positioning during treatment. all images satisfied the resolution requirements for dose calculations and have an identical slice thicknesses of 2.5 mm. the provided ct files are those that match with the rtdose and rtstruct files of the same patient.\\u00a0target and organ contours: each patient\\u2019s data contains an rtstruct dicom with manually defined target volume contours and organ at risk (oar) contours. for efficient oar extraction from the rtstruct files, we renamed all oar contours that could be identified to a uniform structure name. if a contour was not identified, it was not defined for treatment, or the nomenclature was ambiguous. all planning target volumes (ptv) are included in the structure set. due to inconsistent documentation and naming conventions, the primary ptv is difficult to distinguish from other ptv structures. for example, some patients include multiple iterations of the same contour as a result of modifications during treatment planning our assisted planning methods from the tps. in addition to organ and target structures, we chose to leave in planning structures created and used by the dosimetrists for generating the plan. for example, ptv expansions, ring structures, avoidance structures, and normal tissue volumes were left within the rtstruct file. oar contours for 26 unique normal tissue structures were renamed to the \\u2018standardized name\\u2019 in the rtstruct dicom file. if a structure was not identified, the structure was not created in the original structure set.\\u00a0radiotherapy dose and treatment plan: each dose was planned by a dosimetrist and approved for treatment by the physician. treatment plans were created using pinnacle (phillips medical systems, fitchburg, wi). the dose was calculated on a 3 mm3 dose grid and plans were optimized for treatment on elekta or varian linear accelerators.research for challenging tumor sites like head and neck cancers can benefit from publicly accessible radiotherapy treatment datasets such as the one offered here. in particular, this dataset provides consistent treatment plans with limited variance due to the uniformity in tumor site, consistent institutional standards, and identical prescriptions. in artificial intelligence research, the need for large and uniform datasets is crucial for testing and evaluating the performance of novel architectures. treatments of a single site from a single institution and with identical prescriptions will have reduced variance of tumor coverage and normal tissue-sparing objectives in comparison to multi-site and multi-institutional studies. however, it can also be combined with other radiotherapy treatment datasets for use in more generalized studies. head and neck cancer head-neck ct rtstruct rtdose rtplan other community\"},{\"id\":42503,\"type\":\"collection\",\"slug\":\"hnscc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/hnscc\\\/\",\"title\":\"HNSCC\",\"short_title\":\"HNSCC\",\"doi\":\"10.7937\\\/k9\\\/tcia.2020.a8sh-7363\",\"summary\":\"This collection contains imaging, radiation therapy, and clinical data from 627 head and neck squamous cell carcinoma (HNSCC) patients\\u00a0at MD Anderson Cancer Center.\\u00a0 Researchers at MDACC analyzed these patients' data as part of two separate research projects.\\u00a0 70 of the patients were selected for inclusion in\\u00a0both projects. This collection provides access to all data for the full set of patients involved in both projects.\\u00a0 Download options for the individual studies are available by following the links in each project description below.The first project: Head-Neck-CT-AtlasThe first project screened\\u00a02840 consecutive patients with HNSCC treated with curative-intent RT at MD Anderson Cancer Center from 2003 to 2013.\\u00a0Patients with whole-body PET-CT or abdominal CT scans both before and after RT (n=215) were selected for the cohort.\\u00a0 De-identified diagnostic imaging, radiation treatment planning, and follow up imaging are provided. Using cross sectional imaging, total body skeletal muscle and adipose content were calculated before and after treatment.\\u00a0\\u00a0All imaging data are subject- and date-matched to clinical data from each patient, including demographics, risk factors, grade, stage, recurrence, and survival.\\u00a0Open access to these data allows for inter-institutional comparisons of complete RT details in non-randomized patient populations, allowing for a more granular understanding of three dimensional factors that influence treatment effectiveness and toxicity sparing.\\u00a0 More information about this study and links to download the corresponding patient\\u00a0subset of this collection including clinical data can be found in the\\u00a0\\\"Head-Neck-CT-Atlas\\\"\\u00a0dataset.The second project: Radiomics outcome prediction in Oropharyngeal cancerThe second project was intended to address the\\u00a0unmet need for integrating quantitative imaging biomarkers into current risk stratification tools and\\u00a0to explore the correlation between radiomics features \\u2013alone or in combination with clinical prognosticators- and tumor outcome.\\u00a0 Clinical meta-data and matched baseline contrast-enhanced computed tomography (CECT) scans were used to build a\\u00a0cohort of 495 oropharyngeal cancer (OPC) patients\\u00a0treated\\u00a0between 2005 and 2012.\\u00a0\\u00a0Expert radiation oncologists manually segmented primary and nodal disease gross volumes (GTVp & GTVn). Structures were named per the American Association of Physicists in Medicine (AAPM) TG-263 recommendations, then retrieved in RT-STRUCT format. Matched patient, disease, treatment and outcomes data were obtained. Radiomics analysis was performed using an open-source institutionally-developed software that runs on Matlab platform.\\u00a0\\u00a0More information about this study and links to download the corresponding patient\\u00a0subset of this collection including clinical data can be found in the \\\"Radiomics outcome prediction in Oropharyngeal cancer\\\" dataset.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Head and Neck Squamous Cell Carcinoma\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"CT\",\"PT\",\"RTPLAN\",\"RTSTRUCT\",\"RTDOSE\",\"MR\",\"Diagnosis\",\"Follow-Up\",\"Treatment\",\"Other\",\"Demographic\",\"Exposure\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":627,\"date_updated\":\"2024-05-15\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=HNSCC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/HNSCC.tcia\",\"blob\":\"hnscc hnscc this collection contains imaging, radiation therapy, and clinical data from 627 head and neck squamous cell carcinoma (hnscc) patients\\u00a0at md anderson cancer center.\\u00a0 researchers at mdacc analyzed these patients' data as part of two separate research projects.\\u00a0 70 of the patients were selected for inclusion in\\u00a0both projects. this collection provides access to all data for the full set of patients involved in both projects.\\u00a0 download options for the individual studies are available by following the links in each project description below.the first project: head-neck-ct-atlasthe first project screened\\u00a02840 consecutive patients with hnscc treated with curative-intent rt at md anderson cancer center from 2003 to 2013.\\u00a0patients with whole-body pet-ct or abdominal ct scans both before and after rt (n=215) were selected for the cohort.\\u00a0 de-identified diagnostic imaging, radiation treatment planning, and follow up imaging are provided. using cross sectional imaging, total body skeletal muscle and adipose content were calculated before and after treatment.\\u00a0\\u00a0all imaging data are subject- and date-matched to clinical data from each patient, including demographics, risk factors, grade, stage, recurrence, and survival.\\u00a0open access to these data allows for inter-institutional comparisons of complete rt details in non-randomized patient populations, allowing for a more granular understanding of three dimensional factors that influence treatment effectiveness and toxicity sparing.\\u00a0 more information about this study and links to download the corresponding patient\\u00a0subset of this collection including clinical data can be found in the\\u00a0\\\"head-neck-ct-atlas\\\"\\u00a0dataset.the second project: radiomics outcome prediction in oropharyngeal cancerthe second project was intended to address the\\u00a0unmet need for integrating quantitative imaging biomarkers into current risk stratification tools and\\u00a0to explore the correlation between radiomics features \\u2013alone or in combination with clinical prognosticators- and tumor outcome.\\u00a0 clinical meta-data and matched baseline contrast-enhanced computed tomography (cect) scans were used to build a\\u00a0cohort of 495 oropharyngeal cancer (opc) patients\\u00a0treated\\u00a0between 2005 and 2012.\\u00a0\\u00a0expert radiation oncologists manually segmented primary and nodal disease gross volumes (gtvp & gtvn). structures were named per the american association of physicists in medicine (aapm) tg-263 recommendations, then retrieved in rt-struct format. matched patient, disease, treatment and outcomes data were obtained. radiomics analysis was performed using an open-source institutionally-developed software that runs on matlab platform.\\u00a0\\u00a0more information about this study and links to download the corresponding patient\\u00a0subset of this collection including clinical data can be found in the \\\"radiomics outcome prediction in oropharyngeal cancer\\\" dataset. head and neck squamous cell carcinoma head-neck ct pt rtplan rtstruct rtdose mr diagnosis follow-up treatment other demographic exposure clinical image analyses community\"},{\"id\":42361,\"type\":\"collection\",\"slug\":\"hnscc-3dct-rt\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/hnscc-3dct-rt\\\/\",\"title\":\"Head-and-neck squamous cell carcinoma patients with CT taken during pre-treatment, mid-treatment, and post-treatment\",\"short_title\":\"HNSCC-3DCT-RT\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2018.13upr2xf\",\"summary\":\"Researchers will have the unique opportunity to visualize how head-and-neck squamous cell carcinoma (HNSCC) patients\\u2019 3D CT image data changes with radiotherapy between pre-treatment, mid-treatment, and post-treatment. They will also have the advantage to receive additional information such as a) photon energy, b) number of arcs for volumetric modulated arc radiotherapy (VMAT) therapy, c) tumor volume and location, d) CT-to-density table, e) treatment related toxicities for parotid glands and oral cavity, f) patient age and weight, patient chemotherapy medication, g) prior surgery, h) performance status, i) histology, j) disease stage, k) number of targets, l) organs at risk, and m) other structures delineated on the CT image.This data collection contains three dimensional high-resolution (3D) fan-beam CT scans collected during pre-treatment, mid-treatment, and post-treatment using a Siemens 16-slice CT scanner with standard clinical protocol for 31 head-and-neck squamous cell carcinoma (HNSCC) patients. Patients underwent radiotherapy treatment to a total dose of 58-70 Gy, using daily 2-2.20 Gy, fractions for 30-35 fractions. The pre-treatment planning CT scans were performed with a median of 13 days (range: 2-27) before treatment, the mid-treatment CT was performed at fraction 17 (range: 8-26), and the post-treatment CT around fraction 30 (range: 26th fraction - 21 weeks post treatment). In addition to the imaging data, the data sets contain contours of anatomical structures for radiotherapy. The contours were generated by the attending radiation oncologists for treatment planning purposes, and subsequently treatment plans were generated with Eclipse\\u00ae (Varian Medical Systems, Inc., Palo Alto, CA) treatment planning system. In addition to CT image data, included are: a) photon energy, b) number of arcs for volumetric modulated arc radiotherapy (VMAT) therapy, c) tumor volume and location, d) CT-to-density table, e) treatment related toxicities for parotid glands and oral cavity, f) patient age and weight, patient chemotherapy medication, g) prior surgery, h) performance status, i) histology, j) disease stage, k) number of targets, l) organs at risk, and m) other structures delineated on the CT image.All patients were recruited according to the following criteria. Inclusion: i) patients had histologically confirmed head-and-neck tumors, ii) were treated with definitive radiation therapy or concurrent chemoradiation therapy, iii) patients treated with post-operative radiation therapy, for whom the gross tumor volume (GTV) or the resection cavity were visible on CT, such that it could be delineated as a target for radiotherapy, iv) patients were informed of the investigational nature of this study and were able to understand and to sign a written informed consent document.Exclusion: i) pregnant or nursing women were not eligible to participate, ii) women of reproductive potential must have been offered a pre-treatment pregnancy test and informed of the need to practice an effective contraceptive method, iii) patients younger than 18 years, and iv) patients whose size and weight would not allow CT scanning.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Head and Neck Squamous Cell Carcinoma\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"CT\",\"RTSTRUCT\",\"RTDOSE\",\"Demographic\",\"Diagnosis\",\"Treatment\",\"Measurement\",\"Follow-Up\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":31,\"date_updated\":\"2019-02-06\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=HNSCC-3DCT-RT\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_HNSCC-3DCT-RT_v2_Jan2019.tcia\",\"blob\":\"head-and-neck squamous cell carcinoma patients with ct taken during pre-treatment, mid-treatment, and post-treatment hnscc-3dct-rt researchers will have the unique opportunity to visualize how head-and-neck squamous cell carcinoma (hnscc) patients\\u2019 3d ct image data changes with radiotherapy between pre-treatment, mid-treatment, and post-treatment. they will also have the advantage to receive additional information such as a) photon energy, b) number of arcs for volumetric modulated arc radiotherapy (vmat) therapy, c) tumor volume and location, d) ct-to-density table, e) treatment related toxicities for parotid glands and oral cavity, f) patient age and weight, patient chemotherapy medication, g) prior surgery, h) performance status, i) histology, j) disease stage, k) number of targets, l) organs at risk, and m) other structures delineated on the ct image.this data collection contains three dimensional high-resolution (3d) fan-beam ct scans collected during pre-treatment, mid-treatment, and post-treatment using a siemens 16-slice ct scanner with standard clinical protocol for 31 head-and-neck squamous cell carcinoma (hnscc) patients. patients underwent radiotherapy treatment to a total dose of 58-70 gy, using daily 2-2.20 gy, fractions for 30-35 fractions. the pre-treatment planning ct scans were performed with a median of 13 days (range: 2-27) before treatment, the mid-treatment ct was performed at fraction 17 (range: 8-26), and the post-treatment ct around fraction 30 (range: 26th fraction - 21 weeks post treatment). in addition to the imaging data, the data sets contain contours of anatomical structures for radiotherapy. the contours were generated by the attending radiation oncologists for treatment planning purposes, and subsequently treatment plans were generated with eclipse\\u00ae (varian medical systems, inc., palo alto, ca) treatment planning system. in addition to ct image data, included are: a) photon energy, b) number of arcs for volumetric modulated arc radiotherapy (vmat) therapy, c) tumor volume and location, d) ct-to-density table, e) treatment related toxicities for parotid glands and oral cavity, f) patient age and weight, patient chemotherapy medication, g) prior surgery, h) performance status, i) histology, j) disease stage, k) number of targets, l) organs at risk, and m) other structures delineated on the ct image.all patients were recruited according to the following criteria. inclusion: i) patients had histologically confirmed head-and-neck tumors, ii) were treated with definitive radiation therapy or concurrent chemoradiation therapy, iii) patients treated with post-operative radiation therapy, for whom the gross tumor volume (gtv) or the resection cavity were visible on ct, such that it could be delineated as a target for radiotherapy, iv) patients were informed of the investigational nature of this study and were able to understand and to sign a written informed consent document.exclusion: i) pregnant or nursing women were not eligible to participate, ii) women of reproductive potential must have been offered a pre-treatment pregnancy test and informed of the need to practice an effective contraceptive method, iii) patients younger than 18 years, and iv) patients whose size and weight would not allow ct scanning. head and neck squamous cell carcinoma head-neck ct rtstruct rtdose demographic diagnosis treatment measurement follow-up image analyses community\"},{\"id\":41331,\"type\":\"collection\",\"slug\":\"hnscc-mif-mihc-comparison\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/hnscc-mif-mihc-comparison\\\/\",\"title\":\"AI-ready restained and co-registered multiplex dataset for head-and-neck carcinoma\",\"short_title\":\"HNSCC-mIF-mIHC-comparison\",\"doi\":\"10.7937\\\/TCIA.2020.T90F-WB82\",\"summary\":\"\\u00a0We introduce a new AI-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. Specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mIF) assay first and then restained with cheaper multiplex immunohistochemistry (mIHC). This is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases; due to the equivalence, our cheaper mIHC staining protocol can offset the need for expensive mIF staining\\\/scanning which requires highly skilled lab technicians. As opposed to subjective and error-prone immune cell annotations from individual pathologists (disagreement > 50%) to drive SOTA deep learning approaches, this dataset provides objective immune and tumor cell annotations via mIF\\\/mIHC restaining for more reproducible and accurate characterization of tumor immune microenvironment (e.g. for immunotherapy). We demonstrate the effectiveness of this dataset in three use cases: (1) IHC quantification of CD3\\\/CD8 tumor-infiltrating lymphocytes via style transfer, (2) virtual translation of cheap mIHC stains to more expensive mIF stains, and (3) virtual tumor\\\/immune cellular phenotyping on standard hematoxylin images. The code for stain translation is available at\\u00a0https:\\\/\\\/github.com\\\/nadeemlab\\\/DeepLIIF\\u00a0and the code for performing interactive deep learning whole-cell\\\/nuclear segmentation is available at\\u00a0https:\\\/\\\/github.com\\\/nadeemlab\\\/impartial. After scanning the full images, nine regions of interest (ROIs) from each slide\\\/Case were chosen by an experienced pathologist on both mIF and mIHC images: three in the tumor core (T), three at the tumor margin (M),and three outside in the adjacent stroma (S) area. These individual ROIs were further subdivided into four 512x512 patches with indices [0_0], [0_1], [1_0], [1_1]. The final notation for each file is Case[patient_id]_[T\\\/M\\\/S][1\\\/2\\\/3]_[ROI_index]_[Marker_name]. More details can be found in the paper.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/mIHC-mIF.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Head and Neck Cancer\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"Histopathology\"],\"supporting_data\":[\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":8,\"date_updated\":\"2023-08-31\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22HNSCC-mIF-mIHC-comparison%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjczNiIsInBhc3Njb2RlIjoiZjQxYTIyNGNmNThiOTFlOGZkNjUzYTIzY2M5MDE1ZDk0OGY2ZjA5MSIsInBhY2thZ2VfaWQiOiI3MzYiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"ai-ready restained and co-registered multiplex dataset for head-and-neck carcinoma hnscc-mif-mihc-comparison \\u00a0we introduce a new ai-ready computational pathology dataset containing restained and co-registered digitized images from eight head-and-neck squamous cell carcinoma patients. specifically, the same tumor sections were stained with the expensive multiplex immunofluorescence (mif) assay first and then restained with cheaper multiplex immunohistochemistry (mihc). this is a first public dataset that demonstrates the equivalence of these two staining methods which in turn allows several use cases; due to the equivalence, our cheaper mihc staining protocol can offset the need for expensive mif staining\\\/scanning which requires highly skilled lab technicians. as opposed to subjective and error-prone immune cell annotations from individual pathologists (disagreement > 50%) to drive sota deep learning approaches, this dataset provides objective immune and tumor cell annotations via mif\\\/mihc restaining for more reproducible and accurate characterization of tumor immune microenvironment (e.g. for immunotherapy). we demonstrate the effectiveness of this dataset in three use cases: (1) ihc quantification of cd3\\\/cd8 tumor-infiltrating lymphocytes via style transfer, (2) virtual translation of cheap mihc stains to more expensive mif stains, and (3) virtual tumor\\\/immune cellular phenotyping on standard hematoxylin images. the code for stain translation is available at\\u00a0https:\\\/\\\/github.com\\\/nadeemlab\\\/deepliif\\u00a0and the code for performing interactive deep learning whole-cell\\\/nuclear segmentation is available at\\u00a0https:\\\/\\\/github.com\\\/nadeemlab\\\/impartial. after scanning the full images, nine regions of interest (rois) from each slide\\\/case were chosen by an experienced pathologist on both mif and mihc images: three in the tumor core (t), three at the tumor margin (m),and three outside in the adjacent stroma (s) area. these individual rois were further subdivided into four 512x512 patches with indices [0_0], [0_1], [1_0], [1_1]. the final notation for each file is case[patient_id]_[t\\\/m\\\/s][1\\\/2\\\/3]_[roi_index]_[marker_name]. more details can be found in the paper. head and neck cancer head-neck histopathology image analyses software\\\/source code community\"},{\"id\":42187,\"type\":\"collection\",\"slug\":\"hungarian-colorectal-screening\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/hungarian-colorectal-screening\\\/\",\"title\":\"Digital pathological slides from Hungarian colorectal cancer screening\",\"short_title\":\"Hungarian-Colorectal-Screening\",\"doi\":\"10.7937\\\/TCIA.9CJF-0127\",\"summary\":\"In this study, 200 digital whole-slide images are published which were collected via hematoxylin-eosin stained colorectal biopsy. This dataset contains the raw MIRAX (mrxs) formatted data. The samples were selected from the archives of the 2nd Department of Pathology of Semmelweis University, Budapest and were scanned with a 3DHistech Pannoramic 1000 Digital Slide Scanner at the highest available, 40x magnification. This is a single center dataset ensuring consequent and homogeneous data processing and patient handling. The related publication shows, how these data can be utilized for training an artificial neural network in order to detect pathological conditions.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Colorectal Cancer\"],\"cancer_locations\":[\"Colon\"],\"data_types\":[\"Histopathology\",\"Demographic\",\"Diagnosis\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":200,\"date_updated\":\"2022-09-20\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=\\\/system\\\/files\\\/collectionmetadata\\\/202405\\\/Hungarian-Colorectal-Screening_02-29-2024.json\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc0MSIsInBhc3Njb2RlIjoiYmM0NmFjYzJjZTg2MTdhYTQ3N2JhMmY3ZThmZjk3YzUxNDY1ZTIzZiIsInBhY2thZ2VfaWQiOiI3NDEiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"digital pathological slides from hungarian colorectal cancer screening hungarian-colorectal-screening in this study, 200 digital whole-slide images are published which were collected via hematoxylin-eosin stained colorectal biopsy. this dataset contains the raw mirax (mrxs) formatted data. the samples were selected from the archives of the 2nd department of pathology of semmelweis university, budapest and were scanned with a 3dhistech pannoramic 1000 digital slide scanner at the highest available, 40x magnification. this is a single center dataset ensuring consequent and homogeneous data processing and patient handling. the related publication shows, how these data can be utilized for training an artificial neural network in order to detect pathological conditions. colorectal cancer colon histopathology demographic diagnosis clinical image analyses community\"},{\"id\":41783,\"type\":\"collection\",\"slug\":\"icdc-glioma\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/icdc-glioma\\\/\",\"title\":\"Canine glioma characterization project for ICDC\",\"short_title\":\"ICDC-Glioma\",\"doi\":\"10.7937\\\/TCIA.SVQT-Q016\",\"summary\":\"Sporadic gliomas in companion dogs provide a window on the interaction between tumorigenic mechanisms and host environment. We compared the molecular profiles of canine gliomas with those of human pediatric and adult gliomas to characterize evolutionarily conserved mammalian mutational processes in gliomagenesis. Employing whole genome-, exome-, transcriptome- and methylation-sequencing of 81 canine gliomas, our cross-species comparative genomic analysis provides unique insights into glioma etiology and the chronology of glioma-causing somatic alterations, and rationalizes sporadic canine glioma as a preclinical model tailored to measuring treatment efficacies in patients with canine or human glioma. Here, we provide high-throughput multi-omics sequencing data in binary alignment map (BAM) format for the largest canine glioma cohort to date.This dataset includes baseline pathologic, MRI and genomic features of treatment-naive naturally-occurring canine glioma. Multi-omic data for all 81 subjects can be found in the Integrated Canine Data Commons.\\u00a0 Radiology imaging are available for 57 of these subjects, and pathology imaging for 76 subjects.\\u00a0The data were collected from 5 different veterinary institutions' patient populations. Clinical MRI are available prior to any treatment, and surgical biopsy and\\\/or tissues collected at necropsy are provided as H&E stained sections. The tumors have been classified with a harmonized classification scheme developed by veterinary and physician neuropathologists. The genomic features were described using whole exome, whole genome, RNAseq and methylation profiling. This is the first dataset of its kind to comprehensively describe and report the clinical, pathologic, imaging and genomic landscape of naturally-occurring canine glioma. This work serves the cancer research community by providing the necessary multi-parametric data to define the translational relevance of this canine tumor as a model for its human counterpart(s). Inclusion of appropriate canine glioma patients with similar and\\\/or relevant features can be studied in the context of comparative oncology clinical trials to advance questions in cancer biology and drug development.About the Integrated Canine Data CommonsNCI's Division of Cancer Treatment and Diagnosis (DCTD) charged the Frederick National Laboratory for Cancer Research (FNLCR) to build the Integrated Canine Data Commons (ICDC), a cloud-based repository of spontaneously-arising canine cancer data. ICDC was established to further research on human cancers by enabling comparative analysis with canine cancer. The data in the ICDC is sourced from multiple different programs and projects; all focused on canine subjects. The data is harmonized into an integrated data model and then made available to the research community. The ICDC is part of the Cancer Research Data Commons (CRDC), an initiative from NCI\\u2019s Center for Biomedical Informatics and Information Technology (CBIIT). Bioinformatic analysis of the ICDC data is accomplished using the CRDC\\u2019s\\u00a0Cloud Resources.\",\"image\":false,\"program\":\"ICDC\",\"cancer_types\":[\"Glioma\"],\"cancer_locations\":[\"Head\"],\"data_types\":[\"MR\",\"Histopathology\"],\"supporting_data\":[\"Genomics\"],\"species\":[\"Canine\"],\"subjects\":78,\"date_updated\":\"2021-01-12\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=ICDC-Glioma\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ICDC-Glioma.tcia\",\"blob\":\"canine glioma characterization project for icdc icdc-glioma sporadic gliomas in companion dogs provide a window on the interaction between tumorigenic mechanisms and host environment. we compared the molecular profiles of canine gliomas with those of human pediatric and adult gliomas to characterize evolutionarily conserved mammalian mutational processes in gliomagenesis. employing whole genome-, exome-, transcriptome- and methylation-sequencing of 81 canine gliomas, our cross-species comparative genomic analysis provides unique insights into glioma etiology and the chronology of glioma-causing somatic alterations, and rationalizes sporadic canine glioma as a preclinical model tailored to measuring treatment efficacies in patients with canine or human glioma. here, we provide high-throughput multi-omics sequencing data in binary alignment map (bam) format for the largest canine glioma cohort to date.this dataset includes baseline pathologic, mri and genomic features of treatment-naive naturally-occurring canine glioma. multi-omic data for all 81 subjects can be found in the integrated canine data commons.\\u00a0 radiology imaging are available for 57 of these subjects, and pathology imaging for 76 subjects.\\u00a0the data were collected from 5 different veterinary institutions' patient populations. clinical mri are available prior to any treatment, and surgical biopsy and\\\/or tissues collected at necropsy are provided as h&e stained sections. the tumors have been classified with a harmonized classification scheme developed by veterinary and physician neuropathologists. the genomic features were described using whole exome, whole genome, rnaseq and methylation profiling. this is the first dataset of its kind to comprehensively describe and report the clinical, pathologic, imaging and genomic landscape of naturally-occurring canine glioma. this work serves the cancer research community by providing the necessary multi-parametric data to define the translational relevance of this canine tumor as a model for its human counterpart(s). inclusion of appropriate canine glioma patients with similar and\\\/or relevant features can be studied in the context of comparative oncology clinical trials to advance questions in cancer biology and drug development.about the integrated canine data commonsnci's division of cancer treatment and diagnosis (dctd) charged the frederick national laboratory for cancer research (fnlcr) to build the integrated canine data commons (icdc), a cloud-based repository of spontaneously-arising canine cancer data. icdc was established to further research on human cancers by enabling comparative analysis with canine cancer. the data in the icdc is sourced from multiple different programs and projects; all focused on canine subjects. the data is harmonized into an integrated data model and then made available to the research community. the icdc is part of the cancer research data commons (crdc), an initiative from nci\\u2019s center for biomedical informatics and information technology (cbiit). bioinformatic analysis of the icdc data is accomplished using the crdc\\u2019s\\u00a0cloud resources. glioma head mr histopathology genomics icdc\"},{\"id\":42851,\"type\":\"collection\",\"slug\":\"ispy1\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ispy1\\\/\",\"title\":\"Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1\\\/ACRIN 6657 trials\",\"short_title\":\"ISPY1\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.HdHpgJLK\",\"summary\":\"ACRIN 6657   was designed as a prospective study to test MRI for ability to predict response to treatment and risk-of-recurrence in patients with stage 2 or 3 breast cancer receiving neoadjuvant chemotherapy (NACT).   ACRIN 6657   was conducted as a companion study to CALGB 150007, a correlative science study evaluating tissue-based biomarkers in the setting of neoadjuvant treatment of breast cancer.\\u00a0Collectively, CALGB 150007 and ACRIN 6657 formed the basis of the multicenter\\u00a0Investigation of\\u00a0Serial Studies to\\u00a0Predict\\u00a0Your\\u00a0Therapeutic\\u00a0Response with\\u00a0Imaging and moLecular\\u00a0Analysis (I-SPY TRIAL) breast cancer trial, a study of imaging and tissue-based biomarkers for predicting pathologic complete response (pCR) and recurrence-free survival (RFS). Additional information about the trial is available in the Study Protocol and Case Report Forms.\\u00a0Participant Eligibility and Enrollment: Criteria for inclusion were patients enrolling on CALGB 150007 with T3 tumors measuring at least 3 cm in diameter by clinical exam or imaging and receiving neoadjuvant chemotherapy with an\\u00a0\\u00a0anthracycline-cyclophosphamide regimen alone or followed by a taxane. Pregnant patients and those with ferromagnetic prostheses were excluded from the study. The study was open to enrollment from May 2002 to March 2006. 237 patients were enrolled, of which 230 met eligibility criteria.\",\"image\":false,\"program\":\"NCI Trials\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MR\",\"SEG\",\"Other\",\"Demographic\",\"Molecular Test\",\"Follow-Up\",\"Classification\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":222,\"date_updated\":\"2016-09-28\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=ISPY1\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/doiJNLP-ISPY1-full-09-26-2016.tcia\",\"blob\":\"multi-center breast dce-mri data and segmentations from patients in the i-spy 1\\\/acrin 6657 trials ispy1 acrin 6657   was designed as a prospective study to test mri for ability to predict response to treatment and risk-of-recurrence in patients with stage 2 or 3 breast cancer receiving neoadjuvant chemotherapy (nact).   acrin 6657   was conducted as a companion study to calgb 150007, a correlative science study evaluating tissue-based biomarkers in the setting of neoadjuvant treatment of breast cancer.\\u00a0collectively, calgb 150007 and acrin 6657 formed the basis of the multicenter\\u00a0investigation of\\u00a0serial studies to\\u00a0predict\\u00a0your\\u00a0therapeutic\\u00a0response with\\u00a0imaging and molecular\\u00a0analysis (i-spy trial) breast cancer trial, a study of imaging and tissue-based biomarkers for predicting pathologic complete response (pcr) and recurrence-free survival (rfs). additional information about the trial is available in the study protocol and case report forms.\\u00a0participant eligibility and enrollment: criteria for inclusion were patients enrolling on calgb 150007 with t3 tumors measuring at least 3 cm in diameter by clinical exam or imaging and receiving neoadjuvant chemotherapy with an\\u00a0\\u00a0anthracycline-cyclophosphamide regimen alone or followed by a taxane. pregnant patients and those with ferromagnetic prostheses were excluded from the study. the study was open to enrollment from may 2002 to march 2006. 237 patients were enrolled, of which 230 met eligibility criteria. breast cancer breast mr seg other demographic molecular test follow-up classification clinical image analyses nci trials\"},{\"id\":42613,\"type\":\"collection\",\"slug\":\"ispy2\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ispy2\\\/\",\"title\":\"I-SPY 2 Breast Dynamic Contrast Enhanced MRI Trial\",\"short_title\":\"ISPY2\",\"doi\":\"10.7937\\\/TCIA.D8Z0-9T85\",\"summary\":\"I-SPY 2 (Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And moLecular analysis 2) is an ongoing, multi-center trial (NCT01042379) designed to quickly evaluate the efficacy of new agents for breast cancer in neoadjuvant chemotherapy (NAC) setting. Women aged \\u226518 years diagnosed with locally advanced breast cancer (tumor size \\u22652.5 cm) without distant metastasis are eligible to enroll in the trial. Patients with breast cancer at high-risk for recurrence are adaptively-randomized to either control arm (standard NAC) or one of several concurrent experimental drug arms. I-SPY 2 utilizes change in tumor volume by DCE-MRI at serial time-points during NAC to adjust the randomization schema as the trial proceeds, preferentially assigning patients to receive agents showing an increasing likelihood of efficacy against their breast cancer subtype. I-SPY 2 opened in Spring 2010 and is ongoing.This collection includes DCE MRI data (original acquired images and derived parametric maps) for I-SPY 2 patients adaptively randomized between 2010 and 2016, along with histopathologic outcome data. Breast MRI data in this collection was acquired prospectively at over 22 clinical centers using a standardized image acquisition protocol. Patients underwent 4 MRI exams before and during NAC and over 95% of the DCE imaging data met acceptance criteria for analysis of functional tumor volume (FTV), a demonstrated quantitative imaging marker of breast cancer response to NAC. This is a comprehensive, highly curated imaging data set with histopathologic outcome that can be used to develop, test and compare imaging metrics and prediction models for breast cancer response to treatment. Original but uncurated T2weighted MRI images are also included for most studies.719 patients are included in this collection. In combination with 266 patients from the ACRIN 6698\\\/I-SPY2 Breast DWI Collection these 985 patients comprise \\\"I-SPY2 Imaging Cohort 1\\\", the 1st subgroup of publicly released imaging data from the I-SPY 2 TRIAL. All subjects in the I-SPY2 Imaging Cohort 1 were enrolled in I-SPY 2 from 2010 to 2016 and randomized to one of nine completed experimental drug arms or control arm. In order to obtain the MRI data for the entire I-SPY2 Imaging Cohort 1 please download using the manifest file provided in the Data Access table below as this will include the patients from both collections. Downloading this collection alone will result in an incomplete cohort of 719 patients and any results from that patient set will not be comparable to published results from the trial.In addition, a retrospective study using a 384 patient subset (median age: 49\\u2009y\\\/o) was performed to test if prediction models combining multiple MRI features outperform models with single features. Four features were quantitatively calculated in each MRI exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. Logistic regression analysis was used to study the relationship between MRI variables and pathologic complete response (pCR). Predictive performance was estimated using the area under the receiver operating characteristic curve (AUC). The cohort was stratified by hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) status (positive or negative). Results showed analysis with combined features achieved higher AUCs than analysis with any feature alone.\\u00a0 These results are further described in the associated  publication and in the multi-feature MRI spreadsheet included with this Collection.I-SPY2 Imaging Biomarker GroupWe are establishing the I-SPY 2 \\u200bImaging \\u200bBiomarker \\u200bSubgroup to provide a forum where imaging scientists and radiologists can explore,\\u200b discuss, and strategize on the use of I-SPY 2 MRI data \\u200bavailable through\\u00a0TCIA. \\u200bPlease access\\u00a0https:\\\/\\\/forms.office.com\\\/r\\\/u0xzwB9kbU to be considered to join our bimonthly Zoom meeting invite list.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/I-SPY2_schema_TCIA.jpg\",\"program\":\"NCI Trials\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MR\",\"SEG\",\"Demographic\",\"Molecular Test\",\"Follow-Up\",\"Treatment\",\"Radiomic Feature\",\"Measurement\",\"Other\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":719,\"date_updated\":\"2022-05-02\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=ISPY2\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ISPY2-719-patients-only-manifest-Jan-2022.tcia\",\"blob\":\"i-spy 2 breast dynamic contrast enhanced mri trial ispy2 i-spy 2 (investigation of serial studies to predict your therapeutic response with imaging and molecular analysis 2) is an ongoing, multi-center trial (nct01042379) designed to quickly evaluate the efficacy of new agents for breast cancer in neoadjuvant chemotherapy (nac) setting. women aged \\u226518 years diagnosed with locally advanced breast cancer (tumor size \\u22652.5 cm) without distant metastasis are eligible to enroll in the trial. patients with breast cancer at high-risk for recurrence are adaptively-randomized to either control arm (standard nac) or one of several concurrent experimental drug arms. i-spy 2 utilizes change in tumor volume by dce-mri at serial time-points during nac to adjust the randomization schema as the trial proceeds, preferentially assigning patients to receive agents showing an increasing likelihood of efficacy against their breast cancer subtype. i-spy 2 opened in spring 2010 and is ongoing.this collection includes dce mri data (original acquired images and derived parametric maps) for i-spy 2 patients adaptively randomized between 2010 and 2016, along with histopathologic outcome data. breast mri data in this collection was acquired prospectively at over 22 clinical centers using a standardized image acquisition protocol. patients underwent 4 mri exams before and during nac and over 95% of the dce imaging data met acceptance criteria for analysis of functional tumor volume (ftv), a demonstrated quantitative imaging marker of breast cancer response to nac. this is a comprehensive, highly curated imaging data set with histopathologic outcome that can be used to develop, test and compare imaging metrics and prediction models for breast cancer response to treatment. original but uncurated t2weighted mri images are also included for most studies.719 patients are included in this collection. in combination with 266 patients from the acrin 6698\\\/i-spy2 breast dwi collection these 985 patients comprise \\\"i-spy2 imaging cohort 1\\\", the 1st subgroup of publicly released imaging data from the i-spy 2 trial. all subjects in the i-spy2 imaging cohort 1 were enrolled in i-spy 2 from 2010 to 2016 and randomized to one of nine completed experimental drug arms or control arm. in order to obtain the mri data for the entire i-spy2 imaging cohort 1 please download using the manifest file provided in the data access table below as this will include the patients from both collections. downloading this collection alone will result in an incomplete cohort of 719 patients and any results from that patient set will not be comparable to published results from the trial.in addition, a retrospective study using a 384 patient subset (median age: 49\\u2009y\\\/o) was performed to test if prediction models combining multiple mri features outperform models with single features. four features were quantitatively calculated in each mri exam: functional tumor volume, longest diameter, sphericity, and contralateral background parenchymal enhancement. logistic regression analysis was used to study the relationship between mri variables and pathologic complete response (pcr). predictive performance was estimated using the area under the receiver operating characteristic curve (auc). the cohort was stratified by hormone receptor (hr) and human epidermal growth factor receptor 2 (her2) status (positive or negative). results showed analysis with combined features achieved higher aucs than analysis with any feature alone.\\u00a0 these results are further described in the associated  publication and in the multi-feature mri spreadsheet included with this collection.i-spy2 imaging biomarker groupwe are establishing the i-spy 2 \\u200bimaging \\u200bbiomarker \\u200bsubgroup to provide a forum where imaging scientists and radiologists can explore,\\u200b discuss, and strategize on the use of i-spy 2 mri data \\u200bavailable through\\u00a0tcia. \\u200bplease access\\u00a0https:\\\/\\\/forms.office.com\\\/r\\\/u0xzwb9kbu to be considered to join our bimonthly zoom meeting invite list. breast cancer breast mr seg demographic molecular test follow-up treatment radiomic feature measurement other clinical image analyses nci trials\"},{\"id\":42621,\"type\":\"collection\",\"slug\":\"ivygap\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ivygap\\\/\",\"title\":\"Ivy Glioblastoma Atlas Project\",\"short_title\":\"IvyGAP\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.XLwaN6nL\",\"summary\":\"This data collection consists of MRI\\\/CT scan data for brain tumor patients that form the cohort for the resource Ivy Glioblastoma Atlas Project (Ivy GAP). There are 390 studies for 39 patients that include pre-surgery, post-surgery and follow up scans. The Ivy GAP is a collaborative partnership between the Ben and Catherine Ivy Foundation, which generously provided the financial support, the Allen Institute for Brain Science, and the Ben and Catherine Ivy Center for Advanced Brain Tumor Treatment. The goal of the project is to provide online resources to scientists and physicians dedicated to the development of innovative treatments and diagnostics that will enhance the quality of life and survival of patients with brain cancer.These resources represent an unprecedented platform for exploring the anatomic and genetic basis of glioblastoma at the cellular and molecular levels. In addition to the DICOM images in TCIA there are two interactive databases linked together by de-identified tumor specimen numbers to facilitate comparisons across data modalities: Ivy Glioblastoma Atlas Project  - An open\\\/public database providing in situ hybridization (ISH) and RNA sequencing (RNA-Seq) data, which map gene expression across the anatomic structures and putative cancer stem cell clusters in glioblastoma. The associated histological dataset is annotated and is suitable for neuropathological examination.Ivy GAP Clinical and Genomic Database -\\u00a0 A database offering detailed clinical, genomic, and expression array data sets that are designed to elucidate the pathways involved in glioblastoma development and progression. This database requires registration for access.The Ivy GAP is described in the resource paper (70 authors not all listed here) :\\u00a0Puchalski, R. B., Shah, N., \\u2026, Foltz, G. D. (2018). An anatomic transcriptional atlas of human glioblastoma. In Science (Vol. 360, Issue 6389, pp. 660\\u2013663).\\u00a0 https:\\\/\\\/doi.org\\\/10.1126\\\/science.aaf2666\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Glioblastoma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\"],\"supporting_data\":[\"Clinical\",\"Genomics\"],\"species\":[\"Human\"],\"subjects\":39,\"date_updated\":\"2016-12-30\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=IvyGAP\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/doiJNLP-IvyGAP.tcia\",\"blob\":\"ivy glioblastoma atlas project ivygap this data collection consists of mri\\\/ct scan data for brain tumor patients that form the cohort for the resource ivy glioblastoma atlas project (ivy gap). there are 390 studies for 39 patients that include pre-surgery, post-surgery and follow up scans. the ivy gap is a collaborative partnership between the ben and catherine ivy foundation, which generously provided the financial support, the allen institute for brain science, and the ben and catherine ivy center for advanced brain tumor treatment. the goal of the project is to provide online resources to scientists and physicians dedicated to the development of innovative treatments and diagnostics that will enhance the quality of life and survival of patients with brain cancer.these resources represent an unprecedented platform for exploring the anatomic and genetic basis of glioblastoma at the cellular and molecular levels. in addition to the dicom images in tcia there are two interactive databases linked together by de-identified tumor specimen numbers to facilitate comparisons across data modalities: ivy glioblastoma atlas project  - an open\\\/public database providing in situ hybridization (ish) and rna sequencing (rna-seq) data, which map gene expression across the anatomic structures and putative cancer stem cell clusters in glioblastoma. the associated histological dataset is annotated and is suitable for neuropathological examination.ivy gap clinical and genomic database -\\u00a0 a database offering detailed clinical, genomic, and expression array data sets that are designed to elucidate the pathways involved in glioblastoma development and progression. this database requires registration for access.the ivy gap is described in the resource paper (70 authors not all listed here) :\\u00a0puchalski, r. b., shah, n., \\u2026, foltz, g. d. (2018). an anatomic transcriptional atlas of human glioblastoma. in science (vol. 360, issue 6389, pp. 660\\u2013663).\\u00a0 https:\\\/\\\/doi.org\\\/10.1126\\\/science.aaf2666 glioblastoma brain mr clinical genomics community\"},{\"id\":42761,\"type\":\"collection\",\"slug\":\"lctsc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/lctsc\\\/\",\"title\":\"Lung CT Segmentation Challenge 2017\",\"short_title\":\"LCTSC\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2017.3R3FVZ08\",\"summary\":\"This data set was provided in association with an AAPM Thoracic Auto-segmentation challenge competition and a\\u00a0related conference session called Auto-Segmentation for Thoracic Radiation Treatment Planning: A Grand Challenge\\u00a0conducted at the AAPM 2017 Annual Meeting. The initial winners were announced at the AAPM meeting, but the competition website remains open to others who wish to see how their algorithms perform.Numerous auto-segmentation methods exist for Organs at Risk in radiotherapy. The overall objective of this auto-segmentation grand challenge is to provide a platform for comparison of various auto-segmentation algorithms when they are used to delineate organs at risk (OARs) from CT images for thoracic patients in radiation treatment planning. The results will provide an indication of the performances achieved by various auto-segmentation algorithms and can be used to guide the selection of these algorithms for clinic use if desirable.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"RTSTRUCT\",\"CT\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":60,\"date_updated\":\"2020-02-25\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=LCTSC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/LCTSC_v2_20190508.tcia\",\"blob\":\"lung ct segmentation challenge 2017 lctsc this data set was provided in association with an aapm thoracic auto-segmentation challenge competition and a\\u00a0related conference session called auto-segmentation for thoracic radiation treatment planning: a grand challenge\\u00a0conducted at the aapm 2017 annual meeting. the initial winners were announced at the aapm meeting, but the competition website remains open to others who wish to see how their algorithms perform.numerous auto-segmentation methods exist for organs at risk in radiotherapy. the overall objective of this auto-segmentation grand challenge is to provide a platform for comparison of various auto-segmentation algorithms when they are used to delineate organs at risk (oars) from ct images for thoracic patients in radiation treatment planning. the results will provide an indication of the performances achieved by various auto-segmentation algorithms and can be used to guide the selection of these algorithms for clinic use if desirable. lung cancer lung rtstruct ct image analyses community\"},{\"id\":42733,\"type\":\"collection\",\"slug\":\"ldct-and-projection-data\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ldct-and-projection-data\\\/\",\"title\":\"Low Dose CT Image and Projection Data\",\"short_title\":\"LDCT-and-Projection-data\",\"doi\":\"10.7937\\\/9NPB-2637\",\"summary\":\"Investigators at the Mayo Clinic, with funding from the National Institute of Biomedical Imaging and Bioengineering (EB 017095 and EB 017185), have built a library of CT patient projection data in an open and vendor-neutral format. This format, referred to as DICOM-CT-PD (Additional information regarding the CT projection data format in the article by Chen et al at doi: 10.1118\\\/1.4935406), is an extended DICOM format that contains CT projection data and acquisition geometry.\\u00a0 The de-identified patient projection data in the library were decoded with help of the manufacturer and have been converted into an open standardized format.Reconstructed images, patient age and gender, and pathology annotation are also provided for these de-identified data sets. The library consists of scans from various exam types, including non-contrast head CT scans acquired for acute cognitive or motor deficit, low-dose non-contrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced CT scans of the abdomen acquired to look for metastatic liver lesions.2016 Low Dose CT Grand Challenge\\u00a0The 2016 Low Dose CT Grand Challenge, sponsored by the AAPM, NIBIB, and Mayo Clinic, used\\u00a030 contrast-enhanced abdominal CT patient scans, 10 for training and 20 for testing. Thirteen of the 20 testing datasets from the Grand Challenge were subsequently included in this larger collection of CT image and projection data (TCIA LDCT-and-Projection-data).\\u00a0Because of the frequency of requests received by Mayo and the AAPM for the complete 2016 Grand Challenge dataset, on September 21, 2021 all 30 cases were updated to use the same projection data format as used for the TCIA data library and made publicly available in a single location.\\u00a0Please refer to the READ ME file at that location for a mapping between the case ID numbers used in the 2016 Grand Challenge and the case ID numbers used in the TCIA library for the 13 cases that exist in both libraries.Additional information about the 2016 Low Dose CT Grand Challenge can be found on the\\u00a0AAPM website\\u00a0and in the\\u00a0Medical Physics\\u00a0paper\\u00a0by McCollough et al.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/LDCT-PD-logo.png\",\"program\":\"Community\",\"cancer_types\":[\"Various\"],\"cancer_locations\":[\"Head\",\"Chest\",\"Abdomen\"],\"data_types\":[\"CT\",\"Other\",\"Protocol\",\"Demographic\",\"Diagnosis\",\"Measurement\"],\"supporting_data\":[\"Clinical\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":299,\"date_updated\":\"2023-04-03\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=LDCT-and-Projection-data\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/LDCT-and-Projection-data-March-2022.tcia\",\"blob\":\"low dose ct image and projection data ldct-and-projection-data investigators at the mayo clinic, with funding from the national institute of biomedical imaging and bioengineering (eb 017095 and eb 017185), have built a library of ct patient projection data in an open and vendor-neutral format. this format, referred to as dicom-ct-pd (additional information regarding the ct projection data format in the article by chen et al at doi: 10.1118\\\/1.4935406), is an extended dicom format that contains ct projection data and acquisition geometry.\\u00a0 the de-identified patient projection data in the library were decoded with help of the manufacturer and have been converted into an open standardized format.reconstructed images, patient age and gender, and pathology annotation are also provided for these de-identified data sets. the library consists of scans from various exam types, including non-contrast head ct scans acquired for acute cognitive or motor deficit, low-dose non-contrast chest scans acquired to screen high-risk patients for pulmonary nodules, and contrast-enhanced ct scans of the abdomen acquired to look for metastatic liver lesions.2016 low dose ct grand challenge\\u00a0the 2016 low dose ct grand challenge, sponsored by the aapm, nibib, and mayo clinic, used\\u00a030 contrast-enhanced abdominal ct patient scans, 10 for training and 20 for testing. thirteen of the 20 testing datasets from the grand challenge were subsequently included in this larger collection of ct image and projection data (tcia ldct-and-projection-data).\\u00a0because of the frequency of requests received by mayo and the aapm for the complete 2016 grand challenge dataset, on september 21, 2021 all 30 cases were updated to use the same projection data format as used for the tcia data library and made publicly available in a single location.\\u00a0please refer to the read me file at that location for a mapping between the case id numbers used in the 2016 grand challenge and the case id numbers used in the tcia library for the 13 cases that exist in both libraries.additional information about the 2016 low dose ct grand challenge can be found on the\\u00a0aapm website\\u00a0and in the\\u00a0medical physics\\u00a0paper\\u00a0by mccollough et al. various head chest abdomen ct other protocol demographic diagnosis measurement clinical software\\\/source code community\"},{\"id\":42643,\"type\":\"collection\",\"slug\":\"lgg-1p19qdeletion\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/lgg-1p19qdeletion\\\/\",\"title\":\"LGG-1p19qDeletion\",\"short_title\":\"LGG-1p19qDeletion\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2017.DWEHTZ9V\",\"summary\":\"These MRIs are pre-operative examinations performed in 159 subjects with Low Grade Gliomas (WHO grade II & III). Segmentation of tumors in three axial slices that include the one with the largest tumor\\u00a0diameter and ones below and above are provided in NiFTI format.\\u00a0\\u00a0Tumor grade and histologic type are also available.\\u00a0\\u00a0All of these subjects have biopsy proven 1p\\\/19q results, performed using FISH.\\u00a0\\u00a0For the 1p\\\/19q status \\\"n\\\/n\\\" means neither 1p nor 19q were deleted. \\\"d\\\/d\\\" means 1p and 19q are co-deleted.\\u00a0\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Low Grade Glioma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"SEG\",\"Molecular Test\",\"Diagnosis\"],\"supporting_data\":[\"Genomics\",\"Segmentations\"],\"species\":[\"Human\"],\"subjects\":159,\"date_updated\":\"2020-06-26\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=LGG-1p19qDeletion\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/LGG-1p19qDeletion_v2_MRandSEG_Jun2020.tcia\",\"blob\":\"lgg-1p19qdeletion lgg-1p19qdeletion these mris are pre-operative examinations performed in 159 subjects with low grade gliomas (who grade ii & iii). segmentation of tumors in three axial slices that include the one with the largest tumor\\u00a0diameter and ones below and above are provided in nifti format.\\u00a0\\u00a0tumor grade and histologic type are also available.\\u00a0\\u00a0all of these subjects have biopsy proven 1p\\\/19q results, performed using fish.\\u00a0\\u00a0for the 1p\\\/19q status \\\"n\\\/n\\\" means neither 1p nor 19q were deleted. \\\"d\\\/d\\\" means 1p and 19q are co-deleted.\\u00a0 low grade glioma brain mr seg molecular test diagnosis genomics segmentations community\"},{\"id\":42151,\"type\":\"collection\",\"slug\":\"lidc-idri\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/lidc-idri\\\/\",\"title\":\"Data from The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans\",\"short_title\":\"LIDC-IDRI\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.LO9QL9SX\",\"summary\":\"The Lung Image Database Consortium image collection (LIDC-IDRI) consists of diagnostic and lung cancer screening thoracic computed tomography (CT) scans with marked-up annotated lesions. It is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (CAD) methods for lung cancer detection and diagnosis. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.Seven academic centers and eight medical imaging companies collaborated to create this data set which contains 1018 cases. \\u00a0Each subject includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories (\\\"nodule > or =3 mm,\\\" \\\"nodule <3 mm,\\\" and \\\"non-nodule > or =3 mm\\\"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus. Note : The TCIA team strongly encourages users to review pylidc and the Standardized representation of the TCIA LIDC-IDRI annotations using DICOM (DICOM-LIDC-IDRI-Nodules)\\u00a0of the annotations\\\/segmentations included in this dataset before developing custom tools to analyze the XML version.\\u00a0\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Lung Cancer\",\"Non-Cancer\",\"Metastatic disease\"],\"cancer_locations\":[\"Chest\"],\"data_types\":[\"DX\",\"CT\",\"CR\",\"Other\",\"Measurement\",\"Radiomic Feature\",\"Diagnosis\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":1010,\"date_updated\":\"2020-09-21\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=LIDC-IDRI\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_LIDC-IDRI_20200921.tcia\",\"blob\":\"data from the lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans lidc-idri the lung image database consortium image collection (lidc-idri) consists of diagnostic and lung cancer screening thoracic computed tomography (ct) scans with marked-up annotated lesions. it is a web-accessible international resource for development, training, and evaluation of computer-assisted diagnostic (cad) methods for lung cancer detection and diagnosis. initiated by the national cancer institute (nci), further advanced by the foundation for the national institutes of health (fnih), and accompanied by the food and drug administration (fda) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.seven academic centers and eight medical imaging companies collaborated to create this data set which contains 1018 cases. \\u00a0each subject includes images from a clinical thoracic ct scan and an associated xml file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. in the initial blinded-read phase, each radiologist independently reviewed each ct scan and marked lesions belonging to one of three categories (\\\"nodule > or =3 mm,\\\" \\\"nodule <3 mm,\\\" and \\\"non-nodule > or =3 mm\\\"). in the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. the goal of this process was to identify as completely as possible all lung nodules in each ct scan without requiring forced consensus. note : the tcia team strongly encourages users to review pylidc and the standardized representation of the tcia lidc-idri annotations using dicom (dicom-lidc-idri-nodules)\\u00a0of the annotations\\\/segmentations included in this dataset before developing custom tools to analyze the xml version.\\u00a0 lung cancer non-cancer metastatic disease chest dx ct cr other measurement radiomic feature diagnosis clinical image analyses software\\\/source code community\"},{\"id\":42277,\"type\":\"collection\",\"slug\":\"lung-fused-ct-pathology\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/lung-fused-ct-pathology\\\/\",\"title\":\"Fused Radiology-Pathology Lung Dataset\",\"short_title\":\"Lung-Fused-CT-Pathology\",\"doi\":\"10.7937\\\/k9\\\/tcia.2018.smt36lpn\",\"summary\":\"This is the first attempt of mapping the extent of Invasive Adenocarcinoma onto in vivo lung CT. The mappings constitute ground truth of disease and may be used to further investigate the imaging signatures of Invasive Adenocarcinoma in ground glass pulmonary nodules. Patient with small ground glass nodules with >2 histology slices per nodule were included. Patients with solid large nodules (>40mm), with <3 histology slices or with histology slices showing substantial artifacts were excluded from this study (see reference below for details). Data collection and analysis was provided by Case Western Reserve University.\\u00a0ReferencesAll the program scripts that were used for generating the results and data in this paper have been made available at\\u00a0 https:\\\/\\\/github.com\\\/mirabelarusu\\\/RadPathFusionLungThis study is described in detail in the following publication:Rusu M., Rajiah P., Gilkeson R., Yang M., Donatelli C., Thawani R., Jacono F.J., Linden P.,\\u00a0 Madabushi A. (2017) Co-registration of pre-operative CT with ex vivo surgically excised ground glass nodules to define spatial extent of invasive adenocarcinoma on in vivo imaging: a proof-of-concept study. European Radiology 27:10, 4209:4217. DOI:\\u00a0https:\\\/\\\/doi.org\\\/10.1007\\\/s00330-017-4813-0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Lung-Fused-CT-Pathology-featured.png\",\"program\":\"Community\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"SEG\",\"Histopathology\",\"Demographic\",\"Measurement\",\"Other\"],\"supporting_data\":[\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":6,\"date_updated\":\"2018-07-30\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=Lung-Fused-CT-Pathology\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_Lung-Fused-CT-Pathology-2018-07-30.tcia\",\"blob\":\"fused radiology-pathology lung dataset lung-fused-ct-pathology this is the first attempt of mapping the extent of invasive adenocarcinoma onto in vivo lung ct. the mappings constitute ground truth of disease and may be used to further investigate the imaging signatures of invasive adenocarcinoma in ground glass pulmonary nodules. patient with small ground glass nodules with >2 histology slices per nodule were included. patients with solid large nodules (>40mm), with <3 histology slices or with histology slices showing substantial artifacts were excluded from this study (see reference below for details). data collection and analysis was provided by case western reserve university.\\u00a0referencesall the program scripts that were used for generating the results and data in this paper have been made available at\\u00a0 https:\\\/\\\/github.com\\\/mirabelarusu\\\/radpathfusionlungthis study is described in detail in the following publication:rusu m., rajiah p., gilkeson r., yang m., donatelli c., thawani r., jacono f.j., linden p.,\\u00a0 madabushi a. (2017) co-registration of pre-operative ct with ex vivo surgically excised ground glass nodules to define spatial extent of invasive adenocarcinoma on in vivo imaging: a proof-of-concept study. european radiology 27:10, 4209:4217. doi:\\u00a0https:\\\/\\\/doi.org\\\/10.1007\\\/s00330-017-4813-0 lung cancer lung ct seg histopathology demographic measurement other image analyses software\\\/source code community\"},{\"id\":41365,\"type\":\"collection\",\"slug\":\"lung-pet-ct-dx\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/lung-pet-ct-dx\\\/\",\"title\":\"A Large-Scale CT and PET\\\/CT Dataset for Lung Cancer Diagnosis\",\"short_title\":\"Lung-PET-CT-Dx\",\"doi\":\"10.7937\\\/TCIA.2020.NNC2-0461\",\"summary\":\"This dataset consists of CT and PET-CT DICOM images of lung cancer subjects with XML Annotation files that indicate tumor location with bounding boxes.\\u00a0The images were retrospectively acquired from patients with suspicion of lung cancer, and who\\u00a0underwent standard-of-care lung biopsy and PET\\\/CT. Subjects were grouped according to a tissue histopathological diagnosis. Patients with Names\\\/IDs containing the letter 'A' were diagnosed with Adenocarcinoma, 'B' with Small Cell Carcinoma, 'E' with Large Cell Carcinoma, and 'G' with Squamous Cell Carcinoma.The images were analyzed on the mediastinum (window width, 350 HU; level, 40 HU) and lung (window width, 1,400 HU; level, \\u2013700 HU) settings. The reconstructions were made in 2mm-slice-thick and lung settings. The CT slice interval varies from 0.625 mm to 5 mm. Scanning mode includes plain, contrast and 3D reconstruction.\\u00a0Before the examination, the patient underwent fasting for at least 6 hours, and the blood glucose of each patient was less than 11 mmol\\\/L. Whole-body emission scans were acquired 60 minutes after the intravenous injection of 18F-FDG (4.44MBq\\\/kg, 0.12mCi\\\/kg), with patients in the supine position in the PET scanner. FDG doses and uptake times were 168.72-468.79MBq (295.8\\u00b164.8MBq) and 27-171min (70.4\\u00b124.9 minutes), respectively. 18F-FDG with a radiochemical purity of 95% was provided. Patients were allowed to breathe normally during PET and CT acquisitions. Attenuation correction of PET images was performed using CT data with the hybrid segmentation method.\\u00a0 Attenuation corrections were performed using a CT protocol (180mAs,120kV,1.0pitch). Each study comprised one CT volume, one PET volume and fused PET and CT images: the CT resolution was 512 \\u00d7 512 pixels at 1mm \\u00d7 1mm, the PET resolution was 200 \\u00d7 200 pixels at 4.07mm \\u00d7 4.07mm, with a slice thickness and an interslice distance of 1mm. Both volumes were reconstructed with the same number of slices. Three-dimensional (3D) emission and transmission scanning were acquired from the base of the skull to mid femur. The PET images were reconstructed via the TrueX TOF method with a slice thickness of 1mm.\\u00a0\\u00a0The location of each tumor was annotated by five academic thoracic radiologists with expertise in lung cancer to make this dataset a useful tool and resource for developing algorithms for medical diagnosis.\\u00a0 Two of the radiologists had more than 15 years of experience and the others had more than 5 years of experience. After one of the radiologists labeled each subject the other four radiologists performed a verification, resulting in all five radiologists reviewing each annotation file in the dataset.\\u00a0\\u00a0Annotations were captured using Labellmg.\\u00a0\\u00a0The image annotations are saved as XML files in PASCAL VOC format, which can be parsed using the PASCAL Development Toolkit:\\u00a0\\u00a0https:\\\/\\\/pypi.org\\\/project\\\/pascal-voc-tools\\\/.\\u00a0 Python code to visualize the annotation boxes on top of the DICOM images can be downloaded here.Two deep learning researchers used the images and the corresponding annotation files to train several well-known detection models which resulted in a\\u00a0maximum a posteriori probability (MAP)\\u00a0of around 0.87 on the validation set.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/graphic.png\",\"program\":\"Community\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"PT\",\"Radiomic Feature\",\"Other\",\"Demographic\",\"Diagnosis\",\"Exposure\",\"Pathology Detail\"],\"supporting_data\":[\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":355,\"date_updated\":\"2020-12-22\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Lung-PET-CT-Dx\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Lung-PET-CT-Dx-NBIA-Manifest-122220.tcia\",\"blob\":\"a large-scale ct and pet\\\/ct dataset for lung cancer diagnosis lung-pet-ct-dx this dataset consists of ct and pet-ct dicom images of lung cancer subjects with xml annotation files that indicate tumor location with bounding boxes.\\u00a0the images were retrospectively acquired from patients with suspicion of lung cancer, and who\\u00a0underwent standard-of-care lung biopsy and pet\\\/ct. subjects were grouped according to a tissue histopathological diagnosis. patients with names\\\/ids containing the letter 'a' were diagnosed with adenocarcinoma, 'b' with small cell carcinoma, 'e' with large cell carcinoma, and 'g' with squamous cell carcinoma.the images were analyzed on the mediastinum (window width, 350 hu; level, 40 hu) and lung (window width, 1,400 hu; level, \\u2013700 hu) settings. the reconstructions were made in 2mm-slice-thick and lung settings. the ct slice interval varies from 0.625 mm to 5 mm. scanning mode includes plain, contrast and 3d reconstruction.\\u00a0before the examination, the patient underwent fasting for at least 6 hours, and the blood glucose of each patient was less than 11 mmol\\\/l. whole-body emission scans were acquired 60 minutes after the intravenous injection of 18f-fdg (4.44mbq\\\/kg, 0.12mci\\\/kg), with patients in the supine position in the pet scanner. fdg doses and uptake times were 168.72-468.79mbq (295.8\\u00b164.8mbq) and 27-171min (70.4\\u00b124.9 minutes), respectively. 18f-fdg with a radiochemical purity of 95% was provided. patients were allowed to breathe normally during pet and ct acquisitions. attenuation correction of pet images was performed using ct data with the hybrid segmentation method.\\u00a0 attenuation corrections were performed using a ct protocol (180mas,120kv,1.0pitch). each study comprised one ct volume, one pet volume and fused pet and ct images: the ct resolution was 512 \\u00d7 512 pixels at 1mm \\u00d7 1mm, the pet resolution was 200 \\u00d7 200 pixels at 4.07mm \\u00d7 4.07mm, with a slice thickness and an interslice distance of 1mm. both volumes were reconstructed with the same number of slices. three-dimensional (3d) emission and transmission scanning were acquired from the base of the skull to mid femur. the pet images were reconstructed via the truex tof method with a slice thickness of 1mm.\\u00a0\\u00a0the location of each tumor was annotated by five academic thoracic radiologists with expertise in lung cancer to make this dataset a useful tool and resource for developing algorithms for medical diagnosis.\\u00a0 two of the radiologists had more than 15 years of experience and the others had more than 5 years of experience. after one of the radiologists labeled each subject the other four radiologists performed a verification, resulting in all five radiologists reviewing each annotation file in the dataset.\\u00a0\\u00a0annotations were captured using labellmg.\\u00a0\\u00a0the image annotations are saved as xml files in pascal voc format, which can be parsed using the pascal development toolkit:\\u00a0\\u00a0https:\\\/\\\/pypi.org\\\/project\\\/pascal-voc-tools\\\/.\\u00a0 python code to visualize the annotation boxes on top of the dicom images can be downloaded here.two deep learning researchers used the images and the corresponding annotation files to train several well-known detection models which resulted in a\\u00a0maximum a posteriori probability (map)\\u00a0of around 0.87 on the validation set.\\u00a0 lung cancer lung ct pt radiomic feature other demographic diagnosis exposure pathology detail image analyses software\\\/source code community\"},{\"id\":42771,\"type\":\"collection\",\"slug\":\"lung-phantom\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/lung-phantom\\\/\",\"title\":\"Lung Phantom\",\"short_title\":\"Lung Phantom\",\"doi\":\"10.7937\\\/k9\\\/tcia.2015.08a1ixoo\",\"summary\":\"The FDA anthropomorphic thorax phantom with 12 phantom lesions of different sizes (10 and 20 mm in effective diameter), shapes (spherical, elliptical, lobulated, and spiculated), and densities (\\u2212630,\\u221210, and +100 HU) was scanned at Columbia University Medical Center on a 64-detector row scanner (LightSpeed VCT, GE Healthcare, Milwaukee, WI). The CT scanning parameters were 120 kVp, 100 mAs, 64x0.625 collimation, and pitch of 1.375. The images were reconstructed with the lung kernel using 1.25 mm slice thickness.This data set was provided to TCIA for use in the National Cancer Institute's Quantiatitive Imaging Network (QIN) Lung CT Segmentation Challenge. \\u00a0A\\u00a0TCIA Analysis Result dataset was created to enable easy re-use of the complete multi-site challenge data set.About the NCI QINThe mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the Quantitative Imaging Network Collections page. Interested investigators can apply to the QIN at: Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01).\",\"image\":false,\"program\":\"QIN\",\"cancer_types\":[\"Phantom\"],\"cancer_locations\":[\"Lung Phantom\"],\"data_types\":[\"CT\",\"Other\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":1,\"date_updated\":\"2020-09-25\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Lung Phantom\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_Lung_Phantom_06-22-2015.tcia\",\"blob\":\"lung phantom lung phantom the fda anthropomorphic thorax phantom with 12 phantom lesions of different sizes (10 and 20 mm in effective diameter), shapes (spherical, elliptical, lobulated, and spiculated), and densities (\\u2212630,\\u221210, and +100 hu) was scanned at columbia university medical center on a 64-detector row scanner (lightspeed vct, ge healthcare, milwaukee, wi). the ct scanning parameters were 120 kvp, 100 mas, 64x0.625 collimation, and pitch of 1.375. the images were reconstructed with the lung kernel using 1.25 mm slice thickness.this data set was provided to tcia for use in the national cancer institute's quantiatitive imaging network (qin) lung ct segmentation challenge. \\u00a0a\\u00a0tcia analysis result dataset was created to enable easy re-use of the complete multi-site challenge data set.about the nci qinthe mission of the qin is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. more information is available on the quantitative imaging network collections page. interested investigators can apply to the qin at: quantitative imaging for evaluation of responses to cancer therapies (u01). phantom lung phantom ct other image analyses qin\"},{\"id\":43389,\"type\":\"collection\",\"slug\":\"lungct-diagnosis\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/lungct-diagnosis\\\/\",\"title\":\"Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma\",\"short_title\":\"LungCT-Diagnosis\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.A6V7JIWX\",\"summary\":\"All the images are diagnostic contrast enhanced CT scans. The images were retrospectively acquired, to ensure sufficient patient follow-up. Slice thickness is variable : between 3 and 6 mm. All images were done at diagnosis and prior to surgery. The objective of the study was to extract prognostic image features that will describe lung adenocarcinomas and will associate with overall survival. \\u00a0\\nTwo CT features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity and intratumor density variation using routinely obtained diagnostic CT scans. The features systematically scored tumors and identified imaging phenotypes which exhibited survival differences. The features were extracted from routinely obtained CT images and were reproducible and stable despite the inherent clinical image acquisition variability. Our results suggest that quantitative imaging features can be used as an additional diagnostic tool in management of lung adenocarcinomas. More information is available in the related publication (see Citation tab below).\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"Other\",\"Radiomic Feature\",\"Demographic\",\"Diagnosis\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":61,\"date_updated\":\"2014-12-30\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=LungCT-Diagnosis\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_LungCT-Diagnosis_06-22-2015.tcia\",\"blob\":\"quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma lungct-diagnosis all the images are diagnostic contrast enhanced ct scans. the images were retrospectively acquired, to ensure sufficient patient follow-up. slice thickness is variable : between 3 and 6 mm. all images were done at diagnosis and prior to surgery. the objective of the study was to extract prognostic image features that will describe lung adenocarcinomas and will associate with overall survival. \\u00a0\\ntwo ct features were developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity and intratumor density variation using routinely obtained diagnostic ct scans. the features systematically scored tumors and identified imaging phenotypes which exhibited survival differences. the features were extracted from routinely obtained ct images and were reproducible and stable despite the inherent clinical image acquisition variability. our results suggest that quantitative imaging features can be used as an additional diagnostic tool in management of lung adenocarcinomas. more information is available in the related publication (see citation tab below). lung cancer lung ct other radiomic feature demographic diagnosis clinical image analyses community\"},{\"id\":48767,\"type\":\"collection\",\"slug\":\"mediastinal-lymph-node-seg\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/mediastinal-lymph-node-seg\\\/\",\"title\":\"Mediastinal Lymph Node Quantification (LNQ): Segmentation of Heterogeneous CT Data\",\"short_title\":\"Mediastinal-Lymph-Node-SEG\",\"doi\":\"10.7937\\\/qvaz-ja09\",\"summary\":\"The dataset concerns patients with lymphadenopathy (i.e., swelling of lymph nodes) due to illness or disease, such as cancer or infections. Lymph node disease involvement is due to the body\\u2019s immune response.The cohort is a cross-institutional dataset of chest CT scans acquired from 513 patients during treatment for various cancer types, curated for the LNQ2023 MICCAI Challenge\\u00a0to help develop new segmentation tools from weakly annotated cases. The dataset originates from the\\u00a0Tumor Imaging Metrics Core\\u00a0(TIMC), a multi-institutional imaging core lab (Massachusetts General Hospital, Dana Farber Cancer Institute, Brigham and Women\\u2019s Hospital, Boston, MA, USA) that provides multimodality imaging measurements to evaluate treatment response in patients enrolled in oncology clinical trials. TIMC has processed data from over fourteen hundred clinical trials conducted in the period 2007- 2020.The dataset comprises patients with various types of cancer. The top six primary patient cancers which accounted for 60% of all patients were: breast cancer, chronic lymphocytic leukemia (CLL), lung non-small cell cancer, Hodgkin\\u2018s lymphoma, small cell lung cancer, and renal cell cancer. The remaining patient cancer types accounted for 40% of all patients including thyroid cancer, adenocarcinoma, endometrial adenocarcinoma, melanoma, head and neck cancer, non-Hodgkin\\u2019s lymphoma, prostate cancer, mesothelioma, esophageal cancer, ovarian cancer, and colon cancer.All of the scans in the dataset were captured at baseline timepoints during clinical trials. The patients have likely been through standard of care before being placed on the trials, and may have even been through more than one trial. The patient's sex and primary cancer type are also provided. These patients were being treated in clinical trials for their conditions and have therefore undergone pathology review and other eligibility checks prior to enrollment. All annotators were trained radiologists or radiology domain experts with over ten years of experience. The initial localization for the lymph nodes in our partially annotated cases was performed as part of the clinical trial reads by the staff at the Tumor Imaging Metrics Core (TIMC), and each case was read by US-board certified radiologists in addition to TIMC Image Analysts. Annotators on our project extended the initial localizations into full segmentations in a subset of cases.Volumetric measurements were performed using 3D Slicer (Pieper et al., 2004;\\u00a0Fedorov et al., 2012). For each patient, the CT scan as well as the bidimensional measurements of the target lymph node were loaded into 3D Slicer (version 4.11), and then the Segment Editor module was used to perform manual delineation of the lymph node boundary. The Draw tool within the Editor module was used to draw free hand boundaries on axial cross-sections while viewing the sagittal and coronal planes for reference. Excluded from the lesion boundary were large vessels, artifacts, and non-nodal components.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Mediastinal-Image.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Various\",\"Breast Cancer\",\"Non-small Cell Lung Cancer\",\"Hodgkin Lymphoma\",\"Small Cell Lung Cancer\",\"Thyroid Cancer\",\"Adenocarcinoma\",\"Melanoma\",\"Head and Neck Cancer\",\"Prostate Cancer\",\"Mesothelioma\",\"Ovarian Cancer\",\"Colon Cancer\"],\"cancer_locations\":[\"Lymph Node\"],\"data_types\":[\"CT\",\"SEG\",\"Demographic\",\"Diagnosis\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":513,\"date_updated\":\"2024-08-28\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Mediastinal-Lymph-Node-SEG\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Mediastinal-Lymph-Node-SEG-DA-RAD.tcia\",\"blob\":\"mediastinal lymph node quantification (lnq): segmentation of heterogeneous ct data mediastinal-lymph-node-seg the dataset concerns patients with lymphadenopathy (i.e., swelling of lymph nodes) due to illness or disease, such as cancer or infections. lymph node disease involvement is due to the body\\u2019s immune response.the cohort is a cross-institutional dataset of chest ct scans acquired from 513 patients during treatment for various cancer types, curated for the lnq2023 miccai challenge\\u00a0to help develop new segmentation tools from weakly annotated cases. the dataset originates from the\\u00a0tumor imaging metrics core\\u00a0(timc), a multi-institutional imaging core lab (massachusetts general hospital, dana farber cancer institute, brigham and women\\u2019s hospital, boston, ma, usa) that provides multimodality imaging measurements to evaluate treatment response in patients enrolled in oncology clinical trials. timc has processed data from over fourteen hundred clinical trials conducted in the period 2007- 2020.the dataset comprises patients with various types of cancer. the top six primary patient cancers which accounted for 60% of all patients were: breast cancer, chronic lymphocytic leukemia (cll), lung non-small cell cancer, hodgkin\\u2018s lymphoma, small cell lung cancer, and renal cell cancer. the remaining patient cancer types accounted for 40% of all patients including thyroid cancer, adenocarcinoma, endometrial adenocarcinoma, melanoma, head and neck cancer, non-hodgkin\\u2019s lymphoma, prostate cancer, mesothelioma, esophageal cancer, ovarian cancer, and colon cancer.all of the scans in the dataset were captured at baseline timepoints during clinical trials. the patients have likely been through standard of care before being placed on the trials, and may have even been through more than one trial. the patient's sex and primary cancer type are also provided. these patients were being treated in clinical trials for their conditions and have therefore undergone pathology review and other eligibility checks prior to enrollment. all annotators were trained radiologists or radiology domain experts with over ten years of experience. the initial localization for the lymph nodes in our partially annotated cases was performed as part of the clinical trial reads by the staff at the tumor imaging metrics core (timc), and each case was read by us-board certified radiologists in addition to timc image analysts. annotators on our project extended the initial localizations into full segmentations in a subset of cases.volumetric measurements were performed using 3d slicer (pieper et al., 2004;\\u00a0fedorov et al., 2012). for each patient, the ct scan as well as the bidimensional measurements of the target lymph node were loaded into 3d slicer (version 4.11), and then the segment editor module was used to perform manual delineation of the lymph node boundary. the draw tool within the editor module was used to draw free hand boundaries on axial cross-sections while viewing the sagittal and coronal planes for reference. excluded from the lesion boundary were large vessels, artifacts, and non-nodal components. various breast cancer non-small cell lung cancer hodgkin lymphoma small cell lung cancer thyroid cancer adenocarcinoma melanoma head and neck cancer prostate cancer mesothelioma ovarian cancer colon cancer lymph node ct seg demographic diagnosis clinical community\"},{\"id\":43533,\"type\":\"collection\",\"slug\":\"meningioma-seg-class\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/meningioma-seg-class\\\/\",\"title\":\"Segmentation and Classification of Grade I and II Meningiomas from Magnetic Resonance Imaging: An Open Annotated Dataset\",\"short_title\":\"Meningioma-SEG-CLASS\",\"doi\":\"10.7937\\\/0TKV-1A36\",\"summary\":\"The study included 96 consecutive treatment na\\u00efve patients with intracranial meningiomas treated with surgical resection from 2010 to 2019. All patients had pre-operative T1, T1-CE, and T2-FLAIR MR images with subsequent subtotal or gross total resection of pathologically confirmed grade I or grade II meningiomas. A neuropathology team reviewed histopathology, including two subspecialty trained neuropathologists and one neuropathology fellow. The meningioma grade was confirmed based on current classification guidelines, most recently described in the 2016 WHO Bluebook. Clinical information includes grade, subtype, type of surgery, tumor location, and atypical features. Meningioma labels on T1-CE and T2-FLAIR images will also be provided in DICOM format. The hyperintense\\u00a0T1-contrast enhancing\\u00a0tumor\\u00a0and\\u00a0hyperintense\\u00a0T2-FLAIR and\\u00a0tumor\\u00a0were manually contoured on each MRI and reviewed by a central nervous system radiation oncologist specialist.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA-Meningioma_NEW.png\",\"program\":\"Community\",\"cancer_types\":[\"Meningioma\"],\"cancer_locations\":[\"Brain\",\"Spinal Cord\"],\"data_types\":[\"MR\",\"RTSTRUCT\",\"Demographic\",\"Diagnosis\",\"Measurement\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":96,\"date_updated\":\"2023-02-13\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Meningioma-SEG-CLASS\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Meningioma-SEG-Class-February-2022-manifest.tcia\",\"blob\":\"segmentation and classification of grade i and ii meningiomas from magnetic resonance imaging: an open annotated dataset meningioma-seg-class the study included 96 consecutive treatment na\\u00efve patients with intracranial meningiomas treated with surgical resection from 2010 to 2019. all patients had pre-operative t1, t1-ce, and t2-flair mr images with subsequent subtotal or gross total resection of pathologically confirmed grade i or grade ii meningiomas. a neuropathology team reviewed histopathology, including two subspecialty trained neuropathologists and one neuropathology fellow. the meningioma grade was confirmed based on current classification guidelines, most recently described in the 2016 who bluebook. clinical information includes grade, subtype, type of surgery, tumor location, and atypical features. meningioma labels on t1-ce and t2-flair images will also be provided in dicom format. the hyperintense\\u00a0t1-contrast enhancing\\u00a0tumor\\u00a0and\\u00a0hyperintense\\u00a0t2-flair and\\u00a0tumor\\u00a0were manually contoured on each mri and reviewed by a central nervous system radiation oncologist specialist. meningioma brain spinal cord mr rtstruct demographic diagnosis measurement clinical community\"},{\"id\":42789,\"type\":\"collection\",\"slug\":\"midrc-ricord-1a\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/midrc-ricord-1a\\\/\",\"title\":\"Medical Imaging Data Resource Center (MIDRC) - RSNA International COVID-19 Open Radiology Database (RICORD) Release 1a - Chest CT Covid+\",\"short_title\":\"MIDRC-RICORD-1A\",\"doi\":\"10.7937\\\/VTW4-X588\",\"summary\":\"BackgroundThe COVID-19 pandemic is a global healthcare emergency. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making in imaging. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.PurposeThe Radiological Society of North America (RSNA) assembled the RSNA International COVID-19 Open Radiology Database (RICORD), a collection of COVID-related imaging datasets and expert annotations to support research and education. The RICORD datasets are made freely available to the research community and will be incorporated in the Medical Imaging and Data Resource Center (MIDRC), a multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.Materials and MethodsMIDRC-RICORD dataset 1a was created through a collaboration between the RSNA and the Society of Thoracic Radiology (STR). Pixel-level volumetric segmentation with clinical annotations by thoracic radiology subspecialists was performed for all COVID positive thoracic computed tomography (CT) imaging studies in a labeling schema coordinated with other international consensus panels and COVID data annotation efforts.ResultsMIDRC-RICORD dataset 1a consists of 120 thoracic computed tomography (CT) scans from four international sites annotated with detailed segmentation and diagnostic labels.Patient Selection: Patients at least 18 years in age receiving positive diagnosis for COVID-19.Data Abstract1. 120 Chest CT examinations (axial series only, any protocol).2. Annotations comprised of\\u00a0 \\u00a0 a) Detailed segmentation of affected regions;\\u00a0 \\u00a0 b) Image-level labels (Infectious opacity, Infectious TIB\\\/micronodules, Infectious cavity, Noninfectious nodule\\\/mass, Atelectasis, Other noninfectious opacity)\\u00a0 \\u00a0 c) Exam-level diagnostic labels (Typical, Indeterminate, Atypical, Negative for pneumonia, Halo sign, Reversed halo sign, Reticular pattern w\\\/o parenchymal opacity, Perilesional vessel enlargement, Bronchial wall thickening, Bronchiectasis, Subpleural curvilinear line, Effusion, Pleural thickening, Pneumothorax, Pericardial effusion, Lymphadenopathy, Pulmonary embolism, Normal lung, Infectious lung disease, Emphysema, Oncologic lung disease, Non-infectious inflammatory lung disease, Non-infectious interstitial, Fibrotic lung disease, Other lung disease)\\u00a0 \\u00a0 d) Exam-level procedure labels (With IV contrast, Without IV contrast, QA- inadequate motion\\\/breathing, QA- inadequate insufficient inspiration, QA- inadequate low resolution, QA- inadequate incomplete lungs, QA- inadequate wrong body part\\\/modality, Endotracheal tube, Central venous\\\/arterial line, Nasogastric tube, Sternotomy wires, Pacemaker, Other support apparatus).3. Supporting clinical variables: MRN*, Age, Study Date*, Exam Description, Sex, Study UID*, Image Count, Modality, Testing Result, Specimen Source (* pseudonymous values).\\u00a0How to use the JSON annotationsMore information about how the JSON annotations are organized can be found on https:\\\/\\\/docs.md.ai\\\/data\\\/json\\\/.\\u00a0 Steps 2 & 3 in this example code\\u00a0demonstrate how to to load the JSON into a Dataframe. The JSON file can be downloaded via the data access table below; it is not available via MD.ai. This Jupyter Notebook may also be helpful.Code for converting CT scan segmentation labels for lung opacities from MD.ai JSON to DICOM-SEG : https:\\\/\\\/github.com\\\/QIICR\\\/dcmqi\\\/blob\\\/add-mdai-converter\\\/util\\\/mdai2dcm.pyResearch BenefitsAs this is a public dataset, RICORD is available for non-commercial use (and further enrichment) by the research and education communities which may include development of educational resources for COVID-19, use of RICORD to create AI systems for diagnosis and quantification, benchmarking performance for existing solutions, exploration of distributed\\\/federated learning, further annotation or data augmentation efforts, and evaluation of the examinations for disease entities beyond COVID-19 pneumonia. Deliberate consideration of the detailed annotation schema, demographics, and other included meta-data will be critical when generating cohorts with RICORD, particularly as more public COVID-19 imaging datasets are made available via complementary and parallel efforts. It is important to emphasize that there are limitations to the clinical \\u201cground truth\\u201d as the SARS-CoV-2 RT-PCR tests have widely documented limitations and are subject to both false-negative and false-positive results which impact the distribution of the included imaging data, and may have led to an unknown epidemiologic distortion of patients based on the inclusion criteria. These limitations notwithstanding, RICORD has achieved the stated objectives for data complexity, heterogeneity, and high-quality expert annotations as a comprehensive COVID-19 thoracic imaging data resource.\",\"image\":false,\"program\":\"COVID-19\",\"cancer_types\":[\"COVID-19 (non-cancer)\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"Measurement\",\"Diagnosis\",\"Demographic\",\"Molecular Test\"],\"supporting_data\":[\"Clinical\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":110,\"date_updated\":\"2020-01-14\",\"status\":\"Complete\",\"license\":\"CC BY-NC 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=MIDRC-RICORD-1A\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/MIDRC-RICORD-1a-COVID-Positive-Dec-17-2020-NBIA-manifest.tcia\",\"blob\":\"medical imaging data resource center (midrc) - rsna international covid-19 open radiology database (ricord) release 1a - chest ct covid+ midrc-ricord-1a backgroundthe covid-19 pandemic is a global healthcare emergency. prediction models for covid-19 imaging are rapidly being developed to support medical decision making in imaging. however, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.purposethe radiological society of north america (rsna) assembled the rsna international covid-19 open radiology database (ricord), a collection of covid-related imaging datasets and expert annotations to support research and education. the ricord datasets are made freely available to the research community and will be incorporated in the medical imaging and data resource center (midrc), a multi-institutional research data repository funded by the national institute of biomedical imaging and bioengineering of the national institutes of health.materials and methodsmidrc-ricord dataset 1a was created through a collaboration between the rsna and the society of thoracic radiology (str). pixel-level volumetric segmentation with clinical annotations by thoracic radiology subspecialists was performed for all covid positive thoracic computed tomography (ct) imaging studies in a labeling schema coordinated with other international consensus panels and covid data annotation efforts.resultsmidrc-ricord dataset 1a consists of 120 thoracic computed tomography (ct) scans from four international sites annotated with detailed segmentation and diagnostic labels.patient selection: patients at least 18 years in age receiving positive diagnosis for covid-19.data abstract1. 120 chest ct examinations (axial series only, any protocol).2. annotations comprised of\\u00a0 \\u00a0 a) detailed segmentation of affected regions;\\u00a0 \\u00a0 b) image-level labels (infectious opacity, infectious tib\\\/micronodules, infectious cavity, noninfectious nodule\\\/mass, atelectasis, other noninfectious opacity)\\u00a0 \\u00a0 c) exam-level diagnostic labels (typical, indeterminate, atypical, negative for pneumonia, halo sign, reversed halo sign, reticular pattern w\\\/o parenchymal opacity, perilesional vessel enlargement, bronchial wall thickening, bronchiectasis, subpleural curvilinear line, effusion, pleural thickening, pneumothorax, pericardial effusion, lymphadenopathy, pulmonary embolism, normal lung, infectious lung disease, emphysema, oncologic lung disease, non-infectious inflammatory lung disease, non-infectious interstitial, fibrotic lung disease, other lung disease)\\u00a0 \\u00a0 d) exam-level procedure labels (with iv contrast, without iv contrast, qa- inadequate motion\\\/breathing, qa- inadequate insufficient inspiration, qa- inadequate low resolution, qa- inadequate incomplete lungs, qa- inadequate wrong body part\\\/modality, endotracheal tube, central venous\\\/arterial line, nasogastric tube, sternotomy wires, pacemaker, other support apparatus).3. supporting clinical variables: mrn*, age, study date*, exam description, sex, study uid*, image count, modality, testing result, specimen source (* pseudonymous values).\\u00a0how to use the json annotationsmore information about how the json annotations are organized can be found on https:\\\/\\\/docs.md.ai\\\/data\\\/json\\\/.\\u00a0 steps 2 & 3 in this example code\\u00a0demonstrate how to to load the json into a dataframe. the json file can be downloaded via the data access table below; it is not available via md.ai. this jupyter notebook may also be helpful.code for converting ct scan segmentation labels for lung opacities from md.ai json to dicom-seg : https:\\\/\\\/github.com\\\/qiicr\\\/dcmqi\\\/blob\\\/add-mdai-converter\\\/util\\\/mdai2dcm.pyresearch benefitsas this is a public dataset, ricord is available for non-commercial use (and further enrichment) by the research and education communities which may include development of educational resources for covid-19, use of ricord to create ai systems for diagnosis and quantification, benchmarking performance for existing solutions, exploration of distributed\\\/federated learning, further annotation or data augmentation efforts, and evaluation of the examinations for disease entities beyond covid-19 pneumonia. deliberate consideration of the detailed annotation schema, demographics, and other included meta-data will be critical when generating cohorts with ricord, particularly as more public covid-19 imaging datasets are made available via complementary and parallel efforts. it is important to emphasize that there are limitations to the clinical \\u201cground truth\\u201d as the sars-cov-2 rt-pcr tests have widely documented limitations and are subject to both false-negative and false-positive results which impact the distribution of the included imaging data, and may have led to an unknown epidemiologic distortion of patients based on the inclusion criteria. these limitations notwithstanding, ricord has achieved the stated objectives for data complexity, heterogeneity, and high-quality expert annotations as a comprehensive covid-19 thoracic imaging data resource. covid-19 (non-cancer) lung ct measurement diagnosis demographic molecular test clinical software\\\/source code covid-19\"},{\"id\":42799,\"type\":\"collection\",\"slug\":\"midrc-ricord-1b\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/midrc-ricord-1b\\\/\",\"title\":\"Medical Imaging Data Resource Center (MIDRC) - RSNA International COVID-19 Open Radiology Database (RICORD) Release 1b - Chest CT Covid-\",\"short_title\":\"MIDRC-RICORD-1B\",\"doi\":\"10.7937\\\/31V8-4A40\",\"summary\":\"BackgroundThe COVID-19 pandemic is a global healthcare emergency. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making in imaging. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.PurposeTo create the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging.\\u00a0The Radiological Society of North America (RSNA) assembled the\\u00a0RSNA International COVID-19 Open Radiology Database (RICORD)\\u00a0collection\\u00a0of COVID-related imaging datasets and expert annotations to support research and education.\\u00a0RICORD\\u00a0data will be incorporated in the\\u00a0Medical Imaging\\u00a0and\\u00a0Data Resource Center (MIDRC),\\u00a0a\\u00a0multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.Materials and MethodsThis dataset was a collaboration between the RSNA and Society of Thoracic Radiology (STR).ResultsThe RSNA International COVID-19 Open Annotated Radiology Database (RICORD) release 1b consists of 120 thoracic computed tomography (CT) scans of COVID negative patients from four international sites.Patient Selection: Patients at least 18 years in age receiving negative diagnosis for COVID-19.Data Abstract120 de-identified Thoracic CT scans from COVID negative patients.Supporting clinical variables: MRN*, Age, Exam Date\\\/Time*, Exam Description, Sex, Study UID*, Image Count, Modality, Symptomatic, Testing Result, Specimen Source (* pseudonymous values).Research BenefitsAs this is a public dataset, RICORD is available for non-commercial use (and further enrichment) by the research and education communities which may include development of educational resources for COVID-19, use of RICORD to create AI systems for diagnosis and quantification, benchmarking performance for existing solutions, exploration of distributed\\\/federated learning, further annotation or data augmentation efforts, and evaluation of the examinations for disease entities beyond COVID-19 pneumonia. Deliberate consideration of the detailed annotation schema, demographics, and other included meta-data will be critical when generating cohorts with RICORD, particularly as more public COVID-19 imaging datasets are made available via complementary and parallel efforts. It is important to emphasize that there are limitations to the clinical \\u201cground truth\\u201d as the SARS-CoV-2 RT-PCR tests have widely documented limitations and are subject to both false-negative and false-positive results which impact the distribution of the included imaging data, and may have led to an unknown epidemiologic distortion of patients based on the inclusion criteria. These limitations notwithstanding, RICORD has achieved the stated objectives for data complexity, heterogeneity, and high-quality expert annotations as a comprehensive COVID-19 thoracic imaging data resource.\",\"image\":false,\"program\":\"COVID-19\",\"cancer_types\":[\"COVID-19 (non-cancer)\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"Classification\",\"Demographic\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":117,\"date_updated\":\"2021-02-05\",\"status\":\"Complete\",\"license\":\"CC BY-NC 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=MIDRC-RICORD-1B\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/MIDRC-RICORD-1b-manifest-Feb-2021.tcia\",\"blob\":\"medical imaging data resource center (midrc) - rsna international covid-19 open radiology database (ricord) release 1b - chest ct covid- midrc-ricord-1b backgroundthe covid-19 pandemic is a global healthcare emergency. prediction models for covid-19 imaging are rapidly being developed to support medical decision making in imaging. however, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.purposeto create the first multi-institutional, multi-national expert annotated covid-19 imaging dataset made freely available to the machine learning community as a research and educational resource for covid-19 chest imaging.\\u00a0the radiological society of north america (rsna) assembled the\\u00a0rsna international covid-19 open radiology database (ricord)\\u00a0collection\\u00a0of covid-related imaging datasets and expert annotations to support research and education.\\u00a0ricord\\u00a0data will be incorporated in the\\u00a0medical imaging\\u00a0and\\u00a0data resource center (midrc),\\u00a0a\\u00a0multi-institutional research data repository funded by the national institute of biomedical imaging and bioengineering of the national institutes of health.materials and methodsthis dataset was a collaboration between the rsna and society of thoracic radiology (str).resultsthe rsna international covid-19 open annotated radiology database (ricord) release 1b consists of 120 thoracic computed tomography (ct) scans of covid negative patients from four international sites.patient selection: patients at least 18 years in age receiving negative diagnosis for covid-19.data abstract120 de-identified thoracic ct scans from covid negative patients.supporting clinical variables: mrn*, age, exam date\\\/time*, exam description, sex, study uid*, image count, modality, symptomatic, testing result, specimen source (* pseudonymous values).research benefitsas this is a public dataset, ricord is available for non-commercial use (and further enrichment) by the research and education communities which may include development of educational resources for covid-19, use of ricord to create ai systems for diagnosis and quantification, benchmarking performance for existing solutions, exploration of distributed\\\/federated learning, further annotation or data augmentation efforts, and evaluation of the examinations for disease entities beyond covid-19 pneumonia. deliberate consideration of the detailed annotation schema, demographics, and other included meta-data will be critical when generating cohorts with ricord, particularly as more public covid-19 imaging datasets are made available via complementary and parallel efforts. it is important to emphasize that there are limitations to the clinical \\u201cground truth\\u201d as the sars-cov-2 rt-pcr tests have widely documented limitations and are subject to both false-negative and false-positive results which impact the distribution of the included imaging data, and may have led to an unknown epidemiologic distortion of patients based on the inclusion criteria. these limitations notwithstanding, ricord has achieved the stated objectives for data complexity, heterogeneity, and high-quality expert annotations as a comprehensive covid-19 thoracic imaging data resource. covid-19 (non-cancer) lung ct classification demographic clinical covid-19\"},{\"id\":42811,\"type\":\"collection\",\"slug\":\"midrc-ricord-1c\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/midrc-ricord-1c\\\/\",\"title\":\"Medical Imaging Data Resource Center (MIDRC) - RSNA International COVID-19 Open Radiology Database (RICORD) Release 1c - Chest x-ray Covid+\",\"short_title\":\"MIDRC-RICORD-1C\",\"doi\":\"10.7937\\\/91ah-v663\",\"summary\":\"BackgroundThe COVID-19 pandemic is a global healthcare emergency. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making in imaging. However, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.PurposeTo create the first multi-institutional, multi-national expert annotated COVID-19 imaging dataset made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging.\\u00a0The Radiological Society of North America (RSNA) assembled the\\u00a0RSNA International COVID-19 Open Radiology Database (RICORD)\\u00a0collection\\u00a0of COVID-related imaging datasets and expert annotations to support research and education.\\u00a0RICORD\\u00a0data will be incorporated in the\\u00a0Medical Imaging\\u00a0and\\u00a0Data Resource Center (MIDRC),\\u00a0a\\u00a0multi-institutional research data repository funded by the National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.Materials and MethodsThis dataset was created through a collaboration between the RSNA and Society of Thoracic Radiology (STR). Clinical\\u00a0annotation\\u00a0by thoracic radiology subspecialists was performed for all COVID positive chest radiography (CXR) imaging studies using a labeling schema based upon guidelines for reporting classification of COVID-19 findings in CXRs (see Review of Chest Radiograph Findings of COVID-19 Pneumonia and Suggested Reporting Language, Journal of Thoracic Imaging).ResultsThe RSNA International COVID-19 Open Annotated Radiology Database (RICORD) consists of 998 chest x-rays from 361 patients at four international sites annotated with diagnostic labels.Patient Selection: Patients at least 18 years in age receiving positive diagnosis for COVID-19.Data Abstract998 Chest x-ray examinations from 361 patients.Annotations with labels:ClassificationTypical AppearanceMultifocal bilateral, peripheral opacities, and\\\/or Opacities with rounded morphologyLower lung-predominant distribution (Required Feature - must be present with either or both of the first two opacity patterns)Indeterminate AppearanceAbsence of typical findings AND Unilateral, central or upper lung predominant distribution of airspace diseaseAtypical AppearancePneumothorax or pleural effusion, Pulmonary Edema, Lobar Consolidation, Solitary lung nodule or mass, Diffuse tiny nodules, CavityNegative for PneumoniaNo lung opacitiesAirspace Disease GradingLungs are divided on frontal chest xray into 3 zones per lung (6 zones total). The upper zone extends from the apices to the superior hilum. The mid zone spans between the superior and inferior hilar margins. The lower zone extends from the inferior hilar margins to the costophrenic sulci.Mild - Required if not negative for pneumoniaOpacities in 1-2 lung zonesModerate - Required if not negative for pneumoniaOpacities in 3-4 lung zonesSevere - Required if not negative for pneumoniaOpacities in >4 lung zones\\u00a0Supporting clinical variables: MRN*, Age, Study Date*, Exam Description, Sex, Study UID*, Image Count, Modality, Testing Result, Specimen Source (* pseudonymous values).How to use the JSON annotationsMore information about how the JSON annotations are organized can be found on\\u00a0https:\\\/\\\/docs.md.ai\\\/data\\\/json\\\/.\\u00a0 Steps 2 & 3 in this\\u00a0example code\\u00a0demonstrate how to to load the JSON into a Dataframe.\\u00a0The JSON file can be downloaded via the data access table below; it is not available via MD.ai.\\u00a0This\\u00a0Jupyter Notebook\\u00a0may also be helpful.Research BenefitsRICORD is available for non-commercial use (and further enrichment) by the research and education communities which may include development of educational resources for COVID-19, use of RICORD to create AI systems for diagnosis and quantification, benchmarking performance for existing solutions, exploration of distributed\\\/federated learning, further annotation or data augmentation efforts, and evaluation of the examinations for disease entities beyond COVID-19 pneumonia. Deliberate consideration of the detailed annotation schema, demographics, and other included meta-data will be critical when generating cohorts with RICORD, particularly as more public COVID-19 imaging datasets are made available via complementary and parallel efforts. It is important to emphasize that there are limitations to the clinical \\u201cground truth\\u201d as the SARS-CoV-2 RT-PCR tests have widely documented limitations and are subject to both false-negative and false-positive results which impact the distribution of the included imaging data, and may have led to an unknown epidemiologic distortion of patients based on the inclusion criteria. These limitations notwithstanding, RICORD has achieved the stated objectives for data complexity, heterogeneity, and high-quality expert annotations as a comprehensive COVID-19 thoracic imaging data resource.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Covid-x-ray.jpg\",\"program\":\"COVID-19\",\"cancer_types\":[\"COVID-19 (non-cancer)\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"DX\",\"CR\",\"Diagnosis\",\"Classification\",\"Demographic\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":361,\"date_updated\":\"2021-01-15\",\"status\":\"Complete\",\"license\":\"CC BY-NC 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=MIDRC-RICORD-1C\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/MIDRC-RICORD-1c-manifest-Jan-2021.tcia\",\"blob\":\"medical imaging data resource center (midrc) - rsna international covid-19 open radiology database (ricord) release 1c - chest x-ray covid+ midrc-ricord-1c backgroundthe covid-19 pandemic is a global healthcare emergency. prediction models for covid-19 imaging are rapidly being developed to support medical decision making in imaging. however, inadequate availability of a diverse annotated dataset has limited the performance and generalizability of existing models.purposeto create the first multi-institutional, multi-national expert annotated covid-19 imaging dataset made freely available to the machine learning community as a research and educational resource for covid-19 chest imaging.\\u00a0the radiological society of north america (rsna) assembled the\\u00a0rsna international covid-19 open radiology database (ricord)\\u00a0collection\\u00a0of covid-related imaging datasets and expert annotations to support research and education.\\u00a0ricord\\u00a0data will be incorporated in the\\u00a0medical imaging\\u00a0and\\u00a0data resource center (midrc),\\u00a0a\\u00a0multi-institutional research data repository funded by the national institute of biomedical imaging and bioengineering of the national institutes of health.materials and methodsthis dataset was created through a collaboration between the rsna and society of thoracic radiology (str). clinical\\u00a0annotation\\u00a0by thoracic radiology subspecialists was performed for all covid positive chest radiography (cxr) imaging studies using a labeling schema based upon guidelines for reporting classification of covid-19 findings in cxrs (see review of chest radiograph findings of covid-19 pneumonia and suggested reporting language, journal of thoracic imaging).resultsthe rsna international covid-19 open annotated radiology database (ricord) consists of 998 chest x-rays from 361 patients at four international sites annotated with diagnostic labels.patient selection: patients at least 18 years in age receiving positive diagnosis for covid-19.data abstract998 chest x-ray examinations from 361 patients.annotations with labels:classificationtypical appearancemultifocal bilateral, peripheral opacities, and\\\/or opacities with rounded morphologylower lung-predominant distribution (required feature - must be present with either or both of the first two opacity patterns)indeterminate appearanceabsence of typical findings and unilateral, central or upper lung predominant distribution of airspace diseaseatypical appearancepneumothorax or pleural effusion, pulmonary edema, lobar consolidation, solitary lung nodule or mass, diffuse tiny nodules, cavitynegative for pneumoniano lung opacitiesairspace disease gradinglungs are divided on frontal chest xray into 3 zones per lung (6 zones total). the upper zone extends from the apices to the superior hilum. the mid zone spans between the superior and inferior hilar margins. the lower zone extends from the inferior hilar margins to the costophrenic sulci.mild - required if not negative for pneumoniaopacities in 1-2 lung zonesmoderate - required if not negative for pneumoniaopacities in 3-4 lung zonessevere - required if not negative for pneumoniaopacities in >4 lung zones\\u00a0supporting clinical variables: mrn*, age, study date*, exam description, sex, study uid*, image count, modality, testing result, specimen source (* pseudonymous values).how to use the json annotationsmore information about how the json annotations are organized can be found on\\u00a0https:\\\/\\\/docs.md.ai\\\/data\\\/json\\\/.\\u00a0 steps 2 & 3 in this\\u00a0example code\\u00a0demonstrate how to to load the json into a dataframe.\\u00a0the json file can be downloaded via the data access table below; it is not available via md.ai.\\u00a0this\\u00a0jupyter notebook\\u00a0may also be helpful.research benefitsricord is available for non-commercial use (and further enrichment) by the research and education communities which may include development of educational resources for covid-19, use of ricord to create ai systems for diagnosis and quantification, benchmarking performance for existing solutions, exploration of distributed\\\/federated learning, further annotation or data augmentation efforts, and evaluation of the examinations for disease entities beyond covid-19 pneumonia. deliberate consideration of the detailed annotation schema, demographics, and other included meta-data will be critical when generating cohorts with ricord, particularly as more public covid-19 imaging datasets are made available via complementary and parallel efforts. it is important to emphasize that there are limitations to the clinical \\u201cground truth\\u201d as the sars-cov-2 rt-pcr tests have widely documented limitations and are subject to both false-negative and false-positive results which impact the distribution of the included imaging data, and may have led to an unknown epidemiologic distortion of patients based on the inclusion criteria. these limitations notwithstanding, ricord has achieved the stated objectives for data complexity, heterogeneity, and high-quality expert annotations as a comprehensive covid-19 thoracic imaging data resource. covid-19 (non-cancer) lung dx cr diagnosis classification demographic clinical covid-19\"},{\"id\":42823,\"type\":\"collection\",\"slug\":\"mimm_sbilab\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/mimm_sbilab\\\/\",\"title\":\"MiMM_SBILab Dataset: Microscopic Images of Multiple Myeloma\",\"short_title\":\"MiMM_SBILab\",\"doi\":\"10.7937\\\/tcia.2019.pnn6aypl\",\"summary\":\"Microscopic images were captured from bone marrow aspirate slides of patients diagnosed with multiple myeloma as per the standard guidelines. Slides were stained using Jenner- Giemsa stain. Images were captured at 1000x magnification using Nikon Eclipse-200 microscope equipped with a digital camera. Images were captured in raw BMP format with a size of 2560x1920 pixels. In all, this dataset consists of 85 images. All these 85 images were stain normalized using our in-house methodology before being used for segmentation. These stain normalized images have been provided as the annotated dataset with plasma cells marked in all image slides contained in a presentation for the ready reference of readers.Additional NotesThis collection has also been uploaded to the Harvard Blood Cancer Dataverse website. Please refer to DOI 10.7910\\\/DVN\\\/XCX7ST for more information.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image2019-6-10_15-42-35.png\",\"program\":\"Community\",\"cancer_types\":[\"Multiple Myeloma\"],\"cancer_locations\":[\"Bone\"],\"data_types\":[\"Histopathology\",\"Other\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":5,\"date_updated\":\"2019-03-25\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22MiMM_SBILab%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc0MiIsInBhc3Njb2RlIjoiZDJkOTY4MTFhM2M3N2IxNmZkYWRjOGMzNDgyNTgzNzEyNzg1ZTJiZiIsInBhY2thZ2VfaWQiOiI3NDIiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"mimm_sbilab dataset: microscopic images of multiple myeloma mimm_sbilab microscopic images were captured from bone marrow aspirate slides of patients diagnosed with multiple myeloma as per the standard guidelines. slides were stained using jenner- giemsa stain. images were captured at 1000x magnification using nikon eclipse-200 microscope equipped with a digital camera. images were captured in raw bmp format with a size of 2560x1920 pixels. in all, this dataset consists of 85 images. all these 85 images were stain normalized using our in-house methodology before being used for segmentation. these stain normalized images have been provided as the annotated dataset with plasma cells marked in all image slides contained in a presentation for the ready reference of readers.additional notesthis collection has also been uploaded to the harvard blood cancer dataverse website. please refer to doi 10.7910\\\/dvn\\\/xcx7st for more information. multiple myeloma bone histopathology other image analyses community\"},{\"id\":42829,\"type\":\"collection\",\"slug\":\"mouse-astrocytoma\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/mouse-astrocytoma\\\/\",\"title\":\"Mouse-Astrocytoma\",\"short_title\":\"Mouse-Astrocytoma\",\"doi\":\"10.7937\\\/K9TCIA.2017.SGW7CAQW\",\"summary\":\"This collection consists of magnetic resonance images (MRI) of genetically engineered mouse models (GEMMs) of high grade astrocytoma, including glioblastoma multiforme (GBM).\\nIn these GEMMs, the most commonly disregulated networks in GBM -- RB, KRAS and\\\/or PI3K signaling -- are perturbed at the genetic level.\\u00a0 These genetic aberrations induce development of high grade astrocytoma in the mouse with properties similar to that of human disease.\\u00a0 MRI was used to perform a qualitative and quantitative phenotypic characterization of the different genotypes and molecular subtypes.\\u00a0 Additionally, mouse MRI images were compared human GBM imaging parameters using the VASARI lexicon.\\u00a0 The MRI data contained herein includes anatomic T2 weighted images and dynamic contrast enhanced MRI.\\nFor scientific or other inquiries about this dataset, please contact TCIA's Helpdesk.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Glioblastoma Multiforme\"],\"cancer_locations\":[\"Head\"],\"data_types\":[\"MR\"],\"supporting_data\":[],\"species\":[\"Mouse\"],\"subjects\":48,\"date_updated\":\"2017-03-21\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=Mouse-Astrocytoma\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Mouse-Astrocytoma-doiJNLP.tcia\",\"blob\":\"mouse-astrocytoma mouse-astrocytoma this collection consists of magnetic resonance images (mri) of genetically engineered mouse models (gemms) of high grade astrocytoma, including glioblastoma multiforme (gbm).\\nin these gemms, the most commonly disregulated networks in gbm -- rb, kras and\\\/or pi3k signaling -- are perturbed at the genetic level.\\u00a0 these genetic aberrations induce development of high grade astrocytoma in the mouse with properties similar to that of human disease.\\u00a0 mri was used to perform a qualitative and quantitative phenotypic characterization of the different genotypes and molecular subtypes.\\u00a0 additionally, mouse mri images were compared human gbm imaging parameters using the vasari lexicon.\\u00a0 the mri data contained herein includes anatomic t2 weighted images and dynamic contrast enhanced mri.\\nfor scientific or other inquiries about this dataset, please contact tcia's helpdesk. glioblastoma multiforme head mr community\"},{\"id\":42835,\"type\":\"collection\",\"slug\":\"mouse-mammary\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/mouse-mammary\\\/\",\"title\":\"Mouse-Mammary\",\"short_title\":\"Mouse-Mammary\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.9P42KSE6\",\"summary\":\"This collection consists of magnetic resonance images (MRI) of genetically engineered mouse models (GEMMs) of breast cancer.\\u00a0 These images were acquired as part of a Department of Defense (DOD) Breast Cancer Research Program (BCRP) Postdoctoral Award W81XWH-12-1-0307 entitled \\u201cInvestigating Ductal Carcinoma in Situ Using Noninvasive Imaging of Genetically Engineered Mouse Models\\nA particular emphasis of this project was to study the earliest stages of breast cancer\\u2014preinvasive ductal carcinoma in situ (DCIS)\\u2014and to interrogate the underlying genetic events that influence progression into invasive disease. In particular, we focused on the role of perturbed Rb, p53 and BRCA1 functionality and how these pathways, acting alone and in combination, can influence the development and progression of DCIS. GEMMs serve as an excellent model system wherein genetic changes can be controlled and manipulated over time.\\u00a0 In vivo MRI is a superb technique for noninvasively tracking and characterizing these microscopic early stage cancers as they develop, change and transition into lethal invasive disease.\\nFor scientific or other inquiries about this dataset, please contact TCIA's Helpdesk.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Abdomen\"],\"data_types\":[\"MR\"],\"supporting_data\":[],\"species\":[\"Mouse\"],\"subjects\":32,\"date_updated\":\"2015-03-18\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=Mouse-mammary\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_Mouse-Mammary_06-22-2015.tcia\",\"blob\":\"mouse-mammary mouse-mammary this collection consists of magnetic resonance images (mri) of genetically engineered mouse models (gemms) of breast cancer.\\u00a0 these images were acquired as part of a department of defense (dod) breast cancer research program (bcrp) postdoctoral award w81xwh-12-1-0307 entitled \\u201cinvestigating ductal carcinoma in situ using noninvasive imaging of genetically engineered mouse models\\na particular emphasis of this project was to study the earliest stages of breast cancer\\u2014preinvasive ductal carcinoma in situ (dcis)\\u2014and to interrogate the underlying genetic events that influence progression into invasive disease. in particular, we focused on the role of perturbed rb, p53 and brca1 functionality and how these pathways, acting alone and in combination, can influence the development and progression of dcis. gemms serve as an excellent model system wherein genetic changes can be controlled and manipulated over time.\\u00a0 in vivo mri is a superb technique for noninvasively tracking and characterizing these microscopic early stage cancers as they develop, change and transition into lethal invasive disease.\\nfor scientific or other inquiries about this dataset, please contact tcia's helpdesk. breast cancer abdomen mr community\"},{\"id\":43709,\"type\":\"collection\",\"slug\":\"mri-dir\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/mri-dir\\\/\",\"title\":\"Synthetic and Phantom MR Images for Determining Deformable Image Registration Accuracy\",\"short_title\":\"MRI-DIR\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2018.3f08iejt\",\"summary\":\"Two sets of images were created to evaluate deformable image registration accuracy. The first set contains CT, T1-, and T2-weighted images from a porcine phantom. The phantom was implanted with ten 0.35 mm gold markers and then immobilized in a plastic container with movable dividers. The porcine phantom was compressed in 4 different ways and images were acquired in each position. The markers were visible on the CT scans but not the MR scans due to the selected voxel size. Therefore, the markers do not interfere with the registration between MR images and the marker locations can be obtained from the CT images to determine accuracy. The second set of images are synthetic images derived from 28 head and neck squamous cell carcinoma patients who had pre-, mid-, and post-radiotherapy treatment MR scans. From these patients, inter- and intra-patient models were created. Four synthetic pre-treatment images were created by using the inter-patient model on a selected template patient. Four synthetic post-treatment images were created for each synthetic pre-treatment image using the intra-patient model.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Head and Neck Squamous Cell Carcinoma\"],\"cancer_locations\":[\"Head-Neck\",\"Phantom\"],\"data_types\":[\"MR\",\"RTSTRUCT\",\"CT\",\"Measurement\"],\"supporting_data\":[\"Image Registrations\"],\"species\":[\"Phantom\"],\"subjects\":9,\"date_updated\":\"2018-06-30\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=MRI-DIR\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/MRI-DIR-06-30-2018-doiJNLP-1UmgA3nc.tcia\",\"blob\":\"synthetic and phantom mr images for determining deformable image registration accuracy mri-dir two sets of images were created to evaluate deformable image registration accuracy. the first set contains ct, t1-, and t2-weighted images from a porcine phantom. the phantom was implanted with ten 0.35 mm gold markers and then immobilized in a plastic container with movable dividers. the porcine phantom was compressed in 4 different ways and images were acquired in each position. the markers were visible on the ct scans but not the mr scans due to the selected voxel size. therefore, the markers do not interfere with the registration between mr images and the marker locations can be obtained from the ct images to determine accuracy. the second set of images are synthetic images derived from 28 head and neck squamous cell carcinoma patients who had pre-, mid-, and post-radiotherapy treatment mr scans. from these patients, inter- and intra-patient models were created. four synthetic pre-treatment images were created by using the inter-patient model on a selected template patient. four synthetic post-treatment images were created for each synthetic pre-treatment image using the intra-patient model. head and neck squamous cell carcinoma head-neck phantom mr rtstruct ct measurement image registrations community\"},{\"id\":42937,\"type\":\"collection\",\"slug\":\"nadt-prostate\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/nadt-prostate\\\/\",\"title\":\"Nascent prostate cancer heterogeneity drives evolution and resistance to intense hormonal therapy\",\"short_title\":\"NADT-Prostate\",\"doi\":\"10.7937\\\/TCIA.JHQD-FR46\",\"summary\":\"In order to assess mechanisms predictive of response or resistance to intense neoadjuvant therapy, we performed automated immunohistochemistry on targeted biopsies from patients with intermediate or high risk prostate cancer. All biopsy were stained with H&E or antibodies against: p53, PTEN, AR, PSA, GR, Ki67, SYP and PIN4-cocktail (p63+CK5+K18+AMACR). We further performed automated immunohistochemistry on whole mount radical prostatectomy tissues containing residual tumor. RP tissues were stained with H&E or antibodies against: PTEN, AR, AR-V7, PSA, PSMA, GR, Ki67, SYP and PIN4-cocktail (p63+CK5+K18+AMACR), with some slides stained for other markers of residual tumor, to include NKX3.1 and CAM5.2. Slides were scanned at 20x magnification. For each patient, response to intense neoadjuvant therapy is known. As biopsies were targeted to MRI-visible lesions, lesion-level response to therapy is also known. For each biopsy, gene expression profile by RNA-seq and exome sequencing were performed on tumor tissue laser capture microdissected from serial sections. Up to 4 foci were dissected per biopsy. Whole-genome sequencing of germline DNA from each patient was also performed.Exome and gene expression data corresponding to pre-treatment biopsy tissues has been deposited in dbGaP and GEO. Genome, exome and expression data from post-treatment radical prostatectomy tissues is underway. As slides were stained using an automated slide stainer, this is a rich resource for performing data mining for assessing histogenomic correlates or signatures of treatment response or resistance.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Prostate Cancer\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"Histopathology\",\"Diagnosis\",\"Other\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":37,\"date_updated\":\"2021-10-08\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=\\\/system\\\/files\\\/collectionmetadata\\\/202405\\\/NADT-Prostate_02-29-2024.json\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc0MyIsInBhc3Njb2RlIjoiYWMwMjg1ODk1ZDE5ZTM1M2Y2MDJkNGNjODVlOWI0YzIxMjVlNzEwZiIsInBhY2thZ2VfaWQiOiI3NDMiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"nascent prostate cancer heterogeneity drives evolution and resistance to intense hormonal therapy nadt-prostate in order to assess mechanisms predictive of response or resistance to intense neoadjuvant therapy, we performed automated immunohistochemistry on targeted biopsies from patients with intermediate or high risk prostate cancer. all biopsy were stained with h&e or antibodies against: p53, pten, ar, psa, gr, ki67, syp and pin4-cocktail (p63+ck5+k18+amacr). we further performed automated immunohistochemistry on whole mount radical prostatectomy tissues containing residual tumor. rp tissues were stained with h&e or antibodies against: pten, ar, ar-v7, psa, psma, gr, ki67, syp and pin4-cocktail (p63+ck5+k18+amacr), with some slides stained for other markers of residual tumor, to include nkx3.1 and cam5.2. slides were scanned at 20x magnification. for each patient, response to intense neoadjuvant therapy is known. as biopsies were targeted to mri-visible lesions, lesion-level response to therapy is also known. for each biopsy, gene expression profile by rna-seq and exome sequencing were performed on tumor tissue laser capture microdissected from serial sections. up to 4 foci were dissected per biopsy. whole-genome sequencing of germline dna from each patient was also performed.exome and gene expression data corresponding to pre-treatment biopsy tissues has been deposited in dbgap and geo. genome, exome and expression data from post-treatment radical prostatectomy tissues is underway. as slides were stained using an automated slide stainer, this is a rich resource for performing data mining for assessing histogenomic correlates or signatures of treatment response or resistance. prostate cancer prostate histopathology diagnosis other clinical community\"},{\"id\":42927,\"type\":\"collection\",\"slug\":\"naf-prostate\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/naf-prostate\\\/\",\"title\":\"NaF PROSTATE\",\"short_title\":\"NaF PROSTATE\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.ISOQTHKO\",\"summary\":\"This is a collection of F-18 NaF positron emission tomography\\\/computed tomography (PET\\\/CT) images in patients with prostate cancer, with suspected or known bone involvement.Imaging was performed on a Phillips Gemini TF PET\\\/CT scanner based on 4x4x22mm LYSO (lutetium yttrium orthosilicate) crystal detection elements covering 18cm axial field of view (FOV) and 57cm imaging transaxial FOV. The time of flight resolution is 585ps. The scanner achieves a spatial resolution of 4.8mm at the center of the FOV. Data were reconstructed using the RAMLA iterative OSEM algorithm using 3 iterations and 33 subsets. The scanner uses CT based attenuation correction, along with randoms, normalization, dead time, and a model based scatter correction. The CT component of the scanner is a 16 slice helical CT. The CT images were generated using a low X-ray dose of 120KV, 60mAs setting.The prescribed injected dose was 3mCi IV. Some of the patients had 2 baseline studies within 14 days of each other (with no intervening interventions). Many have follow-up PET\\\/CT imaging performed following therapy (varied) at 6 +\\\/-2months and 12 +\\\/- 2 months. For scientific or other inquiries relating to this data set, please contact the TCIA Helpdesk.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Prostate Cancer\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"PT\",\"CT\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":9,\"date_updated\":\"2013-04-23\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=NaF%20PROSTATE\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_NaF_PROSTATE_06-22-2015.tcia\",\"blob\":\"naf prostate naf prostate this is a collection of f-18 naf positron emission tomography\\\/computed tomography (pet\\\/ct) images in patients with prostate cancer, with suspected or known bone involvement.imaging was performed on a phillips gemini tf pet\\\/ct scanner based on 4x4x22mm lyso (lutetium yttrium orthosilicate) crystal detection elements covering 18cm axial field of view (fov) and 57cm imaging transaxial fov. the time of flight resolution is 585ps. the scanner achieves a spatial resolution of 4.8mm at the center of the fov. data were reconstructed using the ramla iterative osem algorithm using 3 iterations and 33 subsets. the scanner uses ct based attenuation correction, along with randoms, normalization, dead time, and a model based scatter correction. the ct component of the scanner is a 16 slice helical ct. the ct images were generated using a low x-ray dose of 120kv, 60mas setting.the prescribed injected dose was 3mci iv. some of the patients had 2 baseline studies within 14 days of each other (with no intervening interventions). many have follow-up pet\\\/ct imaging performed following therapy (varied) at 6 +\\\/-2months and 12 +\\\/- 2 months. for scientific or other inquiries relating to this data set, please contact the tcia helpdesk. prostate cancer prostate pt ct other community\"},{\"id\":45479,\"type\":\"collection\",\"slug\":\"nlst\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/nlst\\\/\",\"title\":\"National Lung Screening Trial\",\"short_title\":\"NLST\",\"doi\":\"10.7937\\\/TCIA.HMQ8-J677\",\"summary\":\"Background:\\u00a0The aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. The advent of low-dose helical computed tomography (CT) altered the landscape of lung-cancer screening, with studies indicating that low-dose CT detects many tumors at early stages. The National Lung Screening Trial (NLST) was conducted to determine whether screening with low-dose CT could reduce mortality from lung cancer.Methods:\\u00a0From August 2002 through April 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 U.S. medical centers. Participants were randomly assigned to undergo three annual screenings with either low-dose CT (26,722 participants) or single-view posteroanterior chest radiography (26,732). Data were collected on cases of lung cancer and deaths from lung cancer that occurred through December 31, 2009.\\u00a0 This dataset includes the low-dose CT scans from 26,254 of these subjects, as well as digitized histopathology images from 451 subjects.Results:\\u00a0The rate of adherence to screening was more than 90%. The rate of positive screening tests was 24.2% with low-dose CT and 6.9% with radiography over all three rounds. A total of 96.4% of the positive screening results in the low-dose CT group and 94.5% in the radiography group were false positive results. The incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose CT group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [CI], 1.03 to 1.23). There were 247 deaths from lung cancer per 100,000 person-years in the low-dose CT group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose CT screening of 20.0% (95% CI, 6.8 to 26.7; P=0.004). The rate of death from any cause was reduced in the low-dose CT group, as compared with the radiography group, by 6.7% (95% CI, 1.2 to 13.6; P=0.02).Conclusions:\\u00a0Screening with the use of low-dose CT reduces mortality from lung cancer. (Funded by the National Cancer Institute; National Lung Screening Trial ClinicalTrials.gov number,\\u00a0NCT00047385).Data Availability: A summary of the National Lung Screening Trial and its available datasets are provided on the Cancer Data Access System (CDAS). CDAS is maintained by Information Management System (IMS), contracted by the National Cancer Institute (NCI) as keepers and statistical analyzers of the NLST trial data.\\u00a0 The full clinical data set from NLST is available through CDAS.\\u00a0 Users of TCIA can download without restriction a publicly distributable subset of that clinical data, along with the CT and Histopathology images collected during the trial.\\u00a0 (These previously were restricted.)\",\"image\":false,\"program\":\"NCI Trials\",\"cancer_types\":[\"Lung Cancer\",\"Non-Cancer\"],\"cancer_locations\":[\"Chest\"],\"data_types\":[\"CT\",\"Histopathology\",\"Demographic\",\"Diagnosis\",\"Exposure\",\"Measurement\",\"Follow-Up\",\"Other\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":26254,\"date_updated\":\"2021-09-24\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nlst.cancerimagingarchive.net\\\/nbia-search\\\/\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/manifest-NLST_allCT.tcia\",\"blob\":\"national lung screening trial nlst background:\\u00a0the aggressive and heterogeneous nature of lung cancer has thwarted efforts to reduce mortality from this cancer through the use of screening. the advent of low-dose helical computed tomography (ct) altered the landscape of lung-cancer screening, with studies indicating that low-dose ct detects many tumors at early stages. the national lung screening trial (nlst) was conducted to determine whether screening with low-dose ct could reduce mortality from lung cancer.methods:\\u00a0from august 2002 through april 2004, we enrolled 53,454 persons at high risk for lung cancer at 33 u.s. medical centers. participants were randomly assigned to undergo three annual screenings with either low-dose ct (26,722 participants) or single-view posteroanterior chest radiography (26,732). data were collected on cases of lung cancer and deaths from lung cancer that occurred through december 31, 2009.\\u00a0 this dataset includes the low-dose ct scans from 26,254 of these subjects, as well as digitized histopathology images from 451 subjects.results:\\u00a0the rate of adherence to screening was more than 90%. the rate of positive screening tests was 24.2% with low-dose ct and 6.9% with radiography over all three rounds. a total of 96.4% of the positive screening results in the low-dose ct group and 94.5% in the radiography group were false positive results. the incidence of lung cancer was 645 cases per 100,000 person-years (1060 cancers) in the low-dose ct group, as compared with 572 cases per 100,000 person-years (941 cancers) in the radiography group (rate ratio, 1.13; 95% confidence interval [ci], 1.03 to 1.23). there were 247 deaths from lung cancer per 100,000 person-years in the low-dose ct group and 309 deaths per 100,000 person-years in the radiography group, representing a relative reduction in mortality from lung cancer with low-dose ct screening of 20.0% (95% ci, 6.8 to 26.7; p=0.004). the rate of death from any cause was reduced in the low-dose ct group, as compared with the radiography group, by 6.7% (95% ci, 1.2 to 13.6; p=0.02).conclusions:\\u00a0screening with the use of low-dose ct reduces mortality from lung cancer. (funded by the national cancer institute; national lung screening trial clinicaltrials.gov number,\\u00a0nct00047385).data availability: a summary of the national lung screening trial and its available datasets are provided on the cancer data access system (cdas). cdas is maintained by information management system (ims), contracted by the national cancer institute (nci) as keepers and statistical analyzers of the nlst trial data.\\u00a0 the full clinical data set from nlst is available through cdas.\\u00a0 users of tcia can download without restriction a publicly distributable subset of that clinical data, along with the ct and histopathology images collected during the trial.\\u00a0 (these previously were restricted.) lung cancer non-cancer chest ct histopathology demographic diagnosis exposure measurement follow-up other clinical nci trials\"},{\"id\":42943,\"type\":\"collection\",\"slug\":\"nrg-1308\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/nrg-1308\\\/\",\"title\":\"NRG-1308\",\"short_title\":\"NRG-1308\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.6fC8z46u\",\"summary\":\"This data collection consists of images acquired in preparation for NRG-1308, a phase 3 randomized trial to compare overall survival after photon versus proton chemoradiation therapy for inoperable stage II-IIIB NSCLC. The images include IMRT photon and proton scans. A detailed summary of the full trial from which these patients were included can be found in Cancer.gov's clinical trials search here.For questions and information regarding this dataset, please\\u00a0contact TCIA's Helpdesk.\",\"image\":false,\"program\":\"NCI Trials\",\"cancer_types\":[\"Non-small Cell Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"RTDOSE\",\"RTPLAN\",\"RTSTRUCT\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":12,\"date_updated\":\"2015-09-14\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=NRG-1308\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NRG-1308_2015-09-14.tcia\",\"blob\":\"nrg-1308 nrg-1308 this data collection consists of images acquired in preparation for nrg-1308, a phase 3 randomized trial to compare overall survival after photon versus proton chemoradiation therapy for inoperable stage ii-iiib nsclc. the images include imrt photon and proton scans. a detailed summary of the full trial from which these patients were included can be found in cancer.gov's clinical trials search here.for questions and information regarding this dataset, please\\u00a0contact tcia's helpdesk. non-small cell lung cancer lung ct rtdose rtplan rtstruct image analyses nci trials\"},{\"id\":43523,\"type\":\"collection\",\"slug\":\"nsclc-cetuximab\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/nsclc-cetuximab\\\/\",\"title\":\"RTOG-0617\",\"short_title\":\"NSCLC-Cetuximab\",\"doi\":\"10.7937\\\/TCIA.2018.jze75u7v\",\"summary\":\"This collection contains data from the National Cancer Insitute Clinical Trial NCT00533949, \\\"High-Dose or Standard-Dose Radiation Therapy and Chemotherapy With or Without Cetuximab in Treating Patients With Newly Diagnosed Stage III Non-Small Cell Lung Cancer That Cannot Be Removed by Surgery.\\\"\\u00a0 It was sponsored by NCI's Radiation Therapy Oncology Group (RTOG)\\u00a0under study number RTOG-0617.\\u00a0 Clinical data which will allow users to compare the adverse events and treatments between arms in trial\\u00a0are located on the NCTN\\\/NCORP Data Archive.\\u00a0 Each patient's\\u00a0CT of the chest (including the adrenals) was obtained within 6 weeks of registration.\\nTrial Description\\nRadiation therapy uses high-energy x-rays to kill tumor cells. Drugs used in chemotherapy, such as paclitaxel, carboplatin work in different ways to stop the growth of tumor cells, either by killing the cells or by stopping them from dividing. Monoclonal antibodies, such as cetuximab can block tumor growth in different ways. Some block the ability of tumor cells to grow and spread. Others find tumor cells and help kill them or carry tumor-killing substances to them. It is not yet known whether high-dose radiation therapy is more effective than standard-dose radiation therapy when given together with combination chemotherapy with or without cetuximab in treating patients with non-small cell lung cancer. This randomized phase III trial is studying high-dose or standard-dose radiation therapy given together with chemotherapy with or without cetuximab to see how well they work in treating patients with newly diagnosed stage III non-small cell lung cancer that cannot be removed by surgery.\\nTrial Outcomes\\u00a0\\nResults of the trial have been reported in the following publications:\\nMovsas B, Hu C, Sloan J, Bradley J, Komaki R, Masters G, Kavadi V, Narayan S, Michalski J, Johnson DW, Koprowski C, Curran WJ Jr, Garces YI, Gaur R, Wynn RB, Schallenkamp J, Gelblum DY, MacRae RM, Paulus R, Choy H. Quality of Life Analysis of a Radiation Dose-Escalation Study of Patients With Non-Small-Cell Lung Cancer: A Secondary Analysis of the Radiation Therapy Oncology Group 0617 Randomized Clinical Trial. JAMA Oncol. 2016 Mar;2(3):359-67. doi: 10.1001\\\/jamaoncol.2015.3969. (PMC4786463)Bradley JD, Paulus R, Komaki R, Masters G, Blumenschein G, Schild S, Bogart J, Hu C, Forster K, Magliocco A, Kavadi V, Garces YI, Narayan S, Iyengar P, Robinson C, Wynn RB, Koprowski C, Meng J, Beitler J, Gaur R, Curran W Jr, Choy H. Standard-dose versus high-dose conformal radiotherapy with concurrent and consolidation carboplatin plus paclitaxel with or without cetuximab for patients with stage IIIA or IIIB non-small-cell lung cancer (RTOG 0617): a randomised, two-by-two factorial phase 3 study. Lancet Oncol. 2015 Feb;16(2):187-99. doi: 10.1016\\\/S1470-2045(14)71207-0. Epub 2015 Jan 16. (PMC4419359)\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/nctn-logo.png\",\"program\":\"NCI Trials\",\"cancer_types\":[\"Non-small Cell Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"RTPLAN\",\"CT\",\"RTSTRUCT\",\"RTDOSE\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":490,\"date_updated\":\"2019-04-15\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=NSCLC-Cetuximab\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NSCLC-Cetuximab-NBIA-manifest-1542748233353.tcia\",\"blob\":\"rtog-0617 nsclc-cetuximab this collection contains data from the national cancer insitute clinical trial nct00533949, \\\"high-dose or standard-dose radiation therapy and chemotherapy with or without cetuximab in treating patients with newly diagnosed stage iii non-small cell lung cancer that cannot be removed by surgery.\\\"\\u00a0 it was sponsored by nci's radiation therapy oncology group (rtog)\\u00a0under study number rtog-0617.\\u00a0 clinical data which will allow users to compare the adverse events and treatments between arms in trial\\u00a0are located on the nctn\\\/ncorp data archive.\\u00a0 each patient's\\u00a0ct of the chest (including the adrenals) was obtained within 6 weeks of registration.\\ntrial description\\nradiation therapy uses high-energy x-rays to kill tumor cells. drugs used in chemotherapy, such as paclitaxel, carboplatin work in different ways to stop the growth of tumor cells, either by killing the cells or by stopping them from dividing. monoclonal antibodies, such as cetuximab can block tumor growth in different ways. some block the ability of tumor cells to grow and spread. others find tumor cells and help kill them or carry tumor-killing substances to them. it is not yet known whether high-dose radiation therapy is more effective than standard-dose radiation therapy when given together with combination chemotherapy with or without cetuximab in treating patients with non-small cell lung cancer. this randomized phase iii trial is studying high-dose or standard-dose radiation therapy given together with chemotherapy with or without cetuximab to see how well they work in treating patients with newly diagnosed stage iii non-small cell lung cancer that cannot be removed by surgery.\\ntrial outcomes\\u00a0\\nresults of the trial have been reported in the following publications:\\nmovsas b, hu c, sloan j, bradley j, komaki r, masters g, kavadi v, narayan s, michalski j, johnson dw, koprowski c, curran wj jr, garces yi, gaur r, wynn rb, schallenkamp j, gelblum dy, macrae rm, paulus r, choy h. quality of life analysis of a radiation dose-escalation study of patients with non-small-cell lung cancer: a secondary analysis of the radiation therapy oncology group 0617 randomized clinical trial. jama oncol. 2016 mar;2(3):359-67. doi: 10.1001\\\/jamaoncol.2015.3969. (pmc4786463)bradley jd, paulus r, komaki r, masters g, blumenschein g, schild s, bogart j, hu c, forster k, magliocco a, kavadi v, garces yi, narayan s, iyengar p, robinson c, wynn rb, koprowski c, meng j, beitler j, gaur r, curran w jr, choy h. standard-dose versus high-dose conformal radiotherapy with concurrent and consolidation carboplatin plus paclitaxel with or without cetuximab for patients with stage iiia or iiib non-small-cell lung cancer (rtog 0617): a randomised, two-by-two factorial phase 3 study. lancet oncol. 2015 feb;16(2):187-99. doi: 10.1016\\\/s1470-2045(14)71207-0. epub 2015 jan 16. (pmc4419359) non-small cell lung cancer lung rtplan ct rtstruct rtdose clinical nci trials\"},{\"id\":42977,\"type\":\"collection\",\"slug\":\"nsclc-radiogenomics\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/nsclc-radiogenomics\\\/\",\"title\":\"NSCLC Radiogenomics\",\"short_title\":\"NSCLC Radiogenomics\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2017.7hs46erv\",\"summary\":\"Medical image biomarkers of cancer promise improvements in patient care through\\u00a0advances in precision medicine. Compared to genomic biomarkers, image\\u00a0biomarkers\\u00a0provide the advantages of being a non-invasive procedure, and characterizing a\\u00a0heterogeneous tumor in its entirety, as opposed to limited tissue available for\\u00a0biopsy. We\\u00a0developed a unique radiogenomic dataset from a Non-Small Cell Lung Cancer (NSCLC) cohort\\u00a0of 211 subjects. The dataset comprises Computed\\u00a0Tomography (CT), Positron Emission\\u00a0Tomography (PET)\\\/CT images, semantic annotations of the tumors as observed on the\\u00a0medical images using a controlled\\u00a0vocabulary, segmentation maps of tumors in the CT scans,\\u00a0and quantitative values obtained from the PET\\\/CT scans. Imaging data are also paired with\\u00a0gene mutation,\\u00a0RNA sequencing data from samples of surgically excised tumor tissue, and\\u00a0clinical data, including survival outcomes. This dataset was created to facilitate the discovery\\u00a0of the underlying relationship between genomic and medical image features, as well as the\\u00a0development and evaluation of prognostic medical image biomarkers.Further details regarding this data-set may be found in Bakr, et. al, Sci Data. 2018 Oct 16;5:180202. doi: 10.1038\\\/sdata.2018.202, https:\\\/\\\/www.ncbi.nlm.nih.gov\\\/pubmed\\\/30325352.For scientific and other inquiries about this dataset, please\\u00a0contact TCIA's Helpdesk.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/nsclc-featured.png\",\"program\":\"Community\",\"cancer_types\":[\"Non-small Cell Lung Cancer\"],\"cancer_locations\":[\"Chest\"],\"data_types\":[\"CT\",\"PT\",\"SEG\",\"Radiomic Feature\",\"Classification\",\"Demographic\",\"Exposure\",\"Follow-Up\",\"Treatment\",\"Molecular Test\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\",\"Genomics\"],\"species\":[\"Human\"],\"subjects\":211,\"date_updated\":\"2021-06-01\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=NSCLC%20Radiogenomics\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NSCLC_Radiogenomics-6-1-21-Version-4.tcia\",\"blob\":\"nsclc radiogenomics nsclc radiogenomics medical image biomarkers of cancer promise improvements in patient care through\\u00a0advances in precision medicine. compared to genomic biomarkers, image\\u00a0biomarkers\\u00a0provide the advantages of being a non-invasive procedure, and characterizing a\\u00a0heterogeneous tumor in its entirety, as opposed to limited tissue available for\\u00a0biopsy. we\\u00a0developed a unique radiogenomic dataset from a non-small cell lung cancer (nsclc) cohort\\u00a0of 211 subjects. the dataset comprises computed\\u00a0tomography (ct), positron emission\\u00a0tomography (pet)\\\/ct images, semantic annotations of the tumors as observed on the\\u00a0medical images using a controlled\\u00a0vocabulary, segmentation maps of tumors in the ct scans,\\u00a0and quantitative values obtained from the pet\\\/ct scans. imaging data are also paired with\\u00a0gene mutation,\\u00a0rna sequencing data from samples of surgically excised tumor tissue, and\\u00a0clinical data, including survival outcomes. this dataset was created to facilitate the discovery\\u00a0of the underlying relationship between genomic and medical image features, as well as the\\u00a0development and evaluation of prognostic medical image biomarkers.further details regarding this data-set may be found in bakr, et. al, sci data. 2018 oct 16;5:180202. doi: 10.1038\\\/sdata.2018.202, https:\\\/\\\/www.ncbi.nlm.nih.gov\\\/pubmed\\\/30325352.for scientific and other inquiries about this dataset, please\\u00a0contact tcia's helpdesk. non-small cell lung cancer chest ct pt seg radiomic feature classification demographic exposure follow-up treatment molecular test clinical image analyses genomics community\"},{\"id\":43005,\"type\":\"collection\",\"slug\":\"nsclc-radiomics\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/nsclc-radiomics\\\/\",\"title\":\"NSCLC-Radiomics\",\"short_title\":\"NSCLC-Radiomics\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.PF0M9REI\",\"summary\":\"This collection contains images from 422 non-small cell lung cancer (NSCLC) patients. For these patients pretreatment CT scans, manual delineation by a radiation oncologist of the 3D volume of the gross tumor volume and clinical outcome data are available. This dataset refers to the Lung1 dataset of the study published in Nature Communications.\\u00a0In short, this publication applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In present analysis 440 features quantifying tumour image intensity, shape and texture, were extracted.\\u00a0 We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.\\u00a0The DICOM Radiotherapy Structure Sets (RTSTRUCT) and DICOM Segmentation (SEG) files in this data contain a manual delineation by a radiation oncologist of the 3D volume of the primary gross tumor volume (\\\"GTV-1\\\") and selected anatomical structures (i.e., lung, heart and esophagus). Of note, DICOM SEG objects contain a subset of annotations available in RTSTRUCT.The dataset described here (Lung1) was used to build a prognostic radiomic signature. The Lung3 dataset used to investigate the association of radiomic imaging features with gene-expression profiles consisting of 89 NSCLC CT scans with outcome data can be found here: NSCLC-Radiomics-Genomics.Other data sets in the Cancer Imaging Archive that were used in the same study published in Nature Communications: Head-Neck-Radiomics-HN1, NSCLC-Radiomics-Interobserver1, RIDER-LungCT-Seg.For scientific or other inquiries about this dataset,\\u00a0please\\u00a0contact TCIA's Helpdesk.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image2014-7-1-134711.png\",\"program\":\"Community\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"SEG\",\"RTSTRUCT\",\"Classification\",\"Diagnosis\",\"Demographic\",\"Follow-Up\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":422,\"date_updated\":\"2020-10-22\",\"status\":\"Ongoing\",\"license\":\"CC BY-NC 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=NSCLC-Radiomics\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NSCLC-Radiomics-Version-4-Oct-2020-NBIA-manifest.tcia\",\"blob\":\"nsclc-radiomics nsclc-radiomics this collection contains images from 422 non-small cell lung cancer (nsclc) patients. for these patients pretreatment ct scans, manual delineation by a radiation oncologist of the 3d volume of the gross tumor volume and clinical outcome data are available. this dataset refers to the lung1 dataset of the study published in nature communications.\\u00a0in short, this publication applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. in present analysis 440 features quantifying tumour image intensity, shape and texture, were extracted.\\u00a0 we found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. these data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. this may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.\\u00a0the dicom radiotherapy structure sets (rtstruct) and dicom segmentation (seg) files in this data contain a manual delineation by a radiation oncologist of the 3d volume of the primary gross tumor volume (\\\"gtv-1\\\") and selected anatomical structures (i.e., lung, heart and esophagus). of note, dicom seg objects contain a subset of annotations available in rtstruct.the dataset described here (lung1) was used to build a prognostic radiomic signature. the lung3 dataset used to investigate the association of radiomic imaging features with gene-expression profiles consisting of 89 nsclc ct scans with outcome data can be found here: nsclc-radiomics-genomics.other data sets in the cancer imaging archive that were used in the same study published in nature communications: head-neck-radiomics-hn1, nsclc-radiomics-interobserver1, rider-lungct-seg.for scientific or other inquiries about this dataset,\\u00a0please\\u00a0contact tcia's helpdesk. lung cancer lung ct seg rtstruct classification diagnosis demographic follow-up clinical image analyses community\"},{\"id\":43015,\"type\":\"collection\",\"slug\":\"nsclc-radiomics-genomics\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/nsclc-radiomics-genomics\\\/\",\"title\":\"NSCLC-Radiomics-Genomics\",\"short_title\":\"NSCLC-Radiomics-Genomics\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.L4FRET6Z\",\"summary\":\"This collection contains images from 89 non-small cell lung cancer (NSCLC) patients that were treated with surgery. For these patients pretreatment CT scans, gene expression, and clinical data are available. This dataset refers to the Lung3 dataset of the study published in Nature Communications.\\u00a0In short, this publication applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In present analysis 440 features quantifying tumour image intensity, shape and texture, were extracted.\\u00a0 We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.The dataset described here (Lung3) was used to investigate the association of radiomic imaging features with gene-expression profiles. The Lung2 dataset used for training the radiomic biomarker and consisting of 422 NSCLC CT scans with outcome data can be found here: NSCLC-Radiomics. Other data sets in the Cancer Imaging Archive that were used in the same study published in Nature Communications: \\u00a0Head-Neck-Radiomics-HN1,\\u00a0NSCLC-Radiomics-Interobserver1,\\u00a0RIDER-LungCT-Seg.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image2014-6-30-145633.png\",\"program\":\"Community\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"Demographic\",\"Measurement\",\"Diagnosis\",\"Molecular Test\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Microarray\"],\"species\":[\"Human\"],\"subjects\":89,\"date_updated\":\"2014-07-02\",\"status\":\"Complete\",\"license\":\"CC BY-NC 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=NSCLC-Radiomics-Genomics\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_NSCLC-Radiomics-Genomics_06-22-2015.tcia\",\"blob\":\"nsclc-radiomics-genomics nsclc-radiomics-genomics this collection contains images from 89 non-small cell lung cancer (nsclc) patients that were treated with surgery. for these patients pretreatment ct scans, gene expression, and clinical data are available. this dataset refers to the lung3 dataset of the study published in nature communications.\\u00a0in short, this publication applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. in present analysis 440 features quantifying tumour image intensity, shape and texture, were extracted.\\u00a0 we found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. these data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. this may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.the dataset described here (lung3) was used to investigate the association of radiomic imaging features with gene-expression profiles. the lung2 dataset used for training the radiomic biomarker and consisting of 422 nsclc ct scans with outcome data can be found here: nsclc-radiomics. other data sets in the cancer imaging archive that were used in the same study published in nature communications: \\u00a0head-neck-radiomics-hn1,\\u00a0nsclc-radiomics-interobserver1,\\u00a0rider-lungct-seg. lung cancer lung ct demographic measurement diagnosis molecular test clinical genomics microarray community\"},{\"id\":43039,\"type\":\"collection\",\"slug\":\"nsclc-radiomics-interobserver1\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/nsclc-radiomics-interobserver1\\\/\",\"title\":\"NSCLC-Radiomics-Interobserver1\",\"short_title\":\"NSCLC-Radiomics-Interobserver1\",\"doi\":\"10.7937\\\/tcia.2019.cwvlpd26\",\"summary\":\"This collection contains clinical data and computed tomography (CT) from 22 non-small cell lung cancer (NSCLC) radiotherapy patients. For 21 of these patients with pre-treatment CT scans, repeated blinded manual delineations by five different radiation oncologists of the 3D volume of the gross tumor volume on CT and clinical outcome data are available. The above was repeated with the same set of five radiation oncologists, using an in-house autosegmentation tool for initial delineation followed by manual adjustment of the primary gross tumor volume outline. For one patient, clinical data and CT was available but the tumor delineations were not extracted. This patient was included in this collection for the sake of completeness.This dataset refers to the \\\"Multiple delineation\\\" dataset of the study published in Nature Communications (https:\\\/\\\/doi.org\\\/10.1038\\\/ncomms5006). In short, the publication used a radiomics approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. Radiomics refers to the comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features. In the published analysis, 440 features quantifying tumor image intensity, shape, and texture were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumor heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.The delineations are provided in two formats; DICOM RTSTRUCT contains slice by slice contour points of the external outline of the primary tumour. DICOM SEGMENTATION contains binary masks of the same primary tumour. The nomenclature of the structures are as follows:\\u201cGTV-1\\u201d denotes the index tumour, specifically the Gross Tumour Volume (GTV)\\u201cvis\\u201d denotes manual delineation by radiation oncologists\\u201cauto\\u201d denotes assistance by an\\u00a0autosegmentation\\u00a0tool followed with manual editing by radiation oncologists\\u201c1\\u201d, \\u201c2\\u201d, \\u2026., \\u201c5\\u201d denotes the individual radiation oncologists working independently of each otherSide note : Radiation oncologists denoted \\u201c1\\u201d and \\u201c3\\u201d were trainee radiation oncologists at the time of this experiment. Radiation oncologists \\u201c2\\u201d, \\u201c4\\u201d and \\u201c5\\u201d were extensively experienced at the time of this experiment.This dataset is intended to be open access to support repeatability and reproducibility of research in the radiomics domain. This dataset has been referenced in Medical Physics Dataset Article addressing FAIR radiomics practices to support transparency, harmonization and collaboration on radiomics (https:\\\/\\\/doi.org\\\/10.1002\\\/mp.14322).\\u00a0Other data sets in the Cancer Imaging Archive that were used in the same\\u00a0study published in Nature Communications:\\u00a0HEAD-NECK-RADIOMICS-HN1,\\u00a0NSCLC-Radiomics-Interobserver1,\\u00a0NSCLC-Radiomics-Genomics, RIDER-LungCT-Seg.For scientific or other inquiries about this dataset, please\\u00a0contact TCIA's Helpdesk.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NSCLC-RADIOMICS-GRAPHIC.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Non-small Cell Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"RTSTRUCT\",\"CT\",\"SEG\",\"Demographic\",\"Diagnosis\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":22,\"date_updated\":\"2020-08-31\",\"status\":\"Complete\",\"license\":\"CC BY-NC 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=NSCLC-Radiomics-Interobserver1\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NSCLC-RADIOMICS-INTEROBSERVER1-Aug-31-2020-NBIA-manifest.tcia\",\"blob\":\"nsclc-radiomics-interobserver1 nsclc-radiomics-interobserver1 this collection contains clinical data and computed tomography (ct) from 22 non-small cell lung cancer (nsclc) radiotherapy patients. for 21 of these patients with pre-treatment ct scans, repeated blinded manual delineations by five different radiation oncologists of the 3d volume of the gross tumor volume on ct and clinical outcome data are available. the above was repeated with the same set of five radiation oncologists, using an in-house autosegmentation tool for initial delineation followed by manual adjustment of the primary gross tumor volume outline. for one patient, clinical data and ct was available but the tumor delineations were not extracted. this patient was included in this collection for the sake of completeness.this dataset refers to the \\\"multiple delineation\\\" dataset of the study published in nature communications (https:\\\/\\\/doi.org\\\/10.1038\\\/ncomms5006). in short, the publication used a radiomics approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. radiomics refers to the comprehensive quantification of tumor phenotypes by applying a large number of quantitative image features. in the published analysis, 440 features quantifying tumor image intensity, shape, and texture were extracted. we found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumor heterogeneity, was associated with underlying gene-expression patterns. these data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. this may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.the delineations are provided in two formats; dicom rtstruct contains slice by slice contour points of the external outline of the primary tumour. dicom segmentation contains binary masks of the same primary tumour. the nomenclature of the structures are as follows:\\u201cgtv-1\\u201d denotes the index tumour, specifically the gross tumour volume (gtv)\\u201cvis\\u201d denotes manual delineation by radiation oncologists\\u201cauto\\u201d denotes assistance by an\\u00a0autosegmentation\\u00a0tool followed with manual editing by radiation oncologists\\u201c1\\u201d, \\u201c2\\u201d, \\u2026., \\u201c5\\u201d denotes the individual radiation oncologists working independently of each otherside note : radiation oncologists denoted \\u201c1\\u201d and \\u201c3\\u201d were trainee radiation oncologists at the time of this experiment. radiation oncologists \\u201c2\\u201d, \\u201c4\\u201d and \\u201c5\\u201d were extensively experienced at the time of this experiment.this dataset is intended to be open access to support repeatability and reproducibility of research in the radiomics domain. this dataset has been referenced in medical physics dataset article addressing fair radiomics practices to support transparency, harmonization and collaboration on radiomics (https:\\\/\\\/doi.org\\\/10.1002\\\/mp.14322).\\u00a0other data sets in the cancer imaging archive that were used in the same\\u00a0study published in nature communications:\\u00a0head-neck-radiomics-hn1,\\u00a0nsclc-radiomics-interobserver1,\\u00a0nsclc-radiomics-genomics, rider-lungct-seg.for scientific or other inquiries about this dataset, please\\u00a0contact tcia's helpdesk. non-small cell lung cancer lung rtstruct ct seg demographic diagnosis clinical community\"},{\"id\":43055,\"type\":\"collection\",\"slug\":\"osteosarcoma-tumor-assessment\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/osteosarcoma-tumor-assessment\\\/\",\"title\":\"Osteosarcoma data from UT Southwestern\\\/UT Dallas for Viable and Necrotic Tumor Assessment\",\"short_title\":\"Osteosarcoma-Tumor-Assessment\",\"doi\":\"10.7937\\\/tcia.2019.bvhjhdas\",\"summary\":\"Osteosarcoma is the most common type of bone cancer that occurs in adolescents in the age of 10 to 14 years. The dataset is composed of Hematoxylin and eosin (H&E) stained osteosarcoma histology images. The data was collected by a team of clinical scientists at University of Texas Southwestern Medical Center, Dallas. Archival samples for 50 patients treated at Children\\u2019 s Medical Center, Dallas, between 1995 and 2015, were used to create this dataset. Four patients (out of 50) were selected by pathologists based on diversity of tumor specimens after surgical resection. The images are labelled as Non-Tumor, Viable Tumor and Necrosis according to the predominant cancer type in each image. The annotation was performed by two medical experts. All images were divided between two pathologists for the annotation activity. Each image had a single annotation as any given image was annotated by only one pathologist. The dataset consists of 1144 images of size 1024 X 1024 at 10X resolution with the following distribution: 536 (47%) non-tumor images, 263 (23%) necrotic tumor images and 345 (30%) viable tumor tiles.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Osteosarcoma\"],\"cancer_locations\":[\"Bone\"],\"data_types\":[\"Histopathology\",\"Classification\",\"Measurement\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":4,\"date_updated\":\"2019-03-22\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22Osteosarcoma-Tumor-Assessment%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc1MiIsInBhc3Njb2RlIjoiYjVmYmMyMWRiZjNlNjQ4NmM3NDc0MzlmNzY0M2FhZTA3NTk2NzJhMSIsInBhY2thZ2VfaWQiOiI3NTIiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"osteosarcoma data from ut southwestern\\\/ut dallas for viable and necrotic tumor assessment osteosarcoma-tumor-assessment osteosarcoma is the most common type of bone cancer that occurs in adolescents in the age of 10 to 14 years. the dataset is composed of hematoxylin and eosin (h&e) stained osteosarcoma histology images. the data was collected by a team of clinical scientists at university of texas southwestern medical center, dallas. archival samples for 50 patients treated at children\\u2019 s medical center, dallas, between 1995 and 2015, were used to create this dataset. four patients (out of 50) were selected by pathologists based on diversity of tumor specimens after surgical resection. the images are labelled as non-tumor, viable tumor and necrosis according to the predominant cancer type in each image. the annotation was performed by two medical experts. all images were divided between two pathologists for the annotation activity. each image had a single annotation as any given image was annotated by only one pathologist. the dataset consists of 1144 images of size 1024 x 1024 at 10x resolution with the following distribution: 536 (47%) non-tumor images, 263 (23%) necrotic tumor images and 345 (30%) viable tumor tiles. osteosarcoma bone histopathology classification measurement image analyses community\"},{\"id\":41295,\"type\":\"collection\",\"slug\":\"ovarian-bevacizumab-response\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ovarian-bevacizumab-response\\\/\",\"title\":\"A dataset of histopathological whole slide images for classification of Treatment effectiveness to ovarian cancer\",\"short_title\":\"Ovarian Bevacizumab Response\",\"doi\":\"10.7937\\\/TCIA.985G-EY35\",\"summary\":\"\\u00a0Despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70% of advanced patients are with recurrent cancer and decease. Bevacizumab has been recently approved by FDA as a monotherapy for advanced ovarian cancer in combination with chemotherapy. Considering the cost, potential toxicity, and finding that only a portion of patients will benefit from these drugs, the identification of a new predictive method for the treatment of ovarian cancer remains an urgent unmet medical need. Prediction of therapeutic effects and individualization of therapeutic strategies are critical, but to the authors' best knowledge, there are no effective biomarkers that can be used to predict patient response to bevacizumab treatment for ovarian cancer. This dataset helps researchers to explore and develop methods to predict the therapeutic effect of patients with epithelial ovarian cancer to bevacizumab.The dataset consists of de-identified 288 hematoxylin and eosin (H&E) stained whole slides with clinical information from 78 patients. The slides were collected from the tissue bank of the Tri-Service General Hospital and the National Defense Medical Center, Taipei, Taiwan. Whole Slide Images (WSIs) were acquired with a digital slide scanner (Leica AT2) with a 20x objective lens. The dimension of the ovarian cancer slides is 54342x41048 in pixels and 27.34 x 20.66mm on average. The bevacizumab treatment is effective in 162 and invalid in 126 of the dataset.\\u00a0 Ethical approvals have been obtained from the research ethics committee of the Tri-Service General Hospital (TSGHIRB No.1-107-05-171 and No.B202005070), and the data were de-identified and used for a retrospective study without impacting patient care.The clinicopathologic characteristics of patients were recorded by the data managers of the Gynecologic Oncology Center. Age, pre- and post-treatment serum CA-125 concentrations, histologic subtype, and recurrence, and survival status were recorded. A tumor, which is resistant to bevacizumab therapy, is defined as a measurable regrowth of the tumor or as a serum CA-125 concentration more than twice the value of the upper limit of normal during the treatment course for the bevacizumab therapy (i.e., the patient had the detectable disease or elevated CA-125 level following cytoreductive surgery combine with carboplatin\\\/paclitaxel plus bevacizumab). A tumor, which is sensitive to bevacizumab therapy, is defined as no measurable regrowth of the tumor or as a serum CA-125 concentration under than twice the value of the upper limit of normal during the treatment course for the bevacizumab therapy.This dataset is further described in the following publications:Wang et al.\\u00a0Weakly Supervised Deep Learning for Prediction of Treatment Effectiveness on Ovarian Cancer from Histopathology Images. Computerized Medical Imaging and Graphics. https:\\\/\\\/doi.org\\\/10.1016\\\/j.compmedimag.2022.102093Wang, CW., Chang, CC., Khalil, M.A.\\u00a0et al.\\u00a0Histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer.\\u00a0Sci Data\\u00a09,\\u00a025 (2022). https:\\\/\\\/doi.org\\\/10.1038\\\/s41597-022-01127-6\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ob-response.png\",\"program\":\"Community\",\"cancer_types\":[\"Ovarian Cancer\"],\"cancer_locations\":[\"Ovary\"],\"data_types\":[\"Histopathology\",\"Follow-Up\",\"Measurement\",\"Treatment\",\"Exposure\",\"Demographic\",\"Diagnosis\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":78,\"date_updated\":\"2023-04-26\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=\\\/system\\\/files\\\/collectionmetadata\\\/202405\\\/Ovarian%20Bevacizumab%20Response_03-01-2024.json\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc0NCIsInBhc3Njb2RlIjoiMWZiZTNmMzgwNmY1MTEyNjBlODkxYjk0MWVjYzdkZTY2MGQwZGNkYSIsInBhY2thZ2VfaWQiOiI3NDQiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"a dataset of histopathological whole slide images for classification of treatment effectiveness to ovarian cancer ovarian bevacizumab response \\u00a0despite the progress made during the last two decades in the surgery and chemotherapy of ovarian cancer, more than 70% of advanced patients are with recurrent cancer and decease. bevacizumab has been recently approved by fda as a monotherapy for advanced ovarian cancer in combination with chemotherapy. considering the cost, potential toxicity, and finding that only a portion of patients will benefit from these drugs, the identification of a new predictive method for the treatment of ovarian cancer remains an urgent unmet medical need. prediction of therapeutic effects and individualization of therapeutic strategies are critical, but to the authors' best knowledge, there are no effective biomarkers that can be used to predict patient response to bevacizumab treatment for ovarian cancer. this dataset helps researchers to explore and develop methods to predict the therapeutic effect of patients with epithelial ovarian cancer to bevacizumab.the dataset consists of de-identified 288 hematoxylin and eosin (h&e) stained whole slides with clinical information from 78 patients. the slides were collected from the tissue bank of the tri-service general hospital and the national defense medical center, taipei, taiwan. whole slide images (wsis) were acquired with a digital slide scanner (leica at2) with a 20x objective lens. the dimension of the ovarian cancer slides is 54342x41048 in pixels and 27.34 x 20.66mm on average. the bevacizumab treatment is effective in 162 and invalid in 126 of the dataset.\\u00a0 ethical approvals have been obtained from the research ethics committee of the tri-service general hospital (tsghirb no.1-107-05-171 and no.b202005070), and the data were de-identified and used for a retrospective study without impacting patient care.the clinicopathologic characteristics of patients were recorded by the data managers of the gynecologic oncology center. age, pre- and post-treatment serum ca-125 concentrations, histologic subtype, and recurrence, and survival status were recorded. a tumor, which is resistant to bevacizumab therapy, is defined as a measurable regrowth of the tumor or as a serum ca-125 concentration more than twice the value of the upper limit of normal during the treatment course for the bevacizumab therapy (i.e., the patient had the detectable disease or elevated ca-125 level following cytoreductive surgery combine with carboplatin\\\/paclitaxel plus bevacizumab). a tumor, which is sensitive to bevacizumab therapy, is defined as no measurable regrowth of the tumor or as a serum ca-125 concentration under than twice the value of the upper limit of normal during the treatment course for the bevacizumab therapy.this dataset is further described in the following publications:wang et al.\\u00a0weakly supervised deep learning for prediction of treatment effectiveness on ovarian cancer from histopathology images. computerized medical imaging and graphics. https:\\\/\\\/doi.org\\\/10.1016\\\/j.compmedimag.2022.102093wang, cw., chang, cc., khalil, m.a.\\u00a0et al.\\u00a0histopathological whole slide image dataset for classification of treatment effectiveness to ovarian cancer.\\u00a0sci data\\u00a09,\\u00a025 (2022). https:\\\/\\\/doi.org\\\/10.1038\\\/s41597-022-01127-6 ovarian cancer ovary histopathology follow-up measurement treatment exposure demographic diagnosis clinical community\"},{\"id\":43071,\"type\":\"collection\",\"slug\":\"pancreas-ct\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/pancreas-ct\\\/\",\"title\":\"Pancreas-CT\",\"short_title\":\"Pancreas-CT\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.tNB1kqBU\",\"summary\":\"The National Institutes of Health Clinical Center performed 82 abdominal contrast enhanced 3D CT scans (~70 seconds after intravenous contrast injection in portal-venous) from 53 male and 27 female subjects.\\u00a0 Seventeen of the subjects are healthy kidney donors scanned prior to nephrectomy.\\u00a0 The remaining 65 patients were selected by a radiologist from patients who neither had major abdominal pathologies nor pancreatic cancer lesions.\\u00a0 Subjects' ages range from 18 to 76 years with a mean age of 46.8 \\u00b1 16.7. The CT scans have resolutions of 512x512 pixels with varying pixel sizes and slice thickness between 1.5 \\u2212 2.5 mm, acquired on Philips and Siemens MDCT scanners (120 kVp tube voltage).A medical student manually performed slice-by-slice segmentations of the pancreas as ground-truth and these were verified\\\/modified by an experienced radiologist.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Healthy Controls (non-cancer)\"],\"cancer_locations\":[\"Pancreas\"],\"data_types\":[\"CT\",\"Segmentation\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":82,\"date_updated\":\"2020-09-10\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=Pancreas-CT\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Pancreas-CT-20200910.tcia\",\"blob\":\"pancreas-ct pancreas-ct the national institutes of health clinical center performed 82 abdominal contrast enhanced 3d ct scans (~70 seconds after intravenous contrast injection in portal-venous) from 53 male and 27 female subjects.\\u00a0 seventeen of the subjects are healthy kidney donors scanned prior to nephrectomy.\\u00a0 the remaining 65 patients were selected by a radiologist from patients who neither had major abdominal pathologies nor pancreatic cancer lesions.\\u00a0 subjects' ages range from 18 to 76 years with a mean age of 46.8 \\u00b1 16.7. the ct scans have resolutions of 512x512 pixels with varying pixel sizes and slice thickness between 1.5 \\u2212 2.5 mm, acquired on philips and siemens mdct scanners (120 kvp tube voltage).a medical student manually performed slice-by-slice segmentations of the pancreas as ground-truth and these were verified\\\/modified by an experienced radiologist. healthy controls (non-cancer) pancreas ct segmentation image analyses community\"},{\"id\":41631,\"type\":\"collection\",\"slug\":\"pancreatic-ct-cbct-seg\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/pancreatic-ct-cbct-seg\\\/\",\"title\":\"Breath-hold CT and cone-beam CT images with expert manual organ-at-risk segmentations from radiation treatments of locally advanced pancreatic cancer\",\"short_title\":\"Pancreatic-CT-CBCT-SEG\",\"doi\":\"10.7937\\\/TCIA.ESHQ-4D90\",\"summary\":\"Accurate deformable registration between CT and cone-beam CT (CBCT) images of pancreatic cancer patients treated with high radiation doses is highly desirable for assessing changes in organ-at-risk (OAR) locations and shapes at treatment. The objective of this dataset is to provide a means of evaluating the performance of CT-to-CBCT deformable registration and auto-segmentation algorithms for delineating OARs. The data were retrospectively obtained with IRB approval from 40 patients who received ablative radiation therapy (>= 100Gy biologically effective dose) for locally advanced pancreatic cancer at Memorial Sloan Kettering Cancer Center.The images (one treatment planning CT and two CBCT scans from each patient) were acquired during a deep inspiration breath-hold verified with an external respiratory monitor. The planning CT was acquired with a diagnostic-quality scanner in helical mode with iodinated contrast. The kilovoltage CBCT scans were acquired without contrast and with the patient in the treatment position. Most of these (58 out of 80) were limited-view scans that were acquired with a 200-degree gantry rotation, resulting in faster acquisition and increased likelihood that the scan was acquired in a single breath hold. The limited-view CBCT scans had 25cm reconstructed diameter and 18cm length. The remaining 22 CBCT scans were acquired with 360-degree gantry rotation and were subsequently cropped to the same reconstructed dimensions as the limited-view scans. Each CBCT image was rigidly registered to the planning CT by aligning to an implanted fiducial marker or to a stent implanted during surgery. The CBCT was then resampled to match the voxel dimensions and volume of the planning CT.A volume of interest (VOI) was defined on the planning CT by expanding the microscopic-dose planning target volume by 1cm. Its purpose was to define a volume fully contained within both the planning CT and CBCT reconstruction volumes while excluding the periphery of the CBCT volume which was subject to cupping artifacts. It included the portions of the OARs receiving moderate to high dose. On the planning CT and each of the CBCT scans, two OAR volumes were delineated by a radiation oncologist: (1) stomach with the first two segments of duodenum, and (2) the remainder of the small bowel. On the CBCT scans, only the portions of the OARs within the VOI were delineated, whereas on the planning CT scans, the OAR delineations extended up to 2 cm outside of the VOI (in some cases contours may extend beyond these limits). The manual delineation was distributed among six radiation oncologists, such that the same physician completed all images belonging to the same patient. The contours were subsequently reviewed by two trained medical physicists and edited as needed to adhere to delineation guidelines.\\u00a0With this dataset researchers will be able to test the performance of deformable registration and auto-segmentation algorithms by comparison to physician-drawn OAR segmentations; an example can be found in a publication by Xu et al. \\u00a0Cone-beam CT scans of the pancreatic disease site are particularly challenging because of the presence of residual-motion-induced image artifacts such as blurring, streaking, x-ray scatter, as well as cupping artifacts from the limited-view acquisition. OAR structures in the CBCT often have limited visibility owing to the lack of contrast and the presence of gas pockets. Currently there is a lack of reliable CT-to-CBCT deformable image registration tools for soft tissues in abdomen. This dataset is unique from existing TCIA collections in that the context is focused on CBCT-guided radiation therapy of pancreatic cancer, the images are acquired with deep-inspiration breath-hold, and physician-drawn segmentations of organs at risk provide a criterion standard for testing registration and auto-segmentation algorithms.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Pancreatic-CT-CBCT-SEG-TCIA_Figure.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Pancreatic Cancer\"],\"cancer_locations\":[\"Pancreas\"],\"data_types\":[\"CT\",\"RTSTRUCT\",\"RTDOSE\",\"Other\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":40,\"date_updated\":\"2022-08-23\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Pancreatic-CT-CBCT-SEG\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Pancreatic-CT-CBCT-SEG_v2_20220823.tcia\",\"blob\":\"breath-hold ct and cone-beam ct images with expert manual organ-at-risk segmentations from radiation treatments of locally advanced pancreatic cancer pancreatic-ct-cbct-seg accurate deformable registration between ct and cone-beam ct (cbct) images of pancreatic cancer patients treated with high radiation doses is highly desirable for assessing changes in organ-at-risk (oar) locations and shapes at treatment. the objective of this dataset is to provide a means of evaluating the performance of ct-to-cbct deformable registration and auto-segmentation algorithms for delineating oars. the data were retrospectively obtained with irb approval from 40 patients who received ablative radiation therapy (>= 100gy biologically effective dose) for locally advanced pancreatic cancer at memorial sloan kettering cancer center.the images (one treatment planning ct and two cbct scans from each patient) were acquired during a deep inspiration breath-hold verified with an external respiratory monitor. the planning ct was acquired with a diagnostic-quality scanner in helical mode with iodinated contrast. the kilovoltage cbct scans were acquired without contrast and with the patient in the treatment position. most of these (58 out of 80) were limited-view scans that were acquired with a 200-degree gantry rotation, resulting in faster acquisition and increased likelihood that the scan was acquired in a single breath hold. the limited-view cbct scans had 25cm reconstructed diameter and 18cm length. the remaining 22 cbct scans were acquired with 360-degree gantry rotation and were subsequently cropped to the same reconstructed dimensions as the limited-view scans. each cbct image was rigidly registered to the planning ct by aligning to an implanted fiducial marker or to a stent implanted during surgery. the cbct was then resampled to match the voxel dimensions and volume of the planning ct.a volume of interest (voi) was defined on the planning ct by expanding the microscopic-dose planning target volume by 1cm. its purpose was to define a volume fully contained within both the planning ct and cbct reconstruction volumes while excluding the periphery of the cbct volume which was subject to cupping artifacts. it included the portions of the oars receiving moderate to high dose. on the planning ct and each of the cbct scans, two oar volumes were delineated by a radiation oncologist: (1) stomach with the first two segments of duodenum, and (2) the remainder of the small bowel. on the cbct scans, only the portions of the oars within the voi were delineated, whereas on the planning ct scans, the oar delineations extended up to 2 cm outside of the voi (in some cases contours may extend beyond these limits). the manual delineation was distributed among six radiation oncologists, such that the same physician completed all images belonging to the same patient. the contours were subsequently reviewed by two trained medical physicists and edited as needed to adhere to delineation guidelines.\\u00a0with this dataset researchers will be able to test the performance of deformable registration and auto-segmentation algorithms by comparison to physician-drawn oar segmentations; an example can be found in a publication by xu et al. \\u00a0cone-beam ct scans of the pancreatic disease site are particularly challenging because of the presence of residual-motion-induced image artifacts such as blurring, streaking, x-ray scatter, as well as cupping artifacts from the limited-view acquisition. oar structures in the cbct often have limited visibility owing to the lack of contrast and the presence of gas pockets. currently there is a lack of reliable ct-to-cbct deformable image registration tools for soft tissues in abdomen. this dataset is unique from existing tcia collections in that the context is focused on cbct-guided radiation therapy of pancreatic cancer, the images are acquired with deep-inspiration breath-hold, and physician-drawn segmentations of organs at risk provide a criterion standard for testing registration and auto-segmentation algorithms. pancreatic cancer pancreas ct rtstruct rtdose other image analyses community\"},{\"id\":41125,\"type\":\"collection\",\"slug\":\"pca_bx_3dpathology\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/pca_bx_3dpathology\\\/\",\"title\":\"3D pathology of prostate biopsies with biochemical recurrence outcomes: raw H&E-analog datasets and image translation-assisted segmentation in 3D (ITAS3D) datasets\",\"short_title\":\"PCa_Bx_3Dpathology\",\"doi\":\"10.7937\\\/44MA-GX21\",\"summary\":\"This collection provides public access to a 3D pathology dataset of prostate cancer, allowing researchers to further investigate various 3D tissue structures and their correlation with prostate cancer patient outcomes (biochemical recurrence). These 3D tissue structures are revealed through: (1) a H&E-analog stain, (2) synthetically generated immunofluorescence staining of CK8 (targeting the luminal epithelial cells of all prostate glands), and (3) 3D segmentation masks of the gland lumen, epithelium, and stromal regions of prostate biopsies.\\u00a0 This data collection will promote research in the field of computational 3D pathology for clinical decision support.In this TCIA collection, we provide the 2x down-sampled fused OTLS-imaged images (H&E-analog staining), the synthetic cytokeratin-8 (CK8) immunofluorescent images at 2x-downsampled resolution, the 3D semantic segmentation masks of glands at 4x down-sampled resolution, the clinical data for patient outcomes (biochemical recurrence), and the coordinates for the cancer-enriched regions of each biopsy. All datasets are from the 50 patient cases studied in this publication: [W. Xie et al., Cancer Research, 2022]. \\u00a0The Python code for the deep-learning models, and for 3D glandular segmentations based on synthetic-CK8 datasets, are available on GitHub at https:\\\/\\\/github.com\\\/WeisiX\\\/ITAS3D.Note that the 3D pathology datasets provided in this collection were generated in Dr. Jonathan Liu\\u2019s lab at the University of Washington with a custom open-top light-sheet (OTLS) microscope developed by the lab [A.K. Glaser et al., Nature Communications, 2019].\\u00a0 There is no clinical metadata within the imaging files and all patients are referred to with coded identifiers. \\u00a0All of the clinical outcomes data provided in this collection have already been published within the supplement of [W. Xie et al., Cancer Research, 2022].\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/FIG_3.png\",\"program\":\"Community\",\"cancer_types\":[\"Prostate Cancer\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"Histopathology\",\"Follow-Up\"],\"supporting_data\":[\"Clinical\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":50,\"date_updated\":\"2023-03-07\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22PCa_Bx_3Dpathology%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjczOCIsInBhc3Njb2RlIjoiMmM5YTk1YTYyOGZkNDA4MjQ4OTI5YjE0MjJlNDRhZTJiMTBkNmJjMSIsInBhY2thZ2VfaWQiOiI3MzgiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"3d pathology of prostate biopsies with biochemical recurrence outcomes: raw h&e-analog datasets and image translation-assisted segmentation in 3d (itas3d) datasets pca_bx_3dpathology this collection provides public access to a 3d pathology dataset of prostate cancer, allowing researchers to further investigate various 3d tissue structures and their correlation with prostate cancer patient outcomes (biochemical recurrence). these 3d tissue structures are revealed through: (1) a h&e-analog stain, (2) synthetically generated immunofluorescence staining of ck8 (targeting the luminal epithelial cells of all prostate glands), and (3) 3d segmentation masks of the gland lumen, epithelium, and stromal regions of prostate biopsies.\\u00a0 this data collection will promote research in the field of computational 3d pathology for clinical decision support.in this tcia collection, we provide the 2x down-sampled fused otls-imaged images (h&e-analog staining), the synthetic cytokeratin-8 (ck8) immunofluorescent images at 2x-downsampled resolution, the 3d semantic segmentation masks of glands at 4x down-sampled resolution, the clinical data for patient outcomes (biochemical recurrence), and the coordinates for the cancer-enriched regions of each biopsy. all datasets are from the 50 patient cases studied in this publication: [w. xie et al., cancer research, 2022]. \\u00a0the python code for the deep-learning models, and for 3d glandular segmentations based on synthetic-ck8 datasets, are available on github at https:\\\/\\\/github.com\\\/weisix\\\/itas3d.note that the 3d pathology datasets provided in this collection were generated in dr. jonathan liu\\u2019s lab at the university of washington with a custom open-top light-sheet (otls) microscope developed by the lab [a.k. glaser et al., nature communications, 2019].\\u00a0 there is no clinical metadata within the imaging files and all patients are referred to with coded identifiers. \\u00a0all of the clinical outcomes data provided in this collection have already been published within the supplement of [w. xie et al., cancer research, 2022]. prostate cancer prostate histopathology follow-up clinical software\\\/source code community\"},{\"id\":42547,\"type\":\"collection\",\"slug\":\"pdmr-292921-168-r\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/pdmr-292921-168-r\\\/\",\"title\":\"Imaging characterization of a metastatic patient derived model of adenocarcinoma pancreas:\",\"short_title\":\"PDMR-292921-168-R\",\"doi\":\"10.7937\\\/TCIA.2020.PCAK-8Z10\",\"summary\":\"Pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist are resource intensive because determination of the presence of metastatic disease, metastatic burden, and response to therapy normally require multiple timed cohorts with animal sacrifice and extensive pathological examination. We identified and characterized a patient derived xenograft model with metastatic potential, adenocarcinoma pancreas xenograft 292921-168-R. In this study we performed a detailed imaging characterization (workflow below) of this model, which develops spontaneous lung metastases, details are provided in the attached standard operating procedures. Tumors in half of the mice were resected in the range 200-300 mm3 size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.\\u00a0The imaging characteristics of\\u00a0this model (PDMR-292921-168-R), which is available from the National Cancer Institute Patient-Derived Models\\u00a0Repository (https:\\\/\\\/pdmr.cancer.gov\\\/), is highly favorable for preclinical research studies of\\u00a0metastatic disease when used in conjunction with non-contrast T2 weighted MRI.Results: Adenocarcinoma pancreas (PDMR-292921-168-R)\\u00a0Table 1: Penetrance and location of pathological confirmed metastatic lesion(s).     # animals in Group# animals that displayed metastasis in MRI and confirmed by PathologyPathology confirmation of MRI (primary imaging site)Other confirmed Location (s)Mouse ID: MRI with pathology confirmation of metastasis10 (non-resected)4\\u00a0 \\u00a0(5 mice were EU due to xenograft size prior to observation of metastases)LungLung1409, 1410, 1418, 142310 (resected)10LungLung, Kidney, Nodes, Peritoneal Wall1405, 1407, 1408, 1411, 1413,1415, 1416, 1419, 1420, 1422Percent penetrance with respect to the average time-to-metastasis for non-resected (plot A: time from implant) and resected (plot B: time from tumor resection) cohorts.   Plot APlot BPET\\\/CT Characterization of the primary tumor:\\u00a0 Baseline PET (SOP available below) were performed when tumor reached an approximate 200 mm3.\\u00a0 \\u00a0Average SUVmax values (n=5) were calculated; [18F]FDG: 1.3 \\u00b1 0.2 and [18F]FLT: 2.3 \\u00b1 0.6.ConclusionExcellent metastatic model with at least 50% penetrance un-resected and 100% with planned early resection.\\u00a0 Metastases are well observed on T2 MRI imaging allowing non-invasive evaluation in treatment trials.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Flow-Chart-Pancreas.png\",\"program\":\"PDMR\",\"cancer_types\":[\"Adenocarcinoma Pancreas\"],\"cancer_locations\":[\"Abdomen\"],\"data_types\":[\"MR\",\"SR\",\"Protocol\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Mouse\"],\"subjects\":20,\"date_updated\":\"2020-03-23\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=PDMR-292921-168-R\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PDMR-292921-168-R.tcia\",\"blob\":\"imaging characterization of a metastatic patient derived model of adenocarcinoma pancreas: pdmr-292921-168-r pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist are resource intensive because determination of the presence of metastatic disease, metastatic burden, and response to therapy normally require multiple timed cohorts with animal sacrifice and extensive pathological examination. we identified and characterized a patient derived xenograft model with metastatic potential, adenocarcinoma pancreas xenograft 292921-168-r. in this study we performed a detailed imaging characterization (workflow below) of this model, which develops spontaneous lung metastases, details are provided in the attached standard operating procedures. tumors in half of the mice were resected in the range 200-300 mm3 size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.\\u00a0the imaging characteristics of\\u00a0this model (pdmr-292921-168-r), which is available from the national cancer institute patient-derived models\\u00a0repository (https:\\\/\\\/pdmr.cancer.gov\\\/), is highly favorable for preclinical research studies of\\u00a0metastatic disease when used in conjunction with non-contrast t2 weighted mri.results: adenocarcinoma pancreas (pdmr-292921-168-r)\\u00a0table 1: penetrance and location of pathological confirmed metastatic lesion(s).     # animals in group# animals that displayed metastasis in mri and confirmed by pathologypathology confirmation of mri (primary imaging site)other confirmed location (s)mouse id: mri with pathology confirmation of metastasis10 (non-resected)4\\u00a0 \\u00a0(5 mice were eu due to xenograft size prior to observation of metastases)lunglung1409, 1410, 1418, 142310 (resected)10lunglung, kidney, nodes, peritoneal wall1405, 1407, 1408, 1411, 1413,1415, 1416, 1419, 1420, 1422percent penetrance with respect to the average time-to-metastasis for non-resected (plot a: time from implant) and resected (plot b: time from tumor resection) cohorts.   plot aplot bpet\\\/ct characterization of the primary tumor:\\u00a0 baseline pet (sop available below) were performed when tumor reached an approximate 200 mm3.\\u00a0 \\u00a0average suvmax values (n=5) were calculated; [18f]fdg: 1.3 \\u00b1 0.2 and [18f]flt: 2.3 \\u00b1 0.6.conclusionexcellent metastatic model with at least 50% penetrance un-resected and 100% with planned early resection.\\u00a0 metastases are well observed on t2 mri imaging allowing non-invasive evaluation in treatment trials. adenocarcinoma pancreas abdomen mr sr protocol clinical pdmr\"},{\"id\":42587,\"type\":\"collection\",\"slug\":\"pdmr-425362-245-t\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/pdmr-425362-245-t\\\/\",\"title\":\"Imaging characterization of a metastatic patient derived model of melanoma:\",\"short_title\":\"PDMR-425362-245-T\",\"doi\":\"10.7937\\\/TCIA.2020.7YRS-7J97\",\"summary\":\"Pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist are resource intensive because determination of the presence of metastatic disease, metastatic burden, and response to therapy normally require multiple timed cohorts with animal sacrifice and extensive pathological examination. We identified and characterized a patient derived xenograft model with metastatic potential, melanoma xenograft 425362-245-T. In this study we performed a detailed imaging characterization (workflow below) of this model, which develops spontaneous lung metastases, details are provided in the attached standard operating procedures. Tumors in half of the mice were resected in the range 200-300 mm3 size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.\\u00a0The imaging characteristics of\\u00a0this model (PDMR-425362-245-T) which is available from the National Cancer Institute Patient-Derived Models\\u00a0Repository (https:\\\/\\\/pdmr.cancer.gov\\\/), is highly favorable for preclinical research studies of\\u00a0metastatic disease when used in conjunction with non-contrast T2 weighted MRI.Results: Melanoma (PDMR-425362-245-T)Table 1: Penetrance and location of pathological confirmed metastatic lesion(s).     # animals in group# animals that displayed metastasis in MRI and confirmed by PathologyPathology confirmation of MRI (primary imaging site)Other confirmed Location (s)Mouse ID: MRI with pathology confirmation of metastasis10 (non-resected)4(6 mice were EU due to xenograft size prior to observation of metastasis)LungKidney1512, 1516, 1518, 152010 (resected)10LungKidney, Liver, Pancreas1506, 1508, 1509, 1510, 1511, 1515, 1517, 1519, 1521, 1523Percent penetrance with respect to the average time-to-metastasis for non-resected (plot A: time from implant) and resected (plot B: time from tumor resection) cohorts.   Plot APlot BPET\\\/CT Characterization of the primary tumor:\\u00a0 Baseline PET (SOP attached) were performed when tumor reached an approximate 200 mm3.\\u00a0\\u00a0 Average SUVmax values (n=5) were calculated; [18F]FDG: 2.7 \\u00b1 0.5 and [18F]FLT: 2.0 \\u00b1 0.3.Conclusion:Melanoma PDMR-425362-245-T model can be challenging due to the rapid growth of the xenograft and regrowth.\\u00a0 Metastases was well observed on T2 MRI imaging allowing non-invasive evaluation in treatment trials.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PDMR-2.png\",\"program\":\"PDMR\",\"cancer_types\":[\"Melanoma\"],\"cancer_locations\":[\"Abdomen\"],\"data_types\":[\"SR\",\"MR\",\"Other\",\"Protocol\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Mouse\"],\"subjects\":20,\"date_updated\":\"2021-02-17\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=PDMR-425362-245-T\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PDMR-425362-245-T.tcia\",\"blob\":\"imaging characterization of a metastatic patient derived model of melanoma: pdmr-425362-245-t pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist are resource intensive because determination of the presence of metastatic disease, metastatic burden, and response to therapy normally require multiple timed cohorts with animal sacrifice and extensive pathological examination. we identified and characterized a patient derived xenograft model with metastatic potential, melanoma xenograft 425362-245-t. in this study we performed a detailed imaging characterization (workflow below) of this model, which develops spontaneous lung metastases, details are provided in the attached standard operating procedures. tumors in half of the mice were resected in the range 200-300 mm3 size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.\\u00a0the imaging characteristics of\\u00a0this model (pdmr-425362-245-t) which is available from the national cancer institute patient-derived models\\u00a0repository (https:\\\/\\\/pdmr.cancer.gov\\\/), is highly favorable for preclinical research studies of\\u00a0metastatic disease when used in conjunction with non-contrast t2 weighted mri.results: melanoma (pdmr-425362-245-t)table 1: penetrance and location of pathological confirmed metastatic lesion(s).     # animals in group# animals that displayed metastasis in mri and confirmed by pathologypathology confirmation of mri (primary imaging site)other confirmed location (s)mouse id: mri with pathology confirmation of metastasis10 (non-resected)4(6 mice were eu due to xenograft size prior to observation of metastasis)lungkidney1512, 1516, 1518, 152010 (resected)10lungkidney, liver, pancreas1506, 1508, 1509, 1510, 1511, 1515, 1517, 1519, 1521, 1523percent penetrance with respect to the average time-to-metastasis for non-resected (plot a: time from implant) and resected (plot b: time from tumor resection) cohorts.   plot aplot bpet\\\/ct characterization of the primary tumor:\\u00a0 baseline pet (sop attached) were performed when tumor reached an approximate 200 mm3.\\u00a0\\u00a0 average suvmax values (n=5) were calculated; [18f]fdg: 2.7 \\u00b1 0.5 and [18f]flt: 2.0 \\u00b1 0.3.conclusion:melanoma pdmr-425362-245-t model can be challenging due to the rapid growth of the xenograft and regrowth.\\u00a0 metastases was well observed on t2 mri imaging allowing non-invasive evaluation in treatment trials. melanoma abdomen sr mr other protocol clinical pdmr\"},{\"id\":42557,\"type\":\"collection\",\"slug\":\"pdmr-521955-158-r4\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/pdmr-521955-158-r4\\\/\",\"title\":\"Imaging characterization of a metastatic patient derived model of adenocarcinoma pancreas:\",\"short_title\":\"PDMR-521955-158-R4\",\"doi\":\"10.7937\\\/q37d-vh79\",\"summary\":\"Pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist are resource intensive because determination of the presence of metastatic disease, metastatic burden, and response to therapy normally require multiple timed cohorts with animal sacrifice and extensive pathological examination. We identified and characterized a patient derived xenograft model with metastatic potential, adenocarcinoma pancreas xenograft 521955-158-R4. In this study we performed a detailed imaging characterization (workflow below) of this model, which develops spontaneous lung metastases, details are provided in the attached standard operating procedures. Tumors in half of the mice were resected in the range 200-300 mm3 size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.The imaging characteristics of\\u00a0this model (PDMR-521955-158-R4), which is available from the National Cancer Institute Patient-Derived Models\\u00a0Repository (https:\\\/\\\/pdmr.cancer.gov\\\/), is highly favorable for preclinical research studies of\\u00a0metastatic disease when used in conjunction with non-contrast T2 weighted MRI.Results: Adenocarcinoma pancreas (PDMR-521955-158-R4)Table 1: Penetrance and location of pathological confirmed metastatic lesion(s).# animals in Group# animals that displayed metastasis in MRI and confirmed by PathologyPathology confirmation of MRI (primary imaging site)Other confirmed Location (s)Mouse ID: MRI with pathology confirmation of metastasis10 (non-resected)4(5 mice were EU due to xenograft size prior to observation of metastases)LungLung2163, 2169, 2171, 217910 (resected)7LungLung (70%)Para-vertebral muscle (50%) Nodal disease (10%)2160, 2161, 2164, 2170, 2172, 2176, 2178Percent penetrance with respect to the average time-to-metastasis for non-resected (time from implant: 10.7 \\u00b1 1.8 weeks) and resected (time from tumor resection: 5.3 \\u00b1 1.1 weeks) cohorts.PET\\\/CT Characterization of the primary tumor:\\u00a0 Baseline PET (SOP attached) were performed when tumor reached an approximate 200 mm3.\\u00a0 \\u00a0Average SUVmax values (n=6) were calculated; [18F]FDG: 2.3 \\u00b1 0.8 and [18F]FLT: 2.4 \\u00b1 0.4.Conclusion:Good metastatic model with 40% penetrance un-resected and 70% with planned early resection.\\u00a0 Metastases are well observed on T2 MRI imaging allowing non-invasive evaluation in treatment trials.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PDMR-Workflow.png\",\"program\":\"PDMR\",\"cancer_types\":[\"Pancreatic adenocarcinoma\"],\"cancer_locations\":[\"Abdomen\"],\"data_types\":[\"SR\",\"MR\",\"Protocol\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Mouse\"],\"subjects\":20,\"date_updated\":\"2022-08-17\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=PDMR-521955-158-R4\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PDMR-521955-158-R4.tcia\",\"blob\":\"imaging characterization of a metastatic patient derived model of adenocarcinoma pancreas: pdmr-521955-158-r4 pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist are resource intensive because determination of the presence of metastatic disease, metastatic burden, and response to therapy normally require multiple timed cohorts with animal sacrifice and extensive pathological examination. we identified and characterized a patient derived xenograft model with metastatic potential, adenocarcinoma pancreas xenograft 521955-158-r4. in this study we performed a detailed imaging characterization (workflow below) of this model, which develops spontaneous lung metastases, details are provided in the attached standard operating procedures. tumors in half of the mice were resected in the range 200-300 mm3 size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.the imaging characteristics of\\u00a0this model (pdmr-521955-158-r4), which is available from the national cancer institute patient-derived models\\u00a0repository (https:\\\/\\\/pdmr.cancer.gov\\\/), is highly favorable for preclinical research studies of\\u00a0metastatic disease when used in conjunction with non-contrast t2 weighted mri.results: adenocarcinoma pancreas (pdmr-521955-158-r4)table 1: penetrance and location of pathological confirmed metastatic lesion(s).# animals in group# animals that displayed metastasis in mri and confirmed by pathologypathology confirmation of mri (primary imaging site)other confirmed location (s)mouse id: mri with pathology confirmation of metastasis10 (non-resected)4(5 mice were eu due to xenograft size prior to observation of metastases)lunglung2163, 2169, 2171, 217910 (resected)7lunglung (70%)para-vertebral muscle (50%) nodal disease (10%)2160, 2161, 2164, 2170, 2172, 2176, 2178percent penetrance with respect to the average time-to-metastasis for non-resected (time from implant: 10.7 \\u00b1 1.8 weeks) and resected (time from tumor resection: 5.3 \\u00b1 1.1 weeks) cohorts.pet\\\/ct characterization of the primary tumor:\\u00a0 baseline pet (sop attached) were performed when tumor reached an approximate 200 mm3.\\u00a0 \\u00a0average suvmax values (n=6) were calculated; [18f]fdg: 2.3 \\u00b1 0.8 and [18f]flt: 2.4 \\u00b1 0.4.conclusion:good metastatic model with 40% penetrance un-resected and 70% with planned early resection.\\u00a0 metastases are well observed on t2 mri imaging allowing non-invasive evaluation in treatment trials. pancreatic adenocarcinoma abdomen sr mr protocol clinical pdmr\"},{\"id\":42597,\"type\":\"collection\",\"slug\":\"pdmr-833975-119-r\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/pdmr-833975-119-r\\\/\",\"title\":\"Imaging tissue characterization of a patient derived xenograft model of adenocarcinoma pancreas:\",\"short_title\":\"PDMR-833975-119-R\",\"doi\":\"10.7937\\\/TCIA.0ECK-C338\",\"summary\":\"Characterization of tissue using in vivo non-invasive imaging is the foundation of Radiology and is clinically used for detection and measurement of disease burden in oncology.\\u00a0 With the migration of imaging to digital media the possibility for advanced mathematically based imaging biomarkers was realized. As part of this endeavor, researchers have developed various algorithms using neural networks, and classification techniques to improve tissue characterization (morphological changes).\\u00a0 However, large datasets are a requirement in this research endeavor in part due to the genomic heterogeneity of tumors in the same histologic classification \\u2013 many tumors from different patients are required to have enough with the same genomic characteristics to adequately evaluate the range of imaging variability for a specific genomic pattern.\\u00a0 Pre-clinical animal models of patient derived xenografts may be an important resource by providing collections with a more homogenous tumor genome across the collection with companion extensive tumor genomic characterization available, allowing determination of the variability of imaging characteristics for that pattern in different individuals. This dataset of a patient derived xenograft\\u00a0model adenocarcinoma pancreas PDMR: 833975-119-R can be used for training algorithms for evaluating variations in tissue texture with respect to tumor growth and regrowth after surgical resection.\\u00a0\\u00a0In this study we performed a detailed imaging characterization (workflow below) of this model, details are provided in the attached standard operating procedures. Tumors in half of the mice were resected in the range 200-300 mm3 size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.\\u00a0T2w MRI at 56 days post implant demonstrated a heterogenous tumor with an apparent central hemorrhage and the implant appears to have a defined capsule.\\u00a0 On day 98 (post-implant) the tumor increased in size (618%) with very similar imaging characteristics as displayed on day 56.In contrast, the regrowth following surgical resection of the primary xenograft (105 days post resection) is homogenous, but also appears to have a well-defined capsule.PET\\\/CT Characterization of the primary tumor:\\u00a0 Baseline PET (SOP attached) were performed when tumor reached an approximate 200 mm3.\\u00a0\\u00a0 Average\\u00a0SUVbw_max values (n=5) were calculated; [18F]FDG: 1.8 \\u00b1 0.2 and [18F]FLT: 2.8 \\u00b1 0.7.The imaging characteristics of\\u00a0this model, which is available from the National Cancer Institute Patient-Derived Models\\u00a0Repository (https:\\\/\\\/pdmr.cancer.gov\\\/), is highly favorable for preclinical research studies when used in conjunction with non-contrast T2 weighted MRI.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PDMR-833975-119-R.png\",\"program\":\"PDMR\",\"cancer_types\":[\"Pancreatic Ductal Adenocarcinoma\"],\"cancer_locations\":[\"Abdomen\"],\"data_types\":[\"MR\",\"SR\",\"Protocol\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Mouse\"],\"subjects\":20,\"date_updated\":\"2020-10-05\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=PDMR-833975-119-R\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PDMR-833975-119-R-NCI.tcia\",\"blob\":\"imaging tissue characterization of a patient derived xenograft model of adenocarcinoma pancreas: pdmr-833975-119-r characterization of tissue using in vivo non-invasive imaging is the foundation of radiology and is clinically used for detection and measurement of disease burden in oncology.\\u00a0 with the migration of imaging to digital media the possibility for advanced mathematically based imaging biomarkers was realized. as part of this endeavor, researchers have developed various algorithms using neural networks, and classification techniques to improve tissue characterization (morphological changes).\\u00a0 however, large datasets are a requirement in this research endeavor in part due to the genomic heterogeneity of tumors in the same histologic classification \\u2013 many tumors from different patients are required to have enough with the same genomic characteristics to adequately evaluate the range of imaging variability for a specific genomic pattern.\\u00a0 pre-clinical animal models of patient derived xenografts may be an important resource by providing collections with a more homogenous tumor genome across the collection with companion extensive tumor genomic characterization available, allowing determination of the variability of imaging characteristics for that pattern in different individuals. this dataset of a patient derived xenograft\\u00a0model adenocarcinoma pancreas pdmr: 833975-119-r can be used for training algorithms for evaluating variations in tissue texture with respect to tumor growth and regrowth after surgical resection.\\u00a0\\u00a0in this study we performed a detailed imaging characterization (workflow below) of this model, details are provided in the attached standard operating procedures. tumors in half of the mice were resected in the range 200-300 mm3 size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.\\u00a0t2w mri at 56 days post implant demonstrated a heterogenous tumor with an apparent central hemorrhage and the implant appears to have a defined capsule.\\u00a0 on day 98 (post-implant) the tumor increased in size (618%) with very similar imaging characteristics as displayed on day 56.in contrast, the regrowth following surgical resection of the primary xenograft (105 days post resection) is homogenous, but also appears to have a well-defined capsule.pet\\\/ct characterization of the primary tumor:\\u00a0 baseline pet (sop attached) were performed when tumor reached an approximate 200 mm3.\\u00a0\\u00a0 average\\u00a0suvbw_max values (n=5) were calculated; [18f]fdg: 1.8 \\u00b1 0.2 and [18f]flt: 2.8 \\u00b1 0.7.the imaging characteristics of\\u00a0this model, which is available from the national cancer institute patient-derived models\\u00a0repository (https:\\\/\\\/pdmr.cancer.gov\\\/), is highly favorable for preclinical research studies when used in conjunction with non-contrast t2 weighted mri. pancreatic ductal adenocarcinoma abdomen mr sr protocol clinical pdmr\"},{\"id\":42537,\"type\":\"collection\",\"slug\":\"pdmr-997537-175-t\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/pdmr-997537-175-t\\\/\",\"title\":\"Imaging characterization of a metastatic patient derived model of adenocarcinoma colon:\",\"short_title\":\"PDMR-997537-175-T\",\"doi\":\"10.7937\\\/TCIA.2020.BRY9-4N29\",\"summary\":\"Pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist are resource intensive because determination of the presence of metastatic disease, metastatic burden, and response to therapy normally require multiple timed cohorts with animal sacrifice and extensive pathological examination. We identified and characterized a patient derived xenograft model with metastatic potential, adenocarcinoma colon xenograft 997537-175-T. In this study we performed a detailed imaging characterization (workflow below) of this model, which develops spontaneous lung metastases, details are provided in the attached standard operating procedures. Tumors in half of the mice were resected in the range 200-300 mm3 size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.\\u00a0The imaging characteristics of\\u00a0this model (PDMR-997537-175-T), which is available from the National Cancer Institute Patient-Derived Models\\u00a0Repository (https:\\\/\\\/pdmr.cancer.gov\\\/), is highly favorable for preclinical research studies of\\u00a0metastatic disease when used in conjunction with non-contrast T2 weighted MRI.Results: Adenocarcinoma colon (PDMR-997537-175-T)Table 1: Penetrance and location of pathological confirmed metastatic lesion(s).     # animals in group# animals that displayed metastasis in MRI and confirmed by PathologyPathology confirmation of MRI (primary imaging site)Other confirmed Location (s)Mouse ID: MRI with pathology confirmation of metastasis10 (non-resected)6(4 mice were EU due to xenograft size prior to observation of metastasis)LungLymph Node1319, 1321, 1326, 1327, 1334, 133810 (resected)10LungLymph Node1320, 1323, 1324, 1325, 1328, 1329, 1332, 1333, 1335, 1337Percent penetrance with respect to the average time-to-metastasis for non-resected (plot A: time from implant) and resected (plot B: time from tumor resection) cohorts.   Plot APlot BPET\\\/CT Characterization of the primary tumor:\\u00a0 Baseline PET (SOP attached) were performed when tumor reached an approximate 200 mm3.\\u00a0\\u00a0 Average SUVmax values (n=5) were calculated; [18F]FDG: 4.4 \\u00b1 0.6 and [18F]FLT: 1.3 \\u00b1 0.3.Conclusion:Excellent metastatic model with at least 60% penetrance non-resected and 100% with planned resection.\\u00a0 Metastases well observed on T2 MRI imaging allowing non-invasive evaluation in treatment trials.\\u00a0 This model can be challenging in a drug study due to the rapid xenograft tumor growth and re-growth after tumor excision.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PDMR-2.png\",\"program\":\"PDMR\",\"cancer_types\":[\"Adenocarcinoma Colon\"],\"cancer_locations\":[\"Colon\"],\"data_types\":[\"SR\",\"MR\",\"Protocol\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Mouse\"],\"subjects\":24,\"date_updated\":\"2020-05-22\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=PDMR-997537-175-T\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PDMR-997537-175-T.tcia\",\"blob\":\"imaging characterization of a metastatic patient derived model of adenocarcinoma colon: pdmr-997537-175-t pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist are resource intensive because determination of the presence of metastatic disease, metastatic burden, and response to therapy normally require multiple timed cohorts with animal sacrifice and extensive pathological examination. we identified and characterized a patient derived xenograft model with metastatic potential, adenocarcinoma colon xenograft 997537-175-t. in this study we performed a detailed imaging characterization (workflow below) of this model, which develops spontaneous lung metastases, details are provided in the attached standard operating procedures. tumors in half of the mice were resected in the range 200-300 mm3 size; tumors in the other half were allowed to grow until it was necessary to euthanize them because of tumor size.\\u00a0the imaging characteristics of\\u00a0this model (pdmr-997537-175-t), which is available from the national cancer institute patient-derived models\\u00a0repository (https:\\\/\\\/pdmr.cancer.gov\\\/), is highly favorable for preclinical research studies of\\u00a0metastatic disease when used in conjunction with non-contrast t2 weighted mri.results: adenocarcinoma colon (pdmr-997537-175-t)table 1: penetrance and location of pathological confirmed metastatic lesion(s).     # animals in group# animals that displayed metastasis in mri and confirmed by pathologypathology confirmation of mri (primary imaging site)other confirmed location (s)mouse id: mri with pathology confirmation of metastasis10 (non-resected)6(4 mice were eu due to xenograft size prior to observation of metastasis)lunglymph node1319, 1321, 1326, 1327, 1334, 133810 (resected)10lunglymph node1320, 1323, 1324, 1325, 1328, 1329, 1332, 1333, 1335, 1337percent penetrance with respect to the average time-to-metastasis for non-resected (plot a: time from implant) and resected (plot b: time from tumor resection) cohorts.   plot aplot bpet\\\/ct characterization of the primary tumor:\\u00a0 baseline pet (sop attached) were performed when tumor reached an approximate 200 mm3.\\u00a0\\u00a0 average suvmax values (n=5) were calculated; [18f]fdg: 4.4 \\u00b1 0.6 and [18f]flt: 1.3 \\u00b1 0.3.conclusion:excellent metastatic model with at least 60% penetrance non-resected and 100% with planned resection.\\u00a0 metastases well observed on t2 mri imaging allowing non-invasive evaluation in treatment trials.\\u00a0 this model can be challenging in a drug study due to the rapid xenograft tumor growth and re-growth after tumor excision. adenocarcinoma colon colon sr mr protocol clinical pdmr\"},{\"id\":42565,\"type\":\"collection\",\"slug\":\"pdmr-bl0293-f563\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/pdmr-bl0293-f563\\\/\",\"title\":\"Imaging characterization of a metastatic patient derived model of bladder cancer: BL0293F\",\"short_title\":\"PDMR-BL0293-F563\",\"doi\":\"10.7937\\\/tcia.2019.b6u7wmqw\",\"summary\":\"Pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist\\u00a0are resource intensive because determination of the presence of metastatic disease,\\u00a0metastatic burden, and response to therapy normally require animal sacrifice and extensive\\u00a0pathological examination. We recently identified and characterized a patient derived xenograft\\u00a0model with metastatic potential, bladder xenograft BL0293-F563, developed by Jackson\\u00a0Laboratories and the University of California at Davis. In this study we performed a detailed\\u00a0imaging characterization of this model, which develops spontaneous liver and bone\\u00a0metastases. Using non-contrast T2 weighted MRI, hepatic metastases were demonstrated in\\u00a0over 70% of animals at 52 days post tumor implantation without resection of the xenograft and\\u00a0in 100% of animals at day 52 following resection of the xenograft. T2w turbo spin echo (T2wTSE) sequence was applied in the coronal view with a repetition time (TR) 5333ms, echo time\\u00a0(TE) 65ms, with an in-plane pixel of 0.180 \\u00d7 0.180 mm2. A Spectral Presaturation with\\u00a0Inversion Recovery (SPIR) sequence (Philips Healthcare, Best, The Netherlands) was used to\\u00a0suppress the fat component and assist in distinguishing fat from cystic mass and tumor\\u00a0tissue. In a group of animals receiving one cycle of effective chemotherapy ([Temozolomide;\\u00a0(50 mg\\\/kg; PO, QDx5)] plus [(veliparib, a poly (ADP-ribose) polymerase inhibitor) (7.75 mg\\\/kg;\\u00a0PO; BIDx7)]), no animals demonstrated metastasis by imaging. The imaging characteristics of\\u00a0this model, which is available from the National Cancer Institute Patient-Derived Models\\u00a0Repository (https:\\\/\\\/pdmr.cancer.gov\\\/), is highly favorable for preclinical research studies of\\u00a0metastatic disease when used in conjunction with non-contrast T2 weighted MRI.\",\"image\":false,\"program\":\"PDMR\",\"cancer_types\":[\"Bladder Cancer\"],\"cancer_locations\":[\"Bladder\"],\"data_types\":[\"MR\",\"Protocol\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Mouse\"],\"subjects\":19,\"date_updated\":\"2019-08-28\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=PDMR-BL0293-F563\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PDMR-BL0293-F563-manifest-08282019.tcia\",\"blob\":\"imaging characterization of a metastatic patient derived model of bladder cancer: bl0293f pdmr-bl0293-f563 pre-clinical animal models of spontaneous metastatic cancer are infrequent; the few that exist\\u00a0are resource intensive because determination of the presence of metastatic disease,\\u00a0metastatic burden, and response to therapy normally require animal sacrifice and extensive\\u00a0pathological examination. we recently identified and characterized a patient derived xenograft\\u00a0model with metastatic potential, bladder xenograft bl0293-f563, developed by jackson\\u00a0laboratories and the university of california at davis. in this study we performed a detailed\\u00a0imaging characterization of this model, which develops spontaneous liver and bone\\u00a0metastases. using non-contrast t2 weighted mri, hepatic metastases were demonstrated in\\u00a0over 70% of animals at 52 days post tumor implantation without resection of the xenograft and\\u00a0in 100% of animals at day 52 following resection of the xenograft. t2w turbo spin echo (t2wtse) sequence was applied in the coronal view with a repetition time (tr) 5333ms, echo time\\u00a0(te) 65ms, with an in-plane pixel of 0.180 \\u00d7 0.180 mm2. a spectral presaturation with\\u00a0inversion recovery (spir) sequence (philips healthcare, best, the netherlands) was used to\\u00a0suppress the fat component and assist in distinguishing fat from cystic mass and tumor\\u00a0tissue. in a group of animals receiving one cycle of effective chemotherapy ([temozolomide;\\u00a0(50 mg\\\/kg; po, qdx5)] plus [(veliparib, a poly (adp-ribose) polymerase inhibitor) (7.75 mg\\\/kg;\\u00a0po; bidx7)]), no animals demonstrated metastasis by imaging. the imaging characteristics of\\u00a0this model, which is available from the national cancer institute patient-derived models\\u00a0repository (https:\\\/\\\/pdmr.cancer.gov\\\/), is highly favorable for preclinical research studies of\\u00a0metastatic disease when used in conjunction with non-contrast t2 weighted mri. bladder cancer bladder mr protocol clinical pdmr\"},{\"id\":43577,\"type\":\"collection\",\"slug\":\"pdmr-texture-analysis\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/pdmr-texture-analysis\\\/\",\"title\":\"Serial Non-contrast Non-gated T2w MRI Datasets of Patient-derived Xenograft Cancer Models for Development of Tissue Characterization Algorithms\",\"short_title\":\"PDMR-Texture-Analysis\",\"doi\":\"10.7937\\\/3KQ0-YK19\",\"summary\":\"This collection contains serial non-contrast non-gated T2w MRI of 18 patient derived xenograft cancer models. 175 mice were imaged at multiple time points (514 total studies) for researchers to develop algorithms using neural networks, and classification techniques to improve tissue characterization (morphological changes) for the improvement in patient care through advances in precision medicine. \\u00a0Characterization of tissue using non-invasive in vivo imaging techniques is used for detection and measurement of disease burden in oncology. Researchers have developed numerous algorithms, such as neural networks, and classification techniques to improve the characterization (morphological changes) of tissue. Unfortunately, to obtain statistical significance, large datasets are a requirement in this research endeavor due to tumor heterogeneity within the same histologic classification. Pre-clinical patient derived xenograft animal models can be a significant resource by providing collections with a more homogenous tumor genome across the collection with companion genomic and pathologic characterization available (https:\\\/\\\/pdmr.cancer.gov\\\/), allowing determination of the variability of imaging characteristics.This dataset of a patient derived xenograft model (below table) can be used for training algorithms for evaluating variations in tissue texture with respect to tumor growth and cancer model.PDX Model Characterizations and Biweekly imaging sessions\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0Characterization12345678Model \\u00a0IDCTEP SDC DescriptionDisease Body Location\\u00a0 Biopsy siteImplant DatePassageGender# Mice imaged per biweekly imaging session144126-210-TNeuroendocrine cancer, NOSEndocrine and Neuroendocrine* Liver2\\\/14\\\/20204M885555\\u00a0\\u00a0146476-266-RUrothelial\\\/bladder cancer, NOSGenitourinaryBladder2\\\/3\\\/20204M171613101144\\u00a0165739-295-RAdenocarcinoma-pancreasDigestive\\\/GastrointestinalPancreas5\\\/4\\\/20182M10101\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0172845-121-TAdenocarcinoma-colonDigestive\\\/Gastrointestinal* Liver10\\\/16\\\/20204F2020\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0172845-142-TAdenocarcinoma-colonDigestive\\\/Gastrointestinal* Liver8\\\/24\\\/20183F151383\\u00a0\\u00a0\\u00a0\\u00a0287954-098-REwing sarcoma\\\/Peripheral PNETMusculoskeletal* Pelvis3\\\/18\\\/20216M108111\\u00a0\\u00a0\\u00a0466636-057-RAdenocarcinoma-pancreasDigestive\\\/GastrointestinalPancreas12\\\/15\\\/2017N\\\/AM5421\\u00a0\\u00a0\\u00a0\\u00a0521955-158-R4Adenocarcinoma-pancreasDigestive\\\/Gastrointestinal* Tumor in colonic fat9\\\/30\\\/20214F1010108511\\u00a0521955-158-R6Adenocarcinoma-pancreasDigestive\\\/Gastrointestinal* Myometrium3\\\/27\\\/2018N\\\/AF774\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0625472-104-R\\u00a0\\u00a0Adenocarcinoma-colonDigestive\\\/Gastrointestinal* Shoulder8\\\/27\\\/20192F91\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0695669-166-RMelanomaSkinArm4\\\/16\\\/20213M78864422698357-238-ROsteosarcomaMusculoskeletalScapula3\\\/5\\\/20216F74\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0765638-272-RSquamous cell lung carcinomaRespiratory\\\/Thoracic* Liver3\\\/26\\\/20214F78531\\u00a0\\u00a0\\u00a0779769-127-RAdenocarcinoma-rectumDigestive\\\/GastrointestinalRectum2\\\/19\\\/20205F555534\\u00a0\\u00a0833975-119-RAdenocarcinoma-pancreasDigestive\\\/GastrointestinalPancreas10\\\/23\\\/20192F1212117\\u00a0\\u00a0\\u00a0\\u00a0894883-131-RSquamous cell carcinoma-anusDigestive\\\/GastrointestinalButtock2\\\/25\\\/20225F66611\\u00a0\\u00a0\\u00a0997537-175-TAdenocarcinoma-colonDigestive\\\/Gastrointestinal* Liver10\\\/25\\\/20183M92\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0BL0382-F1232Urothelial\\\/bladder cancer, NOSGenitourinaryBladder5\\\/20\\\/20204F96411\\u00a0\\u00a0\\u00a0Note: Biopsy sites labeled with an (*) were obtained from a metastatic site.\\u00a0 All other biopsy sites were at the primary tumor site.In this study we performed non-contrast non-gated T2w MRI (SOP50101_MRI), initiated 2 weeks post implantation, and continued biweekly imaging sessions until their tumors reached a size requiring humane termination (ACUC guidance\\u2009>\\u20092 cm in any linear dimension by caliper or MRI measurement) or their clinical status required euthanasia. Fragments (2x2x2 mm3) from the NCI\\\/DCTD PDMR repository were implanted into 5-10 donor mice (NOD.Cg-PrkdcscidIl2rgtm1Wjl\\\/SzJ (NSG)). When tumors reached enrollment criteria (100 \\u2013 300 mm3), tumors were excised, cut into 2x2x2 mm3\\u00a0fragments and implanted with Matrigel (per PDMR SOP50101_Tumor Implantation) into NSG study mice. The multi-mouse non-gated DICOM dataset was split according to the method published in Tomography and retained their individual mouse DICOM header information.\\u00a0 Structured Reports (SR) were added to the dataset to include fragment implant date, CTEP description, mouse strain (NSG) and model.The genomic and pathologic characteristics of these models, which is available from the National Cancer Institute Patient-Derived Models Repository (https:\\\/\\\/pdmr.cancer.gov\\\/), can be used in conjunction with this publicly available dataset to guide the development of algorithms for enhanced characterization of tissue for precision medicine.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image-2023-6-14_15-18-11-1-1.png\",\"program\":\"PDMR\",\"cancer_types\":[\"Adenocarcinoma of colon\",\"Adenocarcinoma of pancreas\",\"Adenocarcinoma of rectum\",\"Ewing sarcoma - Peripheral PNET\",\"Melanoma\",\"Neuroendocrine cancer (NOS)\",\"Osteosarcoma\",\"Squamous cell carcinoma of anus\",\"Squamous cell carcinoma of lung\",\"Urothelial - bladder cancer (NOS)\"],\"cancer_locations\":[\"Arm\",\"Bladder\",\"Buttock\",\"Colon\",\"Liver\",\"Myometrium\",\"Pancreas\",\"Rectum\",\"Shoulder\",\"Scapula\"],\"data_types\":[\"MR\",\"SR\",\"Protocol\",\"Demographic\",\"Follow-Up\",\"Diagnosis\",\"Other\"],\"supporting_data\":[],\"species\":[\"Mouse\"],\"subjects\":175,\"date_updated\":\"2023-06-14\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=PDMR-Texture-Analysis\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PDMR-Texture-Analysis.tcia\",\"blob\":\"serial non-contrast non-gated t2w mri datasets of patient-derived xenograft cancer models for development of tissue characterization algorithms pdmr-texture-analysis this collection contains serial non-contrast non-gated t2w mri of 18 patient derived xenograft cancer models. 175 mice were imaged at multiple time points (514 total studies) for researchers to develop algorithms using neural networks, and classification techniques to improve tissue characterization (morphological changes) for the improvement in patient care through advances in precision medicine. \\u00a0characterization of tissue using non-invasive in vivo imaging techniques is used for detection and measurement of disease burden in oncology. researchers have developed numerous algorithms, such as neural networks, and classification techniques to improve the characterization (morphological changes) of tissue. unfortunately, to obtain statistical significance, large datasets are a requirement in this research endeavor due to tumor heterogeneity within the same histologic classification. pre-clinical patient derived xenograft animal models can be a significant resource by providing collections with a more homogenous tumor genome across the collection with companion genomic and pathologic characterization available (https:\\\/\\\/pdmr.cancer.gov\\\/), allowing determination of the variability of imaging characteristics.this dataset of a patient derived xenograft model (below table) can be used for training algorithms for evaluating variations in tissue texture with respect to tumor growth and cancer model.pdx model characterizations and biweekly imaging sessions\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0characterization12345678model \\u00a0idctep sdc descriptiondisease body location\\u00a0 biopsy siteimplant datepassagegender# mice imaged per biweekly imaging session144126-210-tneuroendocrine cancer, nosendocrine and neuroendocrine* liver2\\\/14\\\/20204m885555\\u00a0\\u00a0146476-266-rurothelial\\\/bladder cancer, nosgenitourinarybladder2\\\/3\\\/20204m171613101144\\u00a0165739-295-radenocarcinoma-pancreasdigestive\\\/gastrointestinalpancreas5\\\/4\\\/20182m10101\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0172845-121-tadenocarcinoma-colondigestive\\\/gastrointestinal* liver10\\\/16\\\/20204f2020\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0172845-142-tadenocarcinoma-colondigestive\\\/gastrointestinal* liver8\\\/24\\\/20183f151383\\u00a0\\u00a0\\u00a0\\u00a0287954-098-rewing sarcoma\\\/peripheral pnetmusculoskeletal* pelvis3\\\/18\\\/20216m108111\\u00a0\\u00a0\\u00a0466636-057-radenocarcinoma-pancreasdigestive\\\/gastrointestinalpancreas12\\\/15\\\/2017n\\\/am5421\\u00a0\\u00a0\\u00a0\\u00a0521955-158-r4adenocarcinoma-pancreasdigestive\\\/gastrointestinal* tumor in colonic fat9\\\/30\\\/20214f1010108511\\u00a0521955-158-r6adenocarcinoma-pancreasdigestive\\\/gastrointestinal* myometrium3\\\/27\\\/2018n\\\/af774\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0625472-104-r\\u00a0\\u00a0adenocarcinoma-colondigestive\\\/gastrointestinal* shoulder8\\\/27\\\/20192f91\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0695669-166-rmelanomaskinarm4\\\/16\\\/20213m78864422698357-238-rosteosarcomamusculoskeletalscapula3\\\/5\\\/20216f74\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0765638-272-rsquamous cell lung carcinomarespiratory\\\/thoracic* liver3\\\/26\\\/20214f78531\\u00a0\\u00a0\\u00a0779769-127-radenocarcinoma-rectumdigestive\\\/gastrointestinalrectum2\\\/19\\\/20205f555534\\u00a0\\u00a0833975-119-radenocarcinoma-pancreasdigestive\\\/gastrointestinalpancreas10\\\/23\\\/20192f1212117\\u00a0\\u00a0\\u00a0\\u00a0894883-131-rsquamous cell carcinoma-anusdigestive\\\/gastrointestinalbuttock2\\\/25\\\/20225f66611\\u00a0\\u00a0\\u00a0997537-175-tadenocarcinoma-colondigestive\\\/gastrointestinal* liver10\\\/25\\\/20183m92\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0bl0382-f1232urothelial\\\/bladder cancer, nosgenitourinarybladder5\\\/20\\\/20204f96411\\u00a0\\u00a0\\u00a0note: biopsy sites labeled with an (*) were obtained from a metastatic site.\\u00a0 all other biopsy sites were at the primary tumor site.in this study we performed non-contrast non-gated t2w mri (sop50101_mri), initiated 2 weeks post implantation, and continued biweekly imaging sessions until their tumors reached a size requiring humane termination (acuc guidance\\u2009>\\u20092 cm in any linear dimension by caliper or mri measurement) or their clinical status required euthanasia. fragments (2x2x2 mm3) from the nci\\\/dctd pdmr repository were implanted into 5-10 donor mice (nod.cg-prkdcscidil2rgtm1wjl\\\/szj (nsg)). when tumors reached enrollment criteria (100 \\u2013 300 mm3), tumors were excised, cut into 2x2x2 mm3\\u00a0fragments and implanted with matrigel (per pdmr sop50101_tumor implantation) into nsg study mice. the multi-mouse non-gated dicom dataset was split according to the method published in tomography and retained their individual mouse dicom header information.\\u00a0 structured reports (sr) were added to the dataset to include fragment implant date, ctep description, mouse strain (nsg) and model.the genomic and pathologic characteristics of these models, which is available from the national cancer institute patient-derived models repository (https:\\\/\\\/pdmr.cancer.gov\\\/), can be used in conjunction with this publicly available dataset to guide the development of algorithms for enhanced characterization of tissue for precision medicine. adenocarcinoma of colon adenocarcinoma of pancreas adenocarcinoma of rectum ewing sarcoma - peripheral pnet melanoma neuroendocrine cancer (nos) osteosarcoma squamous cell carcinoma of anus squamous cell carcinoma of lung urothelial - bladder cancer (nos) arm bladder buttock colon liver myometrium pancreas rectum shoulder scapula mr sr protocol demographic follow-up diagnosis other pdmr\"},{\"id\":43097,\"type\":\"collection\",\"slug\":\"pediatric-ct-seg\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/pediatric-ct-seg\\\/\",\"title\":\"Pediatric Chest\\\/Abdomen\\\/Pelvic CT Exams with Expert Organ Contours\",\"short_title\":\"Pediatric-CT-SEG\",\"doi\":\"10.7937\\\/TCIA.X0H0-1706\",\"summary\":\"This dataset was collected by a collaboration of researchers from Children\\u2019s Wisconsin, Marquette University, Varian Medical Systems, Medical College of Wisconsin, and Stanford University as part of a project funded by the National Institute of Biomedical Imaging and Bioengineering (U01EB023822) to develop tools for rapid, patient-specific CT organ dose estimation. The collection consists of CT images in DICOM format of 359 pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from three CT scanners. The datasets represent random pediatric cases based upon routine clinical indications. Each dataset contains expert contours of up to twenty-nine structures in DICOM RTSS format.\\u00a0\\u00a0Some datasets are missing structures that are not in the scan range or that, in younger patients, could not be reliably identified. Patient ages range from 5 days to 16 years, with a mean age of 7 and with a near equal distribution of male (180) and female (179) patients. The CT acquisition protocols and reconstruction methods vary across the scanner models and patient sizes, with specifications available in the DICOM headers. This data can be used to develop autosegmentation methods for radiation therapy, CT dosimetry, CT diagnostic algorithms, or other applications. The metadata of each CT image series contains the correct patient age and the height and weight data when available.The native slice thickness for the acquired images was 0.625 mm for the GE scanners and 0.6 mm for the Siemens scanners. \\u00a0Sixty-two datasets were manually contoured at this native slice thickness. \\u00a0However, this process required extensive manual labor and was also challenged by high noise in the thin slices. \\u00a0Therefore, the subsequent 297 datasets were reformatted to 2.0-mm slice thickness using a cubic spline interpolation algorithm prior to contouring. For some datasets, this interpolation caused artifacts in the most inferior or superior slices in the volume. \\u00a0This is a known limitation of this dataset and users may need to disregard these corrupted slices, depending on the intended application.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PED_SEG_CT.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Non-Cancer\"],\"cancer_locations\":[\"Various\"],\"data_types\":[\"CT\",\"RTSTRUCT\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":359,\"date_updated\":\"2022-03-31\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Pediatric-CT-SEG\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Pediatric-CT-SEG-Mar-22-2022-manifest.tcia\",\"blob\":\"pediatric chest\\\/abdomen\\\/pelvic ct exams with expert organ contours pediatric-ct-seg this dataset was collected by a collaboration of researchers from children\\u2019s wisconsin, marquette university, varian medical systems, medical college of wisconsin, and stanford university as part of a project funded by the national institute of biomedical imaging and bioengineering (u01eb023822) to develop tools for rapid, patient-specific ct organ dose estimation. the collection consists of ct images in dicom format of 359 pediatric chest-abdomen-pelvis or abdomen-pelvis exams acquired from three ct scanners. the datasets represent random pediatric cases based upon routine clinical indications. each dataset contains expert contours of up to twenty-nine structures in dicom rtss format.\\u00a0\\u00a0some datasets are missing structures that are not in the scan range or that, in younger patients, could not be reliably identified. patient ages range from 5 days to 16 years, with a mean age of 7 and with a near equal distribution of male (180) and female (179) patients. the ct acquisition protocols and reconstruction methods vary across the scanner models and patient sizes, with specifications available in the dicom headers. this data can be used to develop autosegmentation methods for radiation therapy, ct dosimetry, ct diagnostic algorithms, or other applications. the metadata of each ct image series contains the correct patient age and the height and weight data when available.the native slice thickness for the acquired images was 0.625 mm for the ge scanners and 0.6 mm for the siemens scanners. \\u00a0sixty-two datasets were manually contoured at this native slice thickness. \\u00a0however, this process required extensive manual labor and was also challenged by high noise in the thin slices. \\u00a0therefore, the subsequent 297 datasets were reformatted to 2.0-mm slice thickness using a cubic spline interpolation algorithm prior to contouring. for some datasets, this interpolation caused artifacts in the most inferior or superior slices in the volume. \\u00a0this is a known limitation of this dataset and users may need to disregard these corrupted slices, depending on the intended application.\\u00a0 non-cancer various ct rtstruct community\"},{\"id\":43109,\"type\":\"collection\",\"slug\":\"pelvic-reference-data\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/pelvic-reference-data\\\/\",\"title\":\"Pelvic-Reference-Data\",\"short_title\":\"Pelvic-Reference-Data\",\"doi\":\"10.7937\\\/TCIA.2019.WOSKQ5OO\",\"summary\":\"Purpose: Expert selected landmark points on clinical image pairs provide a basis for rigid registration validation. Using combinatorial rigid registration optimization (CORRO) we provide a statistically characterized reference data set for image registration of the pelvis by estimating the optimal ground truth.Methods: Landmark points for each CT\\\/CBCT image pair for 58 pelvic cases were identified. From the identified landmark pairs, combination subsets of k-number of landmark pairs were generated without repeat, to form a k-set for k=4, 8, &12. An affine registration between the image pairs was calculated for each k-combination set (2,000-8,000,000). The mean and the standard deviation of the registration were used as the final registration for each image pair. Joint entropy was employed to measure and compare the quality of CORRO to commercially available software.Results: An average of 154 (range: 91-212) landmark pairs were selected for each CT\\\/CBCT image pair. The mean standard deviation of the registration output decreased as the k-size increased for all cases.\\u00a0 In general the joint entropy evaluated was found to be lower than results from commercially available software. Of all 58 cases 58.3% of the k=4, 15% of k=8 and 18.3% of k=12 resulted in the better registration using CORRO as compared to 8.3% from a commercial registration software. The minimum joint entropy was determined for one case and found to exist at the estimated registration mean in agreement with the CORRO approach.Conclusion: The results demonstrate that CORRO works even in the extreme case of the pelvic anatomy where the CBCT suffers from reduced quality due to increased noise levels. The estimated ground truth using CORRO was found to be better than commercially available software for all k-sets tested. Additionally, the k-set of 4 resulted in overall best outcomes when compared to k=8 and 12, which is anticipated because k=8 and 12 are more likely to have combinations that affected the accuracy of the registration.\\n\\nFigure 1. Content of planning CT shifted to the machine isocenter. Top left to right - axial, coronal and sagittal planning CT image with machine isocenter in red and image isocenter in blue. Middle left to right - axial, coronal and sagittal planning CT at machine isocenter. Bottom left to right - axial, coronal and sagittal CBCT at machine isocenter.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image2019-10-1_12-43-49.png\",\"program\":\"Community\",\"cancer_types\":[\"Prostate\",\"Anal\"],\"cancer_locations\":[\"Pelvis\",\"Prostate\",\"Anus\"],\"data_types\":[\"CT\",\"Other\",\"Measurement\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":58,\"date_updated\":\"2019-09-13\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Pelvic-Reference-Data\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA-Pelvic-Reference-Data.tcia\",\"blob\":\"pelvic-reference-data pelvic-reference-data purpose: expert selected landmark points on clinical image pairs provide a basis for rigid registration validation. using combinatorial rigid registration optimization (corro) we provide a statistically characterized reference data set for image registration of the pelvis by estimating the optimal ground truth.methods: landmark points for each ct\\\/cbct image pair for 58 pelvic cases were identified. from the identified landmark pairs, combination subsets of k-number of landmark pairs were generated without repeat, to form a k-set for k=4, 8, &12. an affine registration between the image pairs was calculated for each k-combination set (2,000-8,000,000). the mean and the standard deviation of the registration were used as the final registration for each image pair. joint entropy was employed to measure and compare the quality of corro to commercially available software.results: an average of 154 (range: 91-212) landmark pairs were selected for each ct\\\/cbct image pair. the mean standard deviation of the registration output decreased as the k-size increased for all cases.\\u00a0 in general the joint entropy evaluated was found to be lower than results from commercially available software. of all 58 cases 58.3% of the k=4, 15% of k=8 and 18.3% of k=12 resulted in the better registration using corro as compared to 8.3% from a commercial registration software. the minimum joint entropy was determined for one case and found to exist at the estimated registration mean in agreement with the corro approach.conclusion: the results demonstrate that corro works even in the extreme case of the pelvic anatomy where the cbct suffers from reduced quality due to increased noise levels. the estimated ground truth using corro was found to be better than commercially available software for all k-sets tested. additionally, the k-set of 4 resulted in overall best outcomes when compared to k=8 and 12, which is anticipated because k=8 and 12 are more likely to have combinations that affected the accuracy of the registration.\\n\\nfigure 1. content of planning ct shifted to the machine isocenter. top left to right - axial, coronal and sagittal planning ct image with machine isocenter in red and image isocenter in blue. middle left to right - axial, coronal and sagittal planning ct at machine isocenter. bottom left to right - axial, coronal and sagittal cbct at machine isocenter. prostate anal pelvis prostate anus ct other measurement image analyses community\"},{\"id\":43125,\"type\":\"collection\",\"slug\":\"phantom-fda\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/phantom-fda\\\/\",\"title\":\"Phantom FDA\",\"short_title\":\"Phantom FDA\",\"doi\":\"10.7937\\\/k9\\\/TCIA.2015.orbjkmux\",\"summary\":\"As part of a more general effort to probe the interrelated factors impacting the accuracy and precision of lung nodule size estimation, we have been conducting phantom CT studies with an anthropomorphic thoracic phantom containing a vasculature insert on which synthetic nodules were inserted or attached.\\nThe utilization of synthetic nodules with known truth regarding size and location allows for bias and variance analysis, enabled by the acquisition of repeat CT scans.\\u00a0 Using a factorial approach to probe imaging parameters (acquisition and reconstruction) and nodule characteristics (size, density, shape, location), ten repeat scans have been collected for each protocol and nodule layout.\\u00a0 The resulting database of CT scans is incrementally becoming available to the public via The Cancer Imaging Archive (TCIA) to facilitate the assessment of lung nodule size estimation methodologies and the development of image analysis software among other possible applications.\\u00a0\",\"image\":false,\"program\":\"FDA\",\"cancer_types\":[\"Phantom\"],\"cancer_locations\":[\"Lung Phantom\"],\"data_types\":[\"CT\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":7,\"date_updated\":\"2017-08-30\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=Phantom%20FDA\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Phantom_FDAv2-doiJNLP-FIR0jVwr.tcia\",\"blob\":\"phantom fda phantom fda as part of a more general effort to probe the interrelated factors impacting the accuracy and precision of lung nodule size estimation, we have been conducting phantom ct studies with an anthropomorphic thoracic phantom containing a vasculature insert on which synthetic nodules were inserted or attached.\\nthe utilization of synthetic nodules with known truth regarding size and location allows for bias and variance analysis, enabled by the acquisition of repeat ct scans.\\u00a0 using a factorial approach to probe imaging parameters (acquisition and reconstruction) and nodule characteristics (size, density, shape, location), ten repeat scans have been collected for each protocol and nodule layout.\\u00a0 the resulting database of ct scans is incrementally becoming available to the public via the cancer imaging archive (tcia) to facilitate the assessment of lung nodule size estimation methodologies and the development of image analysis software among other possible applications.\\u00a0 phantom lung phantom ct other fda\"},{\"id\":41503,\"type\":\"collection\",\"slug\":\"post-nat-brca\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/post-nat-brca\\\/\",\"title\":\"Assessment of Residual Breast Cancer Cellularity after Neoadjuvant Chemotherapy using Digital Pathology\",\"short_title\":\"Post-NAT-BRCA\",\"doi\":\"10.7937\\\/TCIA.2019.4YIBTJNO\",\"summary\":\"Breast cancer (BC) is the second most commonly diagnosed cancer in the U.S. with more than 250,000 new cases of invasive breast cancers reported in 2017. The majority of women with locally advanced and a subset of patients with operable breast cancer will undergo systemic therapy prior to their surgery (neoadjuvant therapy\\\/ NAT) to reduce the size of tumor(s) and possibly further undergo breast conserving surgery. The Post-NAT-BRCA dataset is a collection of representative sections from breast resections in patients with residual invasive BC following NAT. Histologic sections were prepared and digitized to produce high resolution, microscopic images of treated BC tumors. Also included, are clinical features and expert pathology annotations of tumor cellularity and cell types. The Residual Cancer Burden Index (RCBi), is a clinically validated tool for assessment of response to NAT associated with prognosis. Tumor cellularity is one of the parameters used for calculating the RCBi. In this dataset, tumor cellularity refers to a measure of residual disease after NAT, in the form of proportion of malignant tumor inside the tumor bed region; also annotated. (See MD Anderson RCB Calculator for a detailed description of tumor cellularity.) Malignant, healthy, lymphocyte and other labels were also provided for individual cells to aid development of cell segmentation algorithms.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"Histopathology\",\"Demographic\",\"Follow-Up\",\"Molecular Test\",\"Treatment\",\"Diagnosis\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":64,\"date_updated\":\"2019-10-01\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=\\\/system\\\/files\\\/collectionmetadata\\\/202405\\\/Post-NAT-BRCA_03-01-2024.json\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjU2MCIsInBhc3Njb2RlIjoiYzVjMTEwY2YwZDE1NTU4YTgzNTQ3ZTQ0NjdmYzUyNzczMjMyYTU5MCIsInBhY2thZ2VfaWQiOiI1NjAiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"assessment of residual breast cancer cellularity after neoadjuvant chemotherapy using digital pathology post-nat-brca breast cancer (bc) is the second most commonly diagnosed cancer in the u.s. with more than 250,000 new cases of invasive breast cancers reported in 2017. the majority of women with locally advanced and a subset of patients with operable breast cancer will undergo systemic therapy prior to their surgery (neoadjuvant therapy\\\/ nat) to reduce the size of tumor(s) and possibly further undergo breast conserving surgery. the post-nat-brca dataset is a collection of representative sections from breast resections in patients with residual invasive bc following nat. histologic sections were prepared and digitized to produce high resolution, microscopic images of treated bc tumors. also included, are clinical features and expert pathology annotations of tumor cellularity and cell types. the residual cancer burden index (rcbi), is a clinically validated tool for assessment of response to nat associated with prognosis. tumor cellularity is one of the parameters used for calculating the rcbi. in this dataset, tumor cellularity refers to a measure of residual disease after nat, in the form of proportion of malignant tumor inside the tumor bed region; also annotated. (see md anderson rcb calculator for a detailed description of tumor cellularity.) malignant, healthy, lymphocyte and other labels were also provided for individual cells to aid development of cell segmentation algorithms. breast cancer breast histopathology demographic follow-up molecular test treatment diagnosis community\"},{\"id\":46383,\"type\":\"collection\",\"slug\":\"pretreat-metstobrain-masks\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/pretreat-metstobrain-masks\\\/\",\"title\":\"A Large Open Access Dataset of Brain Metastasis 3D Segmentations on MRI with Clinical and Imaging Feature Information\",\"short_title\":\"Pretreat-MetsToBrain-Masks\",\"doi\":\"10.7937\\\/6be1-r748\",\"summary\":\"Cohort Selection:Patient images were collected from three sources: the Yale New Haven Health database (2013-2021), the Yale tumor board registry (2021), and the Yale gamma knife registry (2017-2021).Inclusion\\\/exclusion criteria:Inclusion criteria included a pathologically proven diagnosis of brain metastasis and availability of a pretreatment scan with standard MRI sequences (T1 w, T1 post-gadolinium, T2w, and FLAIR). Exclusion criteria included lack of pretreatment scan or one of the standard MR sequences and significant motion artifact in any of the standard sequences.Imaging acquisition\\\/parameters:Images were acquired using a 1.5T or 3T scanner. Most patients had T1 post-gadolinium sequences acquired using the MPRAGE protocol with a small subset having only spin echoes. Segmentations of core and necrotic tumor components were performed on T1 post-gadolinium sequences while whole tumor was segmented on FLAIR sequences.Segmentation protocol:All segmentations were performed manually on DICOM images in research PACS using a volumetric tool after transfer from clinical PACS.\\u00a0 All segmentations were approved by two neuroradiologists.Image preprocessing:The four standard sequences for each patient were exported from research PACS into NifTI format, co-registered to the SRl24 anatomical template, resampled to a uniform isotropic resolution (1 mm3), and skull stripped. Core, necrosis, and whole segmentation masks were exported individually from research PACS into NifTI format, combined into one mask while retaining their positions in native space, and registered to the SRl24 anatomical template before resampling to a uniform isotropic resolution. The sequence and segmentation NifTI files were manually checked and corrected by a neuroradiologist on the ITK-SNAP software.Clinical and imaging data collection:Demographic data, including sex, ethnicity, age at diagnosis, smoking pack-year history, and presence of extranodal metastasis were obtained using the electronic medical record (EMR). Survival was calculated by subtracting the date of diagnosis from the date of death or from the date of last EMR note for censored patients. Qualitative imaging features, including infratentorial involvement and intratumoral susceptibility in at least one lesion on SWI sequence, were obtained from visual assessment of images. Quantitative imaging features were extracted from the NifTI segmentation mask for each patient and included total enhancing tumor volume, total necrotic tumor volume, total peritumoral edema volume, ratio of necrotic to enhancing volume, ratio of peritumoral edema to enhancing volume, number of enhancing lesions, number of necrotic lesions, and number of lesions with peritumoral edema. \\u00a0Finally, origin of metastasis was also obtained from the EMR from previous oncological and\\\/or pathological reports. Dates of birth, diagnosis, death, and last note were all anonymized preserving duration between events. Clinical and imaging feature information are stored in an Excel file.\\u00a0Currently, many published brain metastasis segmentation algorithms have only been trained and validated on single institution datasets, leading to poor model generalizability. The availability of public datasets is important for algorithm generalizability and implementation into clinical practice. Our dataset is unique in its inclusion of many sub centimeter brain metastases, for which there are yet no robust algorithms. In addition, the dataset has many lesions with necrotic segmentations, which are historically not provided with dataset segmentations. We also include survival outcomes manually obtained from the EMR, allowing for future correlation of our imaging data with clinical outcomes. In addition, the heterogeneity of sources from which we obtained our patient data will allow for algorithms to be trained on a real-world hospital dataset that is not highly curated, thus enhancing model applicability to clinical practice.\\u00a0\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\",\"Small Cell Lung Cancer\",\"Non-small Cell Lung Cancer\",\"Melanoma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"Segmentation\",\"Demographic\",\"Diagnosis\",\"Measurement\",\"Follow-Up\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":200,\"date_updated\":\"2023-12-19\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc0NiIsInBhc3Njb2RlIjoiZmQzZGRmYjA1YjA1ZjNhMTBlZDExMmY4NTJhM2Y0YTlmMzBlODY1MSIsInBhY2thZ2VfaWQiOiI3NDYiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"a large open access dataset of brain metastasis 3d segmentations on mri with clinical and imaging feature information pretreat-metstobrain-masks cohort selection:patient images were collected from three sources: the yale new haven health database (2013-2021), the yale tumor board registry (2021), and the yale gamma knife registry (2017-2021).inclusion\\\/exclusion criteria:inclusion criteria included a pathologically proven diagnosis of brain metastasis and availability of a pretreatment scan with standard mri sequences (t1 w, t1 post-gadolinium, t2w, and flair). exclusion criteria included lack of pretreatment scan or one of the standard mr sequences and significant motion artifact in any of the standard sequences.imaging acquisition\\\/parameters:images were acquired using a 1.5t or 3t scanner. most patients had t1 post-gadolinium sequences acquired using the mprage protocol with a small subset having only spin echoes. segmentations of core and necrotic tumor components were performed on t1 post-gadolinium sequences while whole tumor was segmented on flair sequences.segmentation protocol:all segmentations were performed manually on dicom images in research pacs using a volumetric tool after transfer from clinical pacs.\\u00a0 all segmentations were approved by two neuroradiologists.image preprocessing:the four standard sequences for each patient were exported from research pacs into nifti format, co-registered to the srl24 anatomical template, resampled to a uniform isotropic resolution (1 mm3), and skull stripped. core, necrosis, and whole segmentation masks were exported individually from research pacs into nifti format, combined into one mask while retaining their positions in native space, and registered to the srl24 anatomical template before resampling to a uniform isotropic resolution. the sequence and segmentation nifti files were manually checked and corrected by a neuroradiologist on the itk-snap software.clinical and imaging data collection:demographic data, including sex, ethnicity, age at diagnosis, smoking pack-year history, and presence of extranodal metastasis were obtained using the electronic medical record (emr). survival was calculated by subtracting the date of diagnosis from the date of death or from the date of last emr note for censored patients. qualitative imaging features, including infratentorial involvement and intratumoral susceptibility in at least one lesion on swi sequence, were obtained from visual assessment of images. quantitative imaging features were extracted from the nifti segmentation mask for each patient and included total enhancing tumor volume, total necrotic tumor volume, total peritumoral edema volume, ratio of necrotic to enhancing volume, ratio of peritumoral edema to enhancing volume, number of enhancing lesions, number of necrotic lesions, and number of lesions with peritumoral edema. \\u00a0finally, origin of metastasis was also obtained from the emr from previous oncological and\\\/or pathological reports. dates of birth, diagnosis, death, and last note were all anonymized preserving duration between events. clinical and imaging feature information are stored in an excel file.\\u00a0currently, many published brain metastasis segmentation algorithms have only been trained and validated on single institution datasets, leading to poor model generalizability. the availability of public datasets is important for algorithm generalizability and implementation into clinical practice. our dataset is unique in its inclusion of many sub centimeter brain metastases, for which there are yet no robust algorithms. in addition, the dataset has many lesions with necrotic segmentations, which are historically not provided with dataset segmentations. we also include survival outcomes manually obtained from the emr, allowing for future correlation of our imaging data with clinical outcomes. in addition, the heterogeneity of sources from which we obtained our patient data will allow for algorithms to be trained on a real-world hospital dataset that is not highly curated, thus enhancing model applicability to clinical practice.\\u00a0 breast cancer small cell lung cancer non-small cell lung cancer melanoma brain mr segmentation demographic diagnosis measurement follow-up clinical image analyses software\\\/source code community\"},{\"id\":43161,\"type\":\"collection\",\"slug\":\"prostate-3t\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/prostate-3t\\\/\",\"title\":\"Prostate-3T\",\"short_title\":\"Prostate-3T\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.QJTV5IL5\",\"summary\":\"Prostate transversal T2-weighted magnetic resonance images (MRIs) acquired on a 3.0T Siemens TrioTim using only a pelvic phased-array coil were acquired for prostate cancer detection. The data was provided to TCIA as part of an ISBI challenge competition in 2013.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Prostate Cancer\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"MR\",\"Segmentation\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":64,\"date_updated\":\"2013-05-31\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=Prostate-3T\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_Prostate-3T_06-22-2015.tcia\",\"blob\":\"prostate-3t prostate-3t prostate transversal t2-weighted magnetic resonance images (mris) acquired on a 3.0t siemens triotim using only a pelvic phased-array coil were acquired for prostate cancer detection. the data was provided to tcia as part of an isbi challenge competition in 2013. prostate cancer prostate mr segmentation image analyses community\"},{\"id\":43699,\"type\":\"collection\",\"slug\":\"prostate-anatomical-edge-cases\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/prostate-anatomical-edge-cases\\\/\",\"title\":\"Stress-Testing Pelvic Autosegmentation Algorithms Using Anatomical Edge Cases\",\"short_title\":\"Prostate-Anatomical-Edge-Cases\",\"doi\":\"10.7937\\\/QSTF-ST65\",\"summary\":\"In this single institution retrospective study, we reviewed 950 consecutive patients with prostate adenocarcinoma receiving definitive radiotherapy between 2011 and 2019, and identified among them 112 patients with anatomic variations (edge cases) seen on simulation CT and\\\/or MRI imaging. These variations included hip arthroplasty, prostate median lobe hypertrophy, so-called \\u201cdroopy\\u201d seminal vesicles, presence of a urinary catheter, and others. A separate cohort of 19 \\u201cnormal\\u201d cases were randomly selected for inclusion. Prostate, rectum, bladder, and bilateral femoral heads were manually segmented on all CT simulation images (where present) and were ultimately used clinically for radiation treatment planning.We leveraged this imaging dataset to assess the comparative performance of deep learning, atlas-based, and model-based autosegmentation methods across both normal and edge case cohorts: https:\\\/\\\/doi.org\\\/10.1016\\\/j.phro.2023.100413.\\u00a0 In this paper and in the figure on the right, we show the Cross-sectional CT-based anatomy and autosegmentation performance for representative edge cases.A) Hypertrophic prostate edge case. Each panel depicts a focused excerpt from a single CT scan, centered about two different structures (prostate, bladder) in three different planes (axial, sagittal, coronal). Clinician-delineated \\u201cground truth\\u201d contours (MD) for each structure are shown in red, while atlas-based (AB), model-based (MB), and deep-learning based (DL) autosegmented contours are depicted in green, orange, and blue, respectively. Numerical values represent DSC for the corresponding autosegmented volumes compared to MD volumes.B) So-called \\u201cdroopy\\u201d seminal vesicles edge case. Each panel depicts a focused excerpt from a single CT scan, centered about the prostate in two different planes (axial, sagittal). All colors and labeling are as in Panel A).\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/gr2_lrg.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Prostate Cancer\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"CT\",\"RTSTRUCT\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":131,\"date_updated\":\"2023-05-18\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Prostate-Anatomical-Edge-Cases\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Prostate-Anatomical-Edge-Cases-May-2023-manifest.tcia\",\"blob\":\"stress-testing pelvic autosegmentation algorithms using anatomical edge cases prostate-anatomical-edge-cases in this single institution retrospective study, we reviewed 950 consecutive patients with prostate adenocarcinoma receiving definitive radiotherapy between 2011 and 2019, and identified among them 112 patients with anatomic variations (edge cases) seen on simulation ct and\\\/or mri imaging. these variations included hip arthroplasty, prostate median lobe hypertrophy, so-called \\u201cdroopy\\u201d seminal vesicles, presence of a urinary catheter, and others. a separate cohort of 19 \\u201cnormal\\u201d cases were randomly selected for inclusion. prostate, rectum, bladder, and bilateral femoral heads were manually segmented on all ct simulation images (where present) and were ultimately used clinically for radiation treatment planning.we leveraged this imaging dataset to assess the comparative performance of deep learning, atlas-based, and model-based autosegmentation methods across both normal and edge case cohorts: https:\\\/\\\/doi.org\\\/10.1016\\\/j.phro.2023.100413.\\u00a0 in this paper and in the figure on the right, we show the cross-sectional ct-based anatomy and autosegmentation performance for representative edge cases.a) hypertrophic prostate edge case. each panel depicts a focused excerpt from a single ct scan, centered about two different structures (prostate, bladder) in three different planes (axial, sagittal, coronal). clinician-delineated \\u201cground truth\\u201d contours (md) for each structure are shown in red, while atlas-based (ab), model-based (mb), and deep-learning based (dl) autosegmented contours are depicted in green, orange, and blue, respectively. numerical values represent dsc for the corresponding autosegmented volumes compared to md volumes.b) so-called \\u201cdroopy\\u201d seminal vesicles edge case. each panel depicts a focused excerpt from a single ct scan, centered about the prostate in two different planes (axial, sagittal). all colors and labeling are as in panel a). prostate cancer prostate ct rtstruct community\"},{\"id\":43187,\"type\":\"collection\",\"slug\":\"prostate-diagnosis\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/prostate-diagnosis\\\/\",\"title\":\"PROSTATE-DIAGNOSIS\",\"short_title\":\"PROSTATE-DIAGNOSIS\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.FOQEUJVT\",\"summary\":\"Prostate cancer T1- and T2-weighted magnetic resonance images (MRIs) were acquired on a 1.5 T Philips Achieva by combined surface and endorectal coil, including dynamic contrast-enhanced images obtained prior to, during and after I.V. administration of 0.1 mmol\\\/kg body weight of Gadolinium-DTPA (pentetic acid). Corresponding clinical metadata (XLS format) and 3D segmentation files (NRRD format) are offered as a supplement to this image collection. \\u00a0The XLS file contains pathology biopsy and excised gland tissue reports and the MRI radiology report for most subjects. \\u00a0The Multi-component NRRD Segmentations allow visualization and downstream analysis in 3D Slicer of the following prostate components: prostate gland boundary; internal capsule; central gland, peripheral zone; seminal vesicles; urethra; cancer \\u2013 dominant nodule; neurovascular bundle; penile bulb; ejaculatory duct; veru-montanum; and rectum. See our tutorial on Using 3D Slicer with the Prostate-Diagnosis data if you are not familiar with using this kind of data.The Seminal vesicles (SV) and neurovascular bundle (NVB) Segmentations delineate the neurovascular bundle and seminal vessicles as MHA files. These were provided as part of a planned challenge competition that did not materialize.The Third Party Analysis dataset mentioned beneath the Data Access table was added later as part of the NCI-ISBI 2013 Challenge - Automated Segmentation of Prostate Structures. It includes segmentations for 30 Prostate-Diagnosis subjects in NRRD format which mark the boundaries of the central gland and peripheral zone were also provided\\u00a0\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Prostate Cancer\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"MR\",\"Treatment\",\"Measurement\",\"Classification\",\"Segmentation\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":92,\"date_updated\":\"2021-08-09\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=PROSTATE-DIAGNOSIS\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_PROSTATE-DIAGNOSIS_06-22-2015.tcia\",\"blob\":\"prostate-diagnosis prostate-diagnosis prostate cancer t1- and t2-weighted magnetic resonance images (mris) were acquired on a 1.5 t philips achieva by combined surface and endorectal coil, including dynamic contrast-enhanced images obtained prior to, during and after i.v. administration of 0.1 mmol\\\/kg body weight of gadolinium-dtpa (pentetic acid). corresponding clinical metadata (xls format) and 3d segmentation files (nrrd format) are offered as a supplement to this image collection. \\u00a0the xls file contains pathology biopsy and excised gland tissue reports and the mri radiology report for most subjects. \\u00a0the multi-component nrrd segmentations allow visualization and downstream analysis in 3d slicer of the following prostate components: prostate gland boundary; internal capsule; central gland, peripheral zone; seminal vesicles; urethra; cancer \\u2013 dominant nodule; neurovascular bundle; penile bulb; ejaculatory duct; veru-montanum; and rectum. see our tutorial on using 3d slicer with the prostate-diagnosis data if you are not familiar with using this kind of data.the seminal vesicles (sv) and neurovascular bundle (nvb) segmentations delineate the neurovascular bundle and seminal vessicles as mha files. these were provided as part of a planned challenge competition that did not materialize.the third party analysis dataset mentioned beneath the data access table was added later as part of the nci-isbi 2013 challenge - automated segmentation of prostate structures. it includes segmentations for 30 prostate-diagnosis subjects in nrrd format which mark the boundaries of the central gland and peripheral zone were also provided\\u00a0 prostate cancer prostate mr treatment measurement classification segmentation clinical image analyses community\"},{\"id\":42305,\"type\":\"collection\",\"slug\":\"prostate-fused-mri-pathology\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/prostate-fused-mri-pathology\\\/\",\"title\":\"Fused Radiology-Pathology Prostate Dataset\",\"short_title\":\"Prostate Fused-MRI-Pathology\",\"doi\":\"10.7937\\\/k9\\\/TCIA.2016.tlpmr1am\",\"summary\":\"This collection comprises a total of 28 3 Tesla T1-weighted, T2-weighted, Diffusion weighted and Dynamic Contrast Enhanced prostate MRI along with accompanying digitized histopathology (H&E stained) images of corresponding radical prostatectomy specimens. The MRI scans also have a mapping of extent of prostate cancer on them [10.1002\\\/jmri.24975]. Each surgically excised prostate specimen was originally sectioned and quartered resulting in 4 slides for each section. Each of these individual slides was digitized at 20x magnification using an Aperio slide scanner resulting in a set of 4 .svs images. Each of the 4 .svs images were then digitally stitched together to constitute a pseudo-whole mount section (.tiff) using the program in [PMCID: PMC4023035]. Annotations of cancer presence on the pseudo-whole mount sections were made by an expert pathologist. Slice correspondences were established between the individual T2w MRI and stitched pseudo-whole mount sections by the program in [10.1016\\\/j.compmedimag.2010.12.003] and checked for accuracy by an expert pathologist and radiologist. Deformable co-registration [PMC3078156] was employed to spatially co-registered the corresponding radiologic and histopathologic tissue sections to map disease extent onto the corresponding MRI scans.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Prostate Cancer\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"MR\",\"Histopathology\",\"Other\",\"Measurement\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":28,\"date_updated\":\"2023-04-10\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=Prostate%20Fused-MRI-Pathology\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/doiJNLP-auLIj9wT-Prostate_Fused-MRI-Pathologyv1.tcia\",\"blob\":\"fused radiology-pathology prostate dataset prostate fused-mri-pathology this collection comprises a total of 28 3 tesla t1-weighted, t2-weighted, diffusion weighted and dynamic contrast enhanced prostate mri along with accompanying digitized histopathology (h&e stained) images of corresponding radical prostatectomy specimens. the mri scans also have a mapping of extent of prostate cancer on them [10.1002\\\/jmri.24975]. each surgically excised prostate specimen was originally sectioned and quartered resulting in 4 slides for each section. each of these individual slides was digitized at 20x magnification using an aperio slide scanner resulting in a set of 4 .svs images. each of the 4 .svs images were then digitally stitched together to constitute a pseudo-whole mount section (.tiff) using the program in [pmcid: pmc4023035]. annotations of cancer presence on the pseudo-whole mount sections were made by an expert pathologist. slice correspondences were established between the individual t2w mri and stitched pseudo-whole mount sections by the program in [10.1016\\\/j.compmedimag.2010.12.003] and checked for accuracy by an expert pathologist and radiologist. deformable co-registration [pmc3078156] was employed to spatially co-registered the corresponding radiologic and histopathologic tissue sections to map disease extent onto the corresponding mri scans. prostate cancer prostate mr histopathology other measurement image analyses community\"},{\"id\":43195,\"type\":\"collection\",\"slug\":\"prostate-mri\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/prostate-mri\\\/\",\"title\":\"PROSTATE-MRI\",\"short_title\":\"PROSTATE-MRI\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.6046GUDv\",\"summary\":\"This collection of prostate Magnetic Resonance Images (MRIs) was obtained with an endorectal and phased array surface coil at 3T (Philips Achieva). Each patient had biopsy confirmation of cancer and underwent a robotic-assisted radical prostatectomy. A mold was generated from each MRI, and the prostatectomy specimen was first placed in the mold, then cut in the same plane as the MRI. The data was generated at the National Cancer Institute, Bethesda, Maryland, USA between 2008-2010.\\u00a0For scientific or other inquiries relating to this data set, please contact TCIA's Helpdesk.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Prostate Cancer\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"MR\",\"Histopathology\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":26,\"date_updated\":\"2011-06-30\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=PROSTATE-MRI\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PROSTATE-MRI-5-18-2018-doiJNLP-dKJJAqnS.tcia\",\"blob\":\"prostate-mri prostate-mri this collection of prostate magnetic resonance images (mris) was obtained with an endorectal and phased array surface coil at 3t (philips achieva). each patient had biopsy confirmation of cancer and underwent a robotic-assisted radical prostatectomy. a mold was generated from each mri, and the prostatectomy specimen was first placed in the mold, then cut in the same plane as the mri. the data was generated at the national cancer institute, bethesda, maryland, usa between 2008-2010.\\u00a0for scientific or other inquiries relating to this data set, please contact tcia's helpdesk. prostate cancer prostate mr histopathology community\"},{\"id\":43223,\"type\":\"collection\",\"slug\":\"prostate-mri-us-biopsy\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/prostate-mri-us-biopsy\\\/\",\"title\":\"Prostate MRI and Ultrasound With Pathology and Coordinates of Tracked Biopsy\",\"short_title\":\"Prostate-MRI-US-Biopsy\",\"doi\":\"10.7937\\\/TCIA.2020.A61IOC1A\",\"summary\":\"This dataset was derived from tracked biopsy sessions using the Artemis biopsy system, many of which included image fusion with MRI targets. Patients received a 3D transrectal ultrasound scan, after which nonrigid registration (e.g. \\u201cfusion\\u201d) was performed between real-time ultrasound and preoperative MRI, enabling biopsy cores to be sampled from MR regions of interest.\\u00a0Most cases also included sampling of systematic biopsy cores using a 12-core digital template.\\u00a0The Artemis system tracked targeted and systematic core locations using encoder kinematics of a mechanical arm, and recorded locations relative to the Ultrasound scan. MRI biopsy coordinates were also recorded for most cases.\\u00a0STL files and biopsy overlays are available and can be visualized in\\u00a03D Slicer\\u00a0with the\\u00a0SlicerHeart extension. Spreadsheets summarizing biopsy and MR target data are also available.\\u00a0See the Detailed Description tab below for more information.MRI targets were defined using multiparametric MRI, e.g. t2-weighted, diffusion-weighted, and perfusion-weighted sequences, and scored on a Likert-like scale with close correspondence to PIRADS version 2. t2-weighted MRI was used to trace ROI contours,\\u00a0and is the only sequence provided in this dataset. MR imaging was performed on a 3 Tesla Trio, Verio or Skyra scanner (Siemens, Erlangen, Germany). A transabdominal phased array was used in all cases, and an endorectal coil was used in a subset of cases. The majority of pulse sequences are 3D T2:SPC, with TR\\\/TE 2200\\\/203, Matrix\\\/FOV 256 \\u00d7 205\\\/14 \\u00d7 14 cm, and 1.5mm slice spacing. Some cases were instead 3D T2:TSE with TR\\\/TE 3800\\u20135040\\\/101, and a small minority were imported from other institutions (various T2 protocols.)Ultrasound scans were performed with Hitachi Hi-Vision 5500 7.5 MHz or the Noblus C41V 2-10 MHz end-fire probe. 3D scans were acquired by rotation of the end-fire probe 200 degrees about its axis, and interpolating to resample the volume with isotropic resolution.Patients with suspicion of prostate cancer due to elevated PSA and\\\/or suspicious imaging findings were consecutively accrued. Any consented patient who underwent or had planned to receive a routine, standard-of-care prostate biopsy at the UCLA Clark Urology Center was included.Note: Some Private Tags in this collection are critical to properly displaying the STL surface and the Prostate anatomy. Private Tag (1129,\\\"Eigen, Inc\\\",1016) DS VoxelSize is especially important for multi-frame US cases.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Prostate-MRI-US-Biopsy-Exemplary-Image-Simple.png\",\"program\":\"Community\",\"cancer_types\":[\"Prostate Cancer\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"MR\",\"US\",\"Classification\",\"Measurement\",\"Other\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":1151,\"date_updated\":\"2023-10-20\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=Prostate-MRI-US-Biopsy\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Prostate-MRI-US-Biopsy-NBIA-manifest_v2_20231020.tcia\",\"blob\":\"prostate mri and ultrasound with pathology and coordinates of tracked biopsy prostate-mri-us-biopsy this dataset was derived from tracked biopsy sessions using the artemis biopsy system, many of which included image fusion with mri targets. patients received a 3d transrectal ultrasound scan, after which nonrigid registration (e.g. \\u201cfusion\\u201d) was performed between real-time ultrasound and preoperative mri, enabling biopsy cores to be sampled from mr regions of interest.\\u00a0most cases also included sampling of systematic biopsy cores using a 12-core digital template.\\u00a0the artemis system tracked targeted and systematic core locations using encoder kinematics of a mechanical arm, and recorded locations relative to the ultrasound scan. mri biopsy coordinates were also recorded for most cases.\\u00a0stl files and biopsy overlays are available and can be visualized in\\u00a03d slicer\\u00a0with the\\u00a0slicerheart extension. spreadsheets summarizing biopsy and mr target data are also available.\\u00a0see the detailed description tab below for more information.mri targets were defined using multiparametric mri, e.g. t2-weighted, diffusion-weighted, and perfusion-weighted sequences, and scored on a likert-like scale with close correspondence to pirads version 2. t2-weighted mri was used to trace roi contours,\\u00a0and is the only sequence provided in this dataset. mr imaging was performed on a 3 tesla trio, verio or skyra scanner (siemens, erlangen, germany). a transabdominal phased array was used in all cases, and an endorectal coil was used in a subset of cases. the majority of pulse sequences are 3d t2:spc, with tr\\\/te 2200\\\/203, matrix\\\/fov 256 \\u00d7 205\\\/14 \\u00d7 14 cm, and 1.5mm slice spacing. some cases were instead 3d t2:tse with tr\\\/te 3800\\u20135040\\\/101, and a small minority were imported from other institutions (various t2 protocols.)ultrasound scans were performed with hitachi hi-vision 5500 7.5 mhz or the noblus c41v 2-10 mhz end-fire probe. 3d scans were acquired by rotation of the end-fire probe 200 degrees about its axis, and interpolating to resample the volume with isotropic resolution.patients with suspicion of prostate cancer due to elevated psa and\\\/or suspicious imaging findings were consecutively accrued. any consented patient who underwent or had planned to receive a routine, standard-of-care prostate biopsy at the ucla clark urology center was included.note: some private tags in this collection are critical to properly displaying the stl surface and the prostate anatomy. private tag (1129,\\\"eigen, inc\\\",1016) ds voxelsize is especially important for multi-frame us cases. prostate cancer prostate mr us classification measurement other image analyses community\"},{\"id\":43659,\"type\":\"collection\",\"slug\":\"prostatex\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/prostatex\\\/\",\"title\":\"SPIE-AAPM-NCI PROSTATEx Challenges\",\"short_title\":\"PROSTATEx\",\"doi\":\"10.7937\\\/K9TCIA.2017.MURS5CL\",\"summary\":\"PROSTATEx has been superseded by PI-CAI:\\u00a0The ProstateX dataset (both training and testing cases) have been included in the PI-CAI Public Training and Development dataset. As such, ProstateX as a benchmark has been deprecated and is superseded by the PI-CAI challenge. PI-CAI is an all-new grand challenge, with over 10,000 carefully-curated prostate MRI exams to validate modern AI algorithms and estimate radiologists' performance at clinically significant prostate cancer detection and diagnosis. Key aspects of the study design have been established in conjunction with an international, multi-disciplinary scientific advisory board (16 experts in prostate AI, radiology and urology)\\u00a0 - to unify and standardize present-day guidelines, and to ensure meaningful validation of prostate-AI towards clinical translation. Please refer to https:\\\/\\\/pi-cai.grand-challenge.org for more information.\\u00a0Accessing the PROSTATEx Challenge Data SetsThe PROSTATEx Challenge (\\\"SPIE-AAPM-NCI Prostate MR Classification Challenge\\u201d) focused on quantitative image analysis methods for the diagnostic classification of clinically significant prostate cancers and was held in conjunction with the 2017 SPIE Medical Imaging Symposium. \\u00a0PROSTATEx ran from November 21, 2016 to January 15, 2017, though a \\\"live\\\" version has also been established at https:\\\/\\\/prostatex.grand-challenge.org \\u00a0which serves as an ongoing way for researchers to benchmark their performance for this task.The PROSTATEx-2 Challenge (\\\"SPIE-AAPM-NCI\\u00a0 Prostate MR Gleason Grade Group Challenge\\\" ) ran from May 15, 2017 to June 23, 2017 and was focused on\\u00a0 the development of quantitative multi-parametric MRI biomarkers for the determination of Gleason Grade Group in prostate cancer.\\u00a0 It was held in conjunction with the 2017 AAPM Annual Meeting (see\\u00a0 http:\\\/\\\/www.aapm.org\\\/GrandChallenge\\\/PROSTATEx-2).\\u00a0Supplemental data and instructions specific to both challenges are in the Detailed Description section below.\\u00a0\\u00a0Image Acquisition DetailsThis collection is a retrospective set of prostate MR studies. All studies included T2-weighted (T2W), proton density-weighted (PD-W), dynamic contrast enhanced (DCE), and diffusion-weighted (DW) imaging. The images were acquired on two different types of Siemens 3T MR scanners, the MAGNETOM Trio and Skyra. T2-weighted images were acquired using a turbo spin echo sequence and had a resolution of around 0.5 mm in plane and a slice thickness of 3.6 mm. The DCE time series was acquired using a 3-D turbo flash gradient echo sequence with a resolution of around 1.5 mm in-plane, a slice thickness of 4 mm and a temporal resolution of 3.5 s. The proton density weighted image was acquired prior to the DCE time series using the same sequence with different echo and repetition times and a different flip angle. Finally, the DWI series were acquired with a single-shot echo planar imaging sequence with a resolution of 2 mm in-plane and 3.6 mm slice thickness and with diffusion-encoding gradients in three directions. Three b-values were acquired (50, 400, and 800), and subsequently, the ADC map was calculated by the scanner software. All images were acquired without an endorectal coil.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PROSTATEx-2_logo.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Prostate Cancer\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"MR\",\"Other\",\"Classification\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":346,\"date_updated\":\"2022-07-05\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=PROSTATEx\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PROSTATEx-v1-doiJNLP.tcia\",\"blob\":\"spie-aapm-nci prostatex challenges prostatex prostatex has been superseded by pi-cai:\\u00a0the prostatex dataset (both training and testing cases) have been included in the pi-cai public training and development dataset. as such, prostatex as a benchmark has been deprecated and is superseded by the pi-cai challenge. pi-cai is an all-new grand challenge, with over 10,000 carefully-curated prostate mri exams to validate modern ai algorithms and estimate radiologists' performance at clinically significant prostate cancer detection and diagnosis. key aspects of the study design have been established in conjunction with an international, multi-disciplinary scientific advisory board (16 experts in prostate ai, radiology and urology)\\u00a0 - to unify and standardize present-day guidelines, and to ensure meaningful validation of prostate-ai towards clinical translation. please refer to https:\\\/\\\/pi-cai.grand-challenge.org for more information.\\u00a0accessing the prostatex challenge data setsthe prostatex challenge (\\\"spie-aapm-nci prostate mr classification challenge\\u201d) focused on quantitative image analysis methods for the diagnostic classification of clinically significant prostate cancers and was held in conjunction with the 2017 spie medical imaging symposium. \\u00a0prostatex ran from november 21, 2016 to january 15, 2017, though a \\\"live\\\" version has also been established at https:\\\/\\\/prostatex.grand-challenge.org \\u00a0which serves as an ongoing way for researchers to benchmark their performance for this task.the prostatex-2 challenge (\\\"spie-aapm-nci\\u00a0 prostate mr gleason grade group challenge\\\" ) ran from may 15, 2017 to june 23, 2017 and was focused on\\u00a0 the development of quantitative multi-parametric mri biomarkers for the determination of gleason grade group in prostate cancer.\\u00a0 it was held in conjunction with the 2017 aapm annual meeting (see\\u00a0 http:\\\/\\\/www.aapm.org\\\/grandchallenge\\\/prostatex-2).\\u00a0supplemental data and instructions specific to both challenges are in the detailed description section below.\\u00a0\\u00a0image acquisition detailsthis collection is a retrospective set of prostate mr studies. all studies included t2-weighted (t2w), proton density-weighted (pd-w), dynamic contrast enhanced (dce), and diffusion-weighted (dw) imaging. the images were acquired on two different types of siemens 3t mr scanners, the magnetom trio and skyra. t2-weighted images were acquired using a turbo spin echo sequence and had a resolution of around 0.5 mm in plane and a slice thickness of 3.6 mm. the dce time series was acquired using a 3-d turbo flash gradient echo sequence with a resolution of around 1.5 mm in-plane, a slice thickness of 4 mm and a temporal resolution of 3.5 s. the proton density weighted image was acquired prior to the dce time series using the same sequence with different echo and repetition times and a different flip angle. finally, the dwi series were acquired with a single-shot echo planar imaging sequence with a resolution of 2 mm in-plane and 3.6 mm slice thickness and with diffusion-encoding gradients in three directions. three b-values were acquired (50, 400, and 800), and subsequently, the adc map was calculated by the scanner software. all images were acquired without an endorectal coil. prostate cancer prostate mr other classification image analyses community\"},{\"id\":41319,\"type\":\"collection\",\"slug\":\"pseudo-phi-dicom-data\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/pseudo-phi-dicom-data\\\/\",\"title\":\"A DICOM dataset for evaluation of medical image de-identification\",\"short_title\":\"Pseudo-PHI-DICOM-Data\",\"doi\":\"10.7937\\\/s17z-r072\",\"summary\":\"Open access or shared research data must comply with (HIPAA) patient privacy regulations. These regulations require the de-identification of datasets before they can be placed in the public domain.\\u00a0 The process of image de-identification is time consuming, requires significant human resources, and is prone to human error. \\u00a0Automated image de-identification algorithms have been developed but the research community requires some method of evaluation before such tools can be widely accepted.\\u00a0 This evaluation requires a robust dataset that can be used as part of an evaluation process for de-identification algorithms. \\u00a0We developed a DICOM dataset that can be used to evaluate the performance of de-identification algorithms.\\u00a0DICOM image information objects were selected from datasets published in TCIA.\\u00a0 Synthetic Protected Health Information (PHI) was generated and inserted into selected DICOM data elements to mimic typical clinical imaging exams.\\u00a0 The evaluation dataset was de-identified by a TCIA curation team using standard TCIA tools and procedures. We are publishing the evaluation dataset (containing synthetic PHI) and de-identified evaluation dataset (result of TCIA curation) in advance of a potential competition, sponsored by the National Cancer Institute (NCI), for de-identification algorithm evaluation, and de-identification of medical image datasets. The evaluation dataset published here is a subset of a larger evaluation dataset that was created under contract for the National Cancer Institute. This subset is being published to allow researchers to test their de-identification algorithms and promote standardized procedures for validating automated de-identification.\",\"image\":false,\"program\":\"MIDI\",\"cancer_types\":[\"Various\"],\"cancer_locations\":[\"Various\"],\"data_types\":[\"PT\",\"MR\",\"CT\",\"MG\",\"DX\",\"CR\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":21,\"date_updated\":\"2021-04-07\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&PatientCriteria=6670427471,9189822998,9894340694,8989193730,8155012288,571403367,292821506,339833062,3642991663,6774825273,8732322741,7255997752,7361647728,6451050561,292821506,8834647487,6774825273,6451050561,8548156246,4025360156,6614238035,9894340694,6415974217,3209648408,9894340694,8189244869\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Pseudo-Phi-DICOM-Evaluation-dataset-April-7-2021.tcia\",\"blob\":\"a dicom dataset for evaluation of medical image de-identification pseudo-phi-dicom-data open access or shared research data must comply with (hipaa) patient privacy regulations. these regulations require the de-identification of datasets before they can be placed in the public domain.\\u00a0 the process of image de-identification is time consuming, requires significant human resources, and is prone to human error. \\u00a0automated image de-identification algorithms have been developed but the research community requires some method of evaluation before such tools can be widely accepted.\\u00a0 this evaluation requires a robust dataset that can be used as part of an evaluation process for de-identification algorithms. \\u00a0we developed a dicom dataset that can be used to evaluate the performance of de-identification algorithms.\\u00a0dicom image information objects were selected from datasets published in tcia.\\u00a0 synthetic protected health information (phi) was generated and inserted into selected dicom data elements to mimic typical clinical imaging exams.\\u00a0 the evaluation dataset was de-identified by a tcia curation team using standard tcia tools and procedures. we are publishing the evaluation dataset (containing synthetic phi) and de-identified evaluation dataset (result of tcia curation) in advance of a potential competition, sponsored by the national cancer institute (nci), for de-identification algorithm evaluation, and de-identification of medical image datasets. the evaluation dataset published here is a subset of a larger evaluation dataset that was created under contract for the national cancer institute. this subset is being published to allow researchers to test their de-identification algorithms and promote standardized procedures for validating automated de-identification. various various pt mr ct mg dx cr other midi\"},{\"id\":43233,\"type\":\"collection\",\"slug\":\"ptrc-hgsoc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ptrc-hgsoc\\\/\",\"title\":\"Proteogenomic analysis of chemo-refractory high grade serous ovarian cancer\",\"short_title\":\"PTRC-HGSOC\",\"doi\":\"10.7937\\\/6RDA-P940\",\"summary\":\"In our study, we have generated proteomic and genomic (RNA sequencing and whole genome sequencing) profiles from high grade serous ovarian cancer (HGSOC) tumor biopsies. All biospecimens are formalin-fixed, parrafin-embedded (FFPE) tissues and annotated for patient sensitivity to platinum chemotherapy (refractory or sensitive). For all 174 tumors that were analyzed, we have H&E-stained and imaged the first and last sections (\\u201cbookend\\u201d) cut from each FFPE block to allow study of tumor pathology. These H&E pathology images are uploaded in this dataset. The 174 tumors represented 158 unique patients (imaging was performed on two FFPE blocks for a small subset of patients where additional tumor mass was required for proteomic analysis).\\u00a0The bookend FFPE slides were cut at 4 \\u03bcm thickness using a microtome and mounted on glass slides (Leica Biosystems Cat# 3800040) for H&E staining. Digital images of the H&E slides were recorded using a ScanScope AT Slide Scanner (Leica Aperio Technologies, Vista, CA, USA) under 20X objective magnification (0.5 \\u03bcm resolution). Images were analyzed by HALO Image Analysis Platform software (Indicta Labs, Albuquerque, New Mexico, USA).The following clinical data are also provided for these subjects:\\u00a0Image file name (combination of Image Name and Image ID) and corresponding sample IDsChemotherapy response status: (sensitive\\\/refractory; refractory is defined in our study as clinical ovarian cancer that progresses while on platinum-based therapy or within 4 weeks)neo-adjuvant treatment (yes\\\/no)Tumor locationTumor grade, stage, substagePatient agePatient ethnicityPatient raceAge of sampleThe goals of the study were to understand mechanisms of platinum resistance in epithelial ovarian cancers (EOCs) in order to: i) predict EOCs that will respond to DNA-damaging platinum therapy, and ii) identify potential new drug targets in resistant disease to point to desperately needed new therapeutic approaches. The ability to predict platinum-resistant\\\/refractory disease would be clinically impactful by enabling the immediate triage of patients with refractory disease to clinical trials of experimental therapies, avoiding use of ineffective standard of care chemotherapy and helping to identify novel treatments. In this study, we generated proteomic and genomic (RNA sequencing and whole genome sequencing) profiling datasets and H&E images from high grade serous ovarian cancer (HGSOC) tumor biopsies representing platinum-sensitive and platinum-refractory disease.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/highlight.jpg\",\"program\":\"Community\",\"cancer_types\":[\"High grade serous ovarian cancer\"],\"cancer_locations\":[\"Ovary\"],\"data_types\":[\"Histopathology\",\"Diagnosis\",\"Demographic\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":174,\"date_updated\":\"2023-08-03\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=\\\/system\\\/files\\\/collectionmetadata\\\/202405\\\/PTRC-HGSOC_05-28-2024.json\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc0NyIsInBhc3Njb2RlIjoiODY5ZmVkMGY2MmIwYjAyOTIwMzIwMGYzZTRhOTI1MjYwNWYxZjdmOSIsInBhY2thZ2VfaWQiOiI3NDciLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"proteogenomic analysis of chemo-refractory high grade serous ovarian cancer ptrc-hgsoc in our study, we have generated proteomic and genomic (rna sequencing and whole genome sequencing) profiles from high grade serous ovarian cancer (hgsoc) tumor biopsies. all biospecimens are formalin-fixed, parrafin-embedded (ffpe) tissues and annotated for patient sensitivity to platinum chemotherapy (refractory or sensitive). for all 174 tumors that were analyzed, we have h&e-stained and imaged the first and last sections (\\u201cbookend\\u201d) cut from each ffpe block to allow study of tumor pathology. these h&e pathology images are uploaded in this dataset. the 174 tumors represented 158 unique patients (imaging was performed on two ffpe blocks for a small subset of patients where additional tumor mass was required for proteomic analysis).\\u00a0the bookend ffpe slides were cut at 4 \\u03bcm thickness using a microtome and mounted on glass slides (leica biosystems cat# 3800040) for h&e staining. digital images of the h&e slides were recorded using a scanscope at slide scanner (leica aperio technologies, vista, ca, usa) under 20x objective magnification (0.5 \\u03bcm resolution). images were analyzed by halo image analysis platform software (indicta labs, albuquerque, new mexico, usa).the following clinical data are also provided for these subjects:\\u00a0image file name (combination of image name and image id) and corresponding sample idschemotherapy response status: (sensitive\\\/refractory; refractory is defined in our study as clinical ovarian cancer that progresses while on platinum-based therapy or within 4 weeks)neo-adjuvant treatment (yes\\\/no)tumor locationtumor grade, stage, substagepatient agepatient ethnicitypatient raceage of samplethe goals of the study were to understand mechanisms of platinum resistance in epithelial ovarian cancers (eocs) in order to: i) predict eocs that will respond to dna-damaging platinum therapy, and ii) identify potential new drug targets in resistant disease to point to desperately needed new therapeutic approaches. the ability to predict platinum-resistant\\\/refractory disease would be clinically impactful by enabling the immediate triage of patients with refractory disease to clinical trials of experimental therapies, avoiding use of ineffective standard of care chemotherapy and helping to identify novel treatments. in this study, we generated proteomic and genomic (rna sequencing and whole genome sequencing) profiling datasets and h&e images from high grade serous ovarian cancer (hgsoc) tumor biopsies representing platinum-sensitive and platinum-refractory disease. high grade serous ovarian cancer ovary histopathology diagnosis demographic clinical community\"},{\"id\":43245,\"type\":\"collection\",\"slug\":\"qiba-ct-1c\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/qiba-ct-1c\\\/\",\"title\":\"QIBA CT-1C\",\"short_title\":\"QIBA CT-1C\",\"doi\":\"10.7937\\\/k9\\\/tcia.2016.yxgr4blu\",\"summary\":\"The QIBA CT-1C phantom collection was designed and shared to assist in Characterizing Variability, sans Biology. This data set was contributed by RSNA's Quantitative Imaging Biomarker Alliance activity, Volumetric CT Group 1C. Multiple image sets of the same phantoms were re-scanned across centers to isolate contributors to variability. The goal was to determine necessary control conditions to be documented in QIBA profiles, ensuring that the output for imaging when performed under these conditions will be adequately precise and accurate when scanned on profile-compliant equipment.Much more information about this data set can be found on the QIBA CT-1C wiki page at\\u00a0http:\\\/\\\/qibawiki.rsna.org\\\/index.php?title=VolCT_-_Group_1C.\\u00a0NOTE: If using the NBIA Data Retriever, be sure to use CLASSIC directory naming to avoid a bug where DESCRIPTIVE directory naming may overwrite some files .\",\"image\":false,\"program\":\"QIBA\",\"cancer_types\":[\"Phantom\"],\"cancer_locations\":[\"Phantom\"],\"data_types\":[\"CT\",\"PR\",\"SEG\",\"SR\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":1,\"date_updated\":\"2011-11-28\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=QIBA%20CT-1C\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_QIBA_CT-1C_06-22-2015.tcia\",\"blob\":\"qiba ct-1c qiba ct-1c the qiba ct-1c phantom collection was designed and shared to assist in characterizing variability, sans biology. this data set was contributed by rsna's quantitative imaging biomarker alliance activity, volumetric ct group 1c. multiple image sets of the same phantoms were re-scanned across centers to isolate contributors to variability. the goal was to determine necessary control conditions to be documented in qiba profiles, ensuring that the output for imaging when performed under these conditions will be adequately precise and accurate when scanned on profile-compliant equipment.much more information about this data set can be found on the qiba ct-1c wiki page at\\u00a0http:\\\/\\\/qibawiki.rsna.org\\\/index.php?title=volct_-_group_1c.\\u00a0note: if using the nbia data retriever, be sure to use classic directory naming to avoid a bug where descriptive directory naming may overwrite some files . phantom phantom ct pr seg sr qiba\"},{\"id\":43239,\"type\":\"collection\",\"slug\":\"qiba-ct-liver-phantom\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/qiba-ct-liver-phantom\\\/\",\"title\":\"QIBA Anthropomorphic Abdominal Phantom CT Scans\",\"short_title\":\"QIBA-CT-Liver-Phantom\",\"doi\":\"10.7937\\\/TCIA.RMV0-9Y95\",\"summary\":\"This database contains a collection of three sets of CT scan images acquired from an anthropomorphic abdominal phantom with removable liver inserts. The anthropomorphic phantoms were designed by a group of scientists from FDA and Columbia University Medical Center and custom manufactured by QRM (Moehrendorf, Germany). Data sets #1 (AP phantom) & #2 (PVP phantom): Two liver inserts, each containing 19 embedded synthetic lesions with known volumes of varying diameter (6\\u201340 mm), shape, contrast (10\\u201365 HU), and density (homogenous and mixed) were designed to have liver parenchyma and lesion CT values simulating arterial phase (AP phantom) and portal venous phase (PVP phantom) imaging, respectively. The two phantoms were scanned using two 64-slice multi-detector helical CT scanners (GE 750HD and Siemens Biograph mCT) across a wide range of abdominal imaging protocols, including three e\\ufb00ective mAs (50, 100, 250), two pitches (GE\\\/Siemens: 1.375 and 0.983\\\/1.35 and 1.0), four slice thicknesses (GE\\\/Siemens: 0.625, 1.25, 2.5, and 5 mm\\\/0.6, 1.5, 3, and 5 mm), and two convolution kernels (GE\\\/Siemens: Standard and Soft\\\/B20f and B30f). Two repeated scans were performed for each protocol and for each phantom. Dataset #3 (IR phantom): One non-uniform liver insert was filled with an in-house made gelatin-based background, and varied concentrations of gelatin, salt, and water were used to mimic normal liver tissue and focal fat at the arterial phase. Ten spherical lesions of five sizes (20, 14, 10, 7, and 5 mm in diameter) and two radiodensities (95 and 110 HU) were placed on the border between the fatty and normal parenchyma. The phantom was imaged with a CT scanner (GE 750HD) across a set of imaging protocols, including three e\\ufb00ective mAs (50, 100, 250), three slice thicknesses (1.25, 2.5, and 5 mm) and five reconstruction algorithms (standard, AISR30%, AISR50%, ASIR70%, and VEO). Five repeated scans were performed for each protocol.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/QIBA-Anthropomorphic-Abdominal-Phantom-CT-Scans.jpg\",\"program\":\"QIBA\",\"cancer_types\":[\"Phantom\"],\"cancer_locations\":[\"Liver Phantom\"],\"data_types\":[\"CT\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":3,\"date_updated\":\"2021-04-27\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=QIBA-CT-Liver-Phantom\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/QIBA-CT-Liver-Phantom_20210422-manifest.tcia\",\"blob\":\"qiba anthropomorphic abdominal phantom ct scans qiba-ct-liver-phantom this database contains a collection of three sets of ct scan images acquired from an anthropomorphic abdominal phantom with removable liver inserts. the anthropomorphic phantoms were designed by a group of scientists from fda and columbia university medical center and custom manufactured by qrm (moehrendorf, germany). data sets #1 (ap phantom) & #2 (pvp phantom): two liver inserts, each containing 19 embedded synthetic lesions with known volumes of varying diameter (6\\u201340 mm), shape, contrast (10\\u201365 hu), and density (homogenous and mixed) were designed to have liver parenchyma and lesion ct values simulating arterial phase (ap phantom) and portal venous phase (pvp phantom) imaging, respectively. the two phantoms were scanned using two 64-slice multi-detector helical ct scanners (ge 750hd and siemens biograph mct) across a wide range of abdominal imaging protocols, including three e\\ufb00ective mas (50, 100, 250), two pitches (ge\\\/siemens: 1.375 and 0.983\\\/1.35 and 1.0), four slice thicknesses (ge\\\/siemens: 0.625, 1.25, 2.5, and 5 mm\\\/0.6, 1.5, 3, and 5 mm), and two convolution kernels (ge\\\/siemens: standard and soft\\\/b20f and b30f). two repeated scans were performed for each protocol and for each phantom. dataset #3 (ir phantom): one non-uniform liver insert was filled with an in-house made gelatin-based background, and varied concentrations of gelatin, salt, and water were used to mimic normal liver tissue and focal fat at the arterial phase. ten spherical lesions of five sizes (20, 14, 10, 7, and 5 mm in diameter) and two radiodensities (95 and 110 hu) were placed on the border between the fatty and normal parenchyma. the phantom was imaged with a ct scanner (ge 750hd) across a set of imaging protocols, including three e\\ufb00ective mas (50, 100, 250), three slice thicknesses (1.25, 2.5, and 5 mm) and five reconstruction algorithms (standard, aisr30%, aisr50%, asir70%, and veo). five repeated scans were performed for each protocol.\\u00a0 phantom liver phantom ct image analyses qiba\"},{\"id\":42339,\"type\":\"collection\",\"slug\":\"qin-brain-dsc-mri\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/qin-brain-dsc-mri\\\/\",\"title\":\"Glioma DSC-MRI Perfusion Data with Standard Imaging and ROIs\",\"short_title\":\"QIN-BRAIN-DSC-MRI\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.5DI84Js8\",\"summary\":\"This collection contains MR images of both low and high grade glial brain lesions. Data includes post-contrast T1w images with co-registered volumes of dynamic susceptibility contrast (DSC) MR images in DICOM format. Binary regions of interest are also included, in DICOM format, of the lesion, arterial input function, normal appearing white matter, normal appearing cerebral cortex, and whole brain. The data was provided to help facilitate research activities of the National Cancer Institute's (NCI's) Quantitative Imaging Network (QIN). This collection was supported by Grant U01 CA176110.\\nAbout the NCI QIN\\nThe mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the\\u00a0Quantitative Imaging Network Collections\\u00a0page. Interested investigators can apply to the QIN at:\\u00a0Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01) PAR-11-150.\",\"image\":false,\"program\":\"QIN\",\"cancer_types\":[\"Low & High Grade Glioma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":49,\"date_updated\":\"2019-08-28\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=QIN-BRAIN-DSC-MRI\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/QIN-BRAIN-DSC-MRI_2016-03-29.tcia\",\"blob\":\"glioma dsc-mri perfusion data with standard imaging and rois qin-brain-dsc-mri this collection contains mr images of both low and high grade glial brain lesions. data includes post-contrast t1w images with co-registered volumes of dynamic susceptibility contrast (dsc) mr images in dicom format. binary regions of interest are also included, in dicom format, of the lesion, arterial input function, normal appearing white matter, normal appearing cerebral cortex, and whole brain. the data was provided to help facilitate research activities of the national cancer institute's (nci's) quantitative imaging network (qin). this collection was supported by grant u01 ca176110.\\nabout the nci qin\\nthe mission of the qin is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. more information is available on the\\u00a0quantitative imaging network collections\\u00a0page. interested investigators can apply to the qin at:\\u00a0quantitative imaging for evaluation of responses to cancer therapies (u01) par-11-150. low & high grade glioma brain mr qin\"},{\"id\":43263,\"type\":\"collection\",\"slug\":\"qin-breast\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/qin-breast\\\/\",\"title\":\"QIN-BREAST\",\"short_title\":\"QIN-BREAST\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.21JUEBH0\",\"summary\":\"This collection contains longitudinal PET\\\/CT and quantitative MR images collected for the purpose of studying treatment assessment in breast cancer in the neoadjuvant setting. Images were acquired at three time points: prior to the start of treatment (t1), after the first cycle of treatment (t2), and either after the second cycle of treatment or at the completion of all treatments (prior to surgery) (t3). The PET\\\/CT images were acquired with a support device built in-house to allow the patient to be in the prone position to facilitate registration with the MRI data. The value of this collection is to provide clinical imaging data for the development and evaluation of quantitative imaging methods for treatment assessment early in the course of therapy for breast cancer. Data is provided by Vanderbilt University, PI Dr. Thomas E. Yankeelov.\\u00a0PET\\\/CT data were acquired with a GE Discovery STE scanner (GE Healthcare, Waukesha, WI, USA). A low-mAs CT scan was acquired for attenuation correction of the emission data. The acquisition parameters for the transmission CT scan were the following: the tube current was 80 mAs for a 70-kg patient and scaled accordingly for all patients, the tube voltage was 120 KVp, and the pitch was 1.675\\\/1. The activity of FDG administered was approximately 370 MBq (10 mCi) for a 70-kg patient and scaled according to weight. FDG was administered intravenously via an antecubital vein contra-lateral to the affected breast. After 60 min, emission data was collected in 3D mode for 2 min per bed position. The emission scan was first collected in the prone position over the breast only, and then in the supine position from the skull to mid-femurs. Standard-of-care supine images and research prone images were acquired at times t1 and t3, while only the prone images were acquired at t2.The MRI data consist of diffusion-weighted images (DWIs), dynamic contrast-enhanced (DCE) images, and multi-flip data for T1-mapping. The MRIs were obtained using a dedicated 16-channel bilateral breast coil at 3.0T (Philips Achieva with the MammoTrak table). DWIs were acquired with a single-shot spin echo (SE) echo planar imaging (EPI) sequence in three orthogonal diffusion encoding directions (x, y, and z). For 14 patients, b = 0 and 500 s\\\/mm2, TR\\\/TE = 2500 ms\\\/45 ms\\u00a0\\u0394 = 21.4 ms,\\u00a0\\u03b4 = 10.3 ms and 10 signal acquisitions were acquired. For 19 patients, b = 0 and 600 s\\\/mm2, TR\\\/TE = \\u201cshortest\\u201d (range = 1800 - 3083 ms\\\/43 - 60 ms)\\u00a0\\u0394 = 20.7 - 29 ms,\\u00a0\\u03b4 = 11.4 - 21 ms and 10 signal acquisitions were acquired. For four patients, b = 50 and 600 s\\\/mm2 for two patients), TR\\\/TE = \\u201cshortest\\u201d (range = 1840 - 3593 ms\\\/43 - 60 ms)\\u00a0\\u0394 = 20.6 - 29 ms,\\u00a0\\u03b4 = 11.5 - 21 ms and 10 signal acquisitions were acquired. Prior to the DCE-MRI acquisition, data for constructing a T1 map were acquired with an RF-spoiled 3D gradient echo multi-flip angle approach with ten flip angles from 2 to 20 degrees in 2o increments. For both the T1 map and DCE scans, TR = 7.9 ms, TE = 4.6 ms, and the acquisition matrix was 192x192x20 (full-breast) over a sagittal square field of view (22 cm2) with slice thickness of 5 mm. For the DCE study, each 20-slice set was collected in 16 seconds at 25 time points for just under seven minutes of dynamic scanning. A catheter placed within an antecubital vein delivered 0.1 mmol\\\/kg (9 \\u2013 15 mL, depending on patient weight) of gadopentetate dimeglumine, Gd-DTPA, (Magnevist, Wayne, NJ) at 2 mL\\\/sec (followed by a saline flush) via a power injector (Medrad, Warrendale, PA) after the acquisition of the first three dynamic scans (baseline).QIN-BREAST-02 Multi-center StudyAn extension of this\\u00a0QIN-BREAST collection is available via the QIN-BREAST-02 collection.\\u00a0 It contains\\u00a0updated scan protocols and data collected at both Vanderbilt University Medical Center and the University of Chicago to demonstrate similar results at multiple sites (both using Philips 3T MR scanners).About the NCI QINThe mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the Quantitative Imaging Network Collections page. Interested investigators can apply to the QIN at: Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01) PAR-11-150.\",\"image\":false,\"program\":\"QIN\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"CT\",\"PT\",\"MR\",\"Classification\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":67,\"date_updated\":\"2020-01-10\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=QIN-BREAST\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/QIN-BREAST_2015-09-04.tcia\",\"blob\":\"qin-breast qin-breast this collection contains longitudinal pet\\\/ct and quantitative mr images collected for the purpose of studying treatment assessment in breast cancer in the neoadjuvant setting. images were acquired at three time points: prior to the start of treatment (t1), after the first cycle of treatment (t2), and either after the second cycle of treatment or at the completion of all treatments (prior to surgery) (t3). the pet\\\/ct images were acquired with a support device built in-house to allow the patient to be in the prone position to facilitate registration with the mri data. the value of this collection is to provide clinical imaging data for the development and evaluation of quantitative imaging methods for treatment assessment early in the course of therapy for breast cancer. data is provided by vanderbilt university, pi dr. thomas e. yankeelov.\\u00a0pet\\\/ct data were acquired with a ge discovery ste scanner (ge healthcare, waukesha, wi, usa). a low-mas ct scan was acquired for attenuation correction of the emission data. the acquisition parameters for the transmission ct scan were the following: the tube current was 80 mas for a 70-kg patient and scaled accordingly for all patients, the tube voltage was 120 kvp, and the pitch was 1.675\\\/1. the activity of fdg administered was approximately 370 mbq (10 mci) for a 70-kg patient and scaled according to weight. fdg was administered intravenously via an antecubital vein contra-lateral to the affected breast. after 60 min, emission data was collected in 3d mode for 2 min per bed position. the emission scan was first collected in the prone position over the breast only, and then in the supine position from the skull to mid-femurs. standard-of-care supine images and research prone images were acquired at times t1 and t3, while only the prone images were acquired at t2.the mri data consist of diffusion-weighted images (dwis), dynamic contrast-enhanced (dce) images, and multi-flip data for t1-mapping. the mris were obtained using a dedicated 16-channel bilateral breast coil at 3.0t (philips achieva with the mammotrak table). dwis were acquired with a single-shot spin echo (se) echo planar imaging (epi) sequence in three orthogonal diffusion encoding directions (x, y, and z). for 14 patients, b = 0 and 500 s\\\/mm2, tr\\\/te = 2500 ms\\\/45 ms\\u00a0\\u0394 = 21.4 ms,\\u00a0\\u03b4 = 10.3 ms and 10 signal acquisitions were acquired. for 19 patients, b = 0 and 600 s\\\/mm2, tr\\\/te = \\u201cshortest\\u201d (range = 1800 - 3083 ms\\\/43 - 60 ms)\\u00a0\\u0394 = 20.7 - 29 ms,\\u00a0\\u03b4 = 11.4 - 21 ms and 10 signal acquisitions were acquired. for four patients, b = 50 and 600 s\\\/mm2 for two patients), tr\\\/te = \\u201cshortest\\u201d (range = 1840 - 3593 ms\\\/43 - 60 ms)\\u00a0\\u0394 = 20.6 - 29 ms,\\u00a0\\u03b4 = 11.5 - 21 ms and 10 signal acquisitions were acquired. prior to the dce-mri acquisition, data for constructing a t1 map were acquired with an rf-spoiled 3d gradient echo multi-flip angle approach with ten flip angles from 2 to 20 degrees in 2o increments. for both the t1 map and dce scans, tr = 7.9 ms, te = 4.6 ms, and the acquisition matrix was 192x192x20 (full-breast) over a sagittal square field of view (22 cm2) with slice thickness of 5 mm. for the dce study, each 20-slice set was collected in 16 seconds at 25 time points for just under seven minutes of dynamic scanning. a catheter placed within an antecubital vein delivered 0.1 mmol\\\/kg (9 \\u2013 15 ml, depending on patient weight) of gadopentetate dimeglumine, gd-dtpa, (magnevist, wayne, nj) at 2 ml\\\/sec (followed by a saline flush) via a power injector (medrad, warrendale, pa) after the acquisition of the first three dynamic scans (baseline).qin-breast-02 multi-center studyan extension of this\\u00a0qin-breast collection is available via the qin-breast-02 collection.\\u00a0 it contains\\u00a0updated scan protocols and data collected at both vanderbilt university medical center and the university of chicago to demonstrate similar results at multiple sites (both using philips 3t mr scanners).about the nci qinthe mission of the qin is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. more information is available on the quantitative imaging network collections page. interested investigators can apply to the qin at: quantitative imaging for evaluation of responses to cancer therapies (u01) par-11-150. breast cancer breast ct pt mr classification clinical qin\"},{\"id\":43273,\"type\":\"collection\",\"slug\":\"qin-breast-02\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/qin-breast-02\\\/\",\"title\":\"QIN-BREAST-02\",\"short_title\":\"QIN-BREAST-02\",\"doi\":\"10.7937\\\/TCIA.2019.4cfm06rr\",\"summary\":\"This data is from a multi-site, multi-parametric quantitative MRI study of adult (18+ years old) females diagnosed with invasive breast cancer.\\u00a0 Subjects all had a lesion size >1cm in longest dimension and were undergoing neoadjuvant therapy. Participants were scanned prior to any therapy and then 2-3 times after the initiation of neoadjuvant therapy, depending upon their treatment regimen.\\u00a0All data sets were acquired with a research-only protocol, including: DWI (SE EPI), OGSE, qMT, B0 map, multi-flip T1 map, Bloch-Siegert B1 map, DCE, and a T1-weighted anatomical image.This dataset is an extension of the original QIN-BREAST collection, with updated scan protocols and data collected at both Vanderbilt University Medical Center and the University of Chicago to demonstrate reproducible results at multiple sites (both using Philips 3T MR scanners).\",\"image\":false,\"program\":\"QIN\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MR\",\"Demographic\",\"Diagnosis\",\"Molecular Test\",\"Treatment\",\"Follow-Up\",\"Protocol\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":13,\"date_updated\":\"2019-07-10\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=QIN-BREAST-02\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/QIN-BREAST-02_20190408_v1.tcia\",\"blob\":\"qin-breast-02 qin-breast-02 this data is from a multi-site, multi-parametric quantitative mri study of adult (18+ years old) females diagnosed with invasive breast cancer.\\u00a0 subjects all had a lesion size >1cm in longest dimension and were undergoing neoadjuvant therapy. participants were scanned prior to any therapy and then 2-3 times after the initiation of neoadjuvant therapy, depending upon their treatment regimen.\\u00a0all data sets were acquired with a research-only protocol, including: dwi (se epi), ogse, qmt, b0 map, multi-flip t1 map, bloch-siegert b1 map, dce, and a t1-weighted anatomical image.this dataset is an extension of the original qin-breast collection, with updated scan protocols and data collected at both vanderbilt university medical center and the university of chicago to demonstrate reproducible results at multiple sites (both using philips 3t mr scanners). breast cancer breast mr demographic diagnosis molecular test treatment follow-up protocol clinical qin\"},{\"id\":45433,\"type\":\"collection\",\"slug\":\"qin-breast-dce-mri\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/qin-breast-dce-mri\\\/\",\"title\":\"Variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge\",\"short_title\":\"QIN Breast DCE-MRI\",\"doi\":\"10.7937\\\/k9\\\/tcia.2014.a2n1ixox\",\"summary\":\"This collection of breast dynamic contrast-enhanced (DCE) MRI data contains images from a longitudinal study to assess breast cancer response to neoadjuvant chemotherapy. Images were acquired at four time points: prior to the start of treatment (Visit 1, V1), after the first cycle of treatment (Visit 2, V2), at midpoint of treatment course (Visit 3, V3), and after completion of treatment (prior to surgery) (Visit 4, V4). The value of this collection is to provide clinical imaging data for the development and validation of quantitative imaging methods for assessment of breast cancer response to treatment. Data is provided by Oregon Health & Science University, PI Dr. Wei Huang.The MRI data consist of DCE-MRI images, which were acquired using a Siemens 3T TIM Trio system with the body coil and a four-channel bilateral phased-array breast coil as the transmitter and receiver, respectively.\\u00a0 Following pilot scans and pre-contrast T2-weighted MRI with fat-saturation and T1-weighted MRI without fat-saturation, axial bilateral DCE-MRI images with fat-saturation and full breast coverage were acquired with a 3D gradient echo-based TWIST (Time-resolved angiography WIth Stochastic Trajectories) sequence, which employs the strategy of k-space undersampling during acquisition and data sharing during reconstruction.\\u00a0 DCE-MRI acquisition parameters included 10o flip angle, 2.9\\\/6.2\\u00a0ms TE\\\/TR, a parallel imaging acceleration factor of two, 30-34 cm FOV, 320x320 in-plane matrix size, and 1.4 mm slice thickness.\\u00a0 The total acquisition time was ~10 minutes for 32-34 image volume sets of 112-120 slices each with 18-20 s temporal resolution.\\u00a0 The contrast agent Gd(HP-DO3A) [ProHance] IV injection (0.1 mmol\\\/kg at 2 mL\\\/s) by a programmable power injector was timed to commence after acquisition of two baseline image volumes, followed by a 20-mL saline flush.\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0A total of 20 data sets from this collection have been used for a multi-QIN center challenge, in which each participating site performed pharmacokinetic analysis of the breast DCE-MRI data using software tools\\\/algorithms available to them. The shared data sets are from the V1 and V2 studies of 10 patients (BreastChemo 1, 5, 6, 8, 10, 12, 13, 14, 15, and 16) \\u2013 3 pathologic complete responders (pCRs) and 7 non-pCRs.\\u00a0 The goal of the challenge was to evaluate variations in DCE-MRI assessment of breast cancer response to neoadjuvant chemotherapy caused by differences in software tools\\\/algorithms only.\\u00a0About the NCI QINThe mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the Quantitative Imaging Network Collections page. Interested investigators can apply to the QIN at: Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01) PAR-11-150.\",\"image\":false,\"program\":\"QIN\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MR\",\"KO\",\"Follow-Up\",\"Other\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":10,\"date_updated\":\"2019-04-18\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=QIN%20Breast%20DCE-MRI\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/qin-dce-mri-challenge_DCM_NIFTI.tcia\",\"blob\":\"variations of dynamic contrast-enhanced magnetic resonance imaging in evaluation of breast cancer therapy response: a multicenter data analysis challenge qin breast dce-mri this collection of breast dynamic contrast-enhanced (dce) mri data contains images from a longitudinal study to assess breast cancer response to neoadjuvant chemotherapy. images were acquired at four time points: prior to the start of treatment (visit 1, v1), after the first cycle of treatment (visit 2, v2), at midpoint of treatment course (visit 3, v3), and after completion of treatment (prior to surgery) (visit 4, v4). the value of this collection is to provide clinical imaging data for the development and validation of quantitative imaging methods for assessment of breast cancer response to treatment. data is provided by oregon health & science university, pi dr. wei huang.the mri data consist of dce-mri images, which were acquired using a siemens 3t tim trio system with the body coil and a four-channel bilateral phased-array breast coil as the transmitter and receiver, respectively.\\u00a0 following pilot scans and pre-contrast t2-weighted mri with fat-saturation and t1-weighted mri without fat-saturation, axial bilateral dce-mri images with fat-saturation and full breast coverage were acquired with a 3d gradient echo-based twist (time-resolved angiography with stochastic trajectories) sequence, which employs the strategy of k-space undersampling during acquisition and data sharing during reconstruction.\\u00a0 dce-mri acquisition parameters included 10o flip angle, 2.9\\\/6.2\\u00a0ms te\\\/tr, a parallel imaging acceleration factor of two, 30-34 cm fov, 320x320 in-plane matrix size, and 1.4 mm slice thickness.\\u00a0 the total acquisition time was ~10 minutes for 32-34 image volume sets of 112-120 slices each with 18-20 s temporal resolution.\\u00a0 the contrast agent gd(hp-do3a) [prohance] iv injection (0.1 mmol\\\/kg at 2 ml\\\/s) by a programmable power injector was timed to commence after acquisition of two baseline image volumes, followed by a 20-ml saline flush.\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0a total of 20 data sets from this collection have been used for a multi-qin center challenge, in which each participating site performed pharmacokinetic analysis of the breast dce-mri data using software tools\\\/algorithms available to them. the shared data sets are from the v1 and v2 studies of 10 patients (breastchemo 1, 5, 6, 8, 10, 12, 13, 14, 15, and 16) \\u2013 3 pathologic complete responders (pcrs) and 7 non-pcrs.\\u00a0 the goal of the challenge was to evaluate variations in dce-mri assessment of breast cancer response to neoadjuvant chemotherapy caused by differences in software tools\\\/algorithms only.\\u00a0about the nci qinthe mission of the qin is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. more information is available on the quantitative imaging network collections page. interested investigators can apply to the qin at: quantitative imaging for evaluation of responses to cancer therapies (u01) par-11-150. breast cancer breast mr ko follow-up other clinical image analyses qin\"},{\"id\":43287,\"type\":\"collection\",\"slug\":\"qin-gbm-treatment-response\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/qin-gbm-treatment-response\\\/\",\"title\":\"QIN GBM Treatment Response\",\"short_title\":\"QIN GBM Treatment Response\",\"doi\":\"10.7937\\\/k9\\\/tcia.2016.nQF4gpn2\",\"summary\":\"This collection contains \\u201cdouble baseline\\u201d multi-parametric MRI images collected on patients with newly diagnosed glioblastoma. \\u00a0The value of this collection is to provide clinical image data to establish the test-retest characteristics of parameters calculated from DW-MRI, DCE-MRI, and DSC-MRI such as ADC, Ktrans and rCBV. Data were provided by Dr. Elizabeth Gerstner and Dr. Kalpathy-Cramer (MGH) as part of their participation in the Quantitative Imaging Network.\\nThe patients were scanned after surgery but prior to the start of therapy, typically 2-5 days apart. The structural images included T1-weighted pre- and post-contrast images, T2-weighted images, FLAIR and MEMPRAGE images.\\u00a0 Advanced MRI sequences included Diffusion Weighted (DW) Imaging, Dynamic Contrast-Enhanced MRI (DCE-MRI), and Dynamic Susceptibility Contrast MRI (DSC-MRI). \\u00a0\\nNote: If there was an issue with the scan it was excluded, so not all patients have every type of scan available.\\nThe images were obtained on 32-channel Siemens Trio 3T scanners using Siemens 32-channel head coils.\\nT1-weighted imaging was performed using a 2D FLASH sequence with TR\\\/TE\\\/\\u03b1 = 600 ms\\\/12 ms\\\/90\\u00b0, matrix size 256 \\u00d7 216, field of view (FOV ) 185mm \\u00d7 220mm, \\u00a023 slices and 5mm slice thickness.T2-weighted imaging was performed using a 3D SPACE spin-echo sequence with TR\\\/TE = 3200 ms\\\/428 ms, matrix size 256 \\u00d7 258, FOV 256mm \\u00d7 256mm,\\u00a0 176 slices and 1mm slice thickness.FLAIR was performed using a 2D spin-echo inversion-recovery sequence with TR\\\/TI\\\/TE = 10000 ms\\\/2500 ms\\\/70 ms, matrix size 256 \\u00d7 162, FOV 185mm \\u00d7 220mm, 23 slices and 5mm slice thickness.MEMPRAGE was performed using a 3D multi-echo Magnetization Prepared Rapid Gradient Echo sequence with TR\\\/TI\\\/TE1\\\/TE2\\\/TE3\\\/TE4\\\/\\u03b1 =2530 ms\\\/1200 ms\\\/1.64 ms\\\/3.5 ms\\\/5.36 ms\\\/7.22 ms\\\/7\\u00b0, matrix size 256 \\u00d7 256, FOV 256mm \\u00d7 256mm, 176 slices and 1mm slice thickness.A Diffusion-weighted (DW) echo planar imaging sequence with trace diffusion sensitization and b-values of 0 and 700 s\\\/mm2, TR\\\/TE = 7980 ms\\\/84 ms, matrix size 128 \\u00d7 128, FOV 237mm \\u00d7 237mm, 64 slices and 1.86mm slice thickness, provided data for Apparent Diffusion Coefficient (ADC) maps.Dynamic Contrast-enhanced (DCE-MRI) sequences: To estimate pre-contrast T1 relaxation time for DCE quantitation purposes, T1 mapping was performed by using a 3D FLASH sequence before the injection of contrast agent with TR\\\/TE = 7.3 ms\\\/4.41 ms,\\u00a0matrix size 128 \\u00d7 128, FOV 230mm \\u00d7 230mm, 20 slices, and 2.1mm slice thickness. This sequence was repeated five times at five different flip-angles of 2\\u00b0, 5\\u00b0,10\\u00b0,15\\u00b0, and 30\\u00b0. DCE MRI utilized a 3D FLASH dual gradient-echo sequence with with TR\\\/TE1\\\/TE2\\\/\\u03b1 = 6.8 ms\\\/2.61 ms\\\/3.89 ms\\\/10\\u00b0,\\u00a0matrix size 128 \\u00d7 128, FOV 230mm \\u00d7 230mm, 20 slices and 2.1mm slice thickness. The acquisition was repeated for 60 times (frames) for a total scan time of 6 min,\\u00a0corresponding to a\\u00a0temporal resolution of 6 s. A bolus of\\u00a00.1\\u00a0mmol\\\/kg of Gd-DTPA (gadopentetic acid) was injected 52 s after the scan started.Finally, a combined gradient-echo (ge) and spin-echo (se) 2D EPI sequence was performed to acquire DSC-MRI data with TR\\\/TE(ge)\\\/TE(se)\\\/\\u03b1 = 1500 ms\\\/31 ms\\\/95 ms\\\/80\\u00b0, matrix size 160 \\u00d7 160, FOV 192mm \\u00d7 192mm, 12 slices and 5mm slice thickness. The acquisition was repeated 100 times for a total scan time of 2 min and 41 sec,\\u00a0corresponding to a\\u00a0temporal resolution of 1.61 s. A bolus of 0.1 mmol\\\/kg of GD-DTPA was injected 80 s after the scan started.\\u00a0\\u00a0\\nAbout the NCI QIN\\nThe mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the\\u00a0Quantitative Imaging Network Collections\\u00a0page. Interested investigators can apply to the QIN at:\\u00a0Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01) PAR-11-150.\",\"image\":false,\"program\":\"QIN\",\"cancer_types\":[\"Glioblastoma Multiforme\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":54,\"date_updated\":\"2020-04-03\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=QIN%20GBM%20Treatment%20Response\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/QIN-GBM-Treatment-Response_2015-08-12.tcia\",\"blob\":\"qin gbm treatment response qin gbm treatment response this collection contains \\u201cdouble baseline\\u201d multi-parametric mri images collected on patients with newly diagnosed glioblastoma. \\u00a0the value of this collection is to provide clinical image data to establish the test-retest characteristics of parameters calculated from dw-mri, dce-mri, and dsc-mri such as adc, ktrans and rcbv. data were provided by dr. elizabeth gerstner and dr. kalpathy-cramer (mgh) as part of their participation in the quantitative imaging network.\\nthe patients were scanned after surgery but prior to the start of therapy, typically 2-5 days apart. the structural images included t1-weighted pre- and post-contrast images, t2-weighted images, flair and memprage images.\\u00a0 advanced mri sequences included diffusion weighted (dw) imaging, dynamic contrast-enhanced mri (dce-mri), and dynamic susceptibility contrast mri (dsc-mri). \\u00a0\\nnote: if there was an issue with the scan it was excluded, so not all patients have every type of scan available.\\nthe images were obtained on 32-channel siemens trio 3t scanners using siemens 32-channel head coils.\\nt1-weighted imaging was performed using a 2d flash sequence with tr\\\/te\\\/\\u03b1 = 600 ms\\\/12 ms\\\/90\\u00b0, matrix size 256 \\u00d7 216, field of view (fov ) 185mm \\u00d7 220mm, \\u00a023 slices and 5mm slice thickness.t2-weighted imaging was performed using a 3d space spin-echo sequence with tr\\\/te = 3200 ms\\\/428 ms, matrix size 256 \\u00d7 258, fov 256mm \\u00d7 256mm,\\u00a0 176 slices and 1mm slice thickness.flair was performed using a 2d spin-echo inversion-recovery sequence with tr\\\/ti\\\/te = 10000 ms\\\/2500 ms\\\/70 ms, matrix size 256 \\u00d7 162, fov 185mm \\u00d7 220mm, 23 slices and 5mm slice thickness.memprage was performed using a 3d multi-echo magnetization prepared rapid gradient echo sequence with tr\\\/ti\\\/te1\\\/te2\\\/te3\\\/te4\\\/\\u03b1 =2530 ms\\\/1200 ms\\\/1.64 ms\\\/3.5 ms\\\/5.36 ms\\\/7.22 ms\\\/7\\u00b0, matrix size 256 \\u00d7 256, fov 256mm \\u00d7 256mm, 176 slices and 1mm slice thickness.a diffusion-weighted (dw) echo planar imaging sequence with trace diffusion sensitization and b-values of 0 and 700 s\\\/mm2, tr\\\/te = 7980 ms\\\/84 ms, matrix size 128 \\u00d7 128, fov 237mm \\u00d7 237mm, 64 slices and 1.86mm slice thickness, provided data for apparent diffusion coefficient (adc) maps.dynamic contrast-enhanced (dce-mri) sequences: to estimate pre-contrast t1 relaxation time for dce quantitation purposes, t1 mapping was performed by using a 3d flash sequence before the injection of contrast agent with tr\\\/te = 7.3 ms\\\/4.41 ms,\\u00a0matrix size 128 \\u00d7 128, fov 230mm \\u00d7 230mm, 20 slices, and 2.1mm slice thickness. this sequence was repeated five times at five different flip-angles of 2\\u00b0, 5\\u00b0,10\\u00b0,15\\u00b0, and 30\\u00b0. dce mri utilized a 3d flash dual gradient-echo sequence with with tr\\\/te1\\\/te2\\\/\\u03b1 = 6.8 ms\\\/2.61 ms\\\/3.89 ms\\\/10\\u00b0,\\u00a0matrix size 128 \\u00d7 128, fov 230mm \\u00d7 230mm, 20 slices and 2.1mm slice thickness. the acquisition was repeated for 60 times (frames) for a total scan time of 6 min,\\u00a0corresponding to a\\u00a0temporal resolution of 6 s. a bolus of\\u00a00.1\\u00a0mmol\\\/kg of gd-dtpa (gadopentetic acid) was injected 52 s after the scan started.finally, a combined gradient-echo (ge) and spin-echo (se) 2d epi sequence was performed to acquire dsc-mri data with tr\\\/te(ge)\\\/te(se)\\\/\\u03b1 = 1500 ms\\\/31 ms\\\/95 ms\\\/80\\u00b0, matrix size 160 \\u00d7 160, fov 192mm \\u00d7 192mm, 12 slices and 5mm slice thickness. the acquisition was repeated 100 times for a total scan time of 2 min and 41 sec,\\u00a0corresponding to a\\u00a0temporal resolution of 1.61 s. a bolus of 0.1 mmol\\\/kg of gd-dtpa was injected 80 s after the scan started.\\u00a0\\u00a0\\nabout the nci qin\\nthe mission of the qin is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. more information is available on the\\u00a0quantitative imaging network collections\\u00a0page. interested investigators can apply to the qin at:\\u00a0quantitative imaging for evaluation of responses to cancer therapies (u01) par-11-150. glioblastoma multiforme brain mr qin\"},{\"id\":43315,\"type\":\"collection\",\"slug\":\"qin-headneck\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/qin-headneck\\\/\",\"title\":\"QIN-HEADNECK\",\"short_title\":\"QIN-HEADNECK\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.K0F5CGLI\",\"summary\":\"This collection is a set of head and neck cancer patients' multiple positron emission tomography\\\/computed tomography (PET\\\/CT) 18F-FDG scans\\u2013before and after therapy\\u2013with follow up scans where clinically indicated.\\u00a0The data was provided to help facilitate research activities of the National Cancer Institute's (NCI's) Quantitative Imaging Network (QIN). This collection was supported by Grants: U24 CA180918 (http:\\\/\\\/qiicr.org) and U01 CA140206.The following schematic summarizes much of the work done within the QIICR grant to augment the PET\\\/CT scans with segmentations and clinical data using the DICOM standard: (click to enlarge)About the NCI QINThe mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the Quantitative Imaging Network Collections page. Interested investigators can apply to the QIN at: Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01) PAR-11-150.\",\"image\":false,\"program\":\"QIN\",\"cancer_types\":[\"Head and Neck Carcinomas\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"CT\",\"PT\",\"SR\",\"SEG\",\"RWV\",\"Demographic\",\"Exposure\",\"Treatment\",\"Diagnosis\",\"Follow-Up\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":279,\"date_updated\":\"2020-09-15\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=QIN-HEADNECK\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/QIN-HEADNECK-NBIA-manifest.tcia_20200914.tcia\",\"blob\":\"qin-headneck qin-headneck this collection is a set of head and neck cancer patients' multiple positron emission tomography\\\/computed tomography (pet\\\/ct) 18f-fdg scans\\u2013before and after therapy\\u2013with follow up scans where clinically indicated.\\u00a0the data was provided to help facilitate research activities of the national cancer institute's (nci's) quantitative imaging network (qin). this collection was supported by grants: u24 ca180918 (http:\\\/\\\/qiicr.org) and u01 ca140206.the following schematic summarizes much of the work done within the qiicr grant to augment the pet\\\/ct scans with segmentations and clinical data using the dicom standard: (click to enlarge)about the nci qinthe mission of the qin is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. more information is available on the quantitative imaging network collections page. interested investigators can apply to the qin at: quantitative imaging for evaluation of responses to cancer therapies (u01) par-11-150. head and neck carcinomas head-neck ct pt sr seg rwv demographic exposure treatment diagnosis follow-up clinical image analyses qin\"},{\"id\":43327,\"type\":\"collection\",\"slug\":\"qin-lung-ct\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/qin-lung-ct\\\/\",\"title\":\"QIN LUNG CT\",\"short_title\":\"QIN LUNG CT\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.NPGZYZBZ\",\"summary\":\"The Computed tomography (CT) Image data was obtained on patients diagnosed with Non-Small Cell Lung Cancer (NSCLC) with mixed stage & histology from the H. Lee Moffitt Cancer Center and Research Institute. Scans were obtained from patients who underwent surgical resection and had corresponding pre-surgery diagnostic CTs. The scans were de-identified following HIPAA guidelines to protect patient privacy. The data was shared with the QIN collaborators for research purpose complying with collaborative data sharing policy of the H. Lee Moffitt Total Cancer Care (TCC) .About the NCI QINThe mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the Quantitative Imaging Network Collections page. Interested investigators can apply to the QIN at: Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01) PAR-11-150.\",\"image\":false,\"program\":\"QIN\",\"cancer_types\":[\"Non-small Cell Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":47,\"date_updated\":\"2017-07-31\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=QIN%20LUNG%20CT&ThirdPartyCriteria=NO\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/QIN_LUNG_CT_20170731.tcia\",\"blob\":\"qin lung ct qin lung ct the computed tomography (ct) image data was obtained on patients diagnosed with non-small cell lung cancer (nsclc) with mixed stage & histology from the h. lee moffitt cancer center and research institute. scans were obtained from patients who underwent surgical resection and had corresponding pre-surgery diagnostic cts. the scans were de-identified following hipaa guidelines to protect patient privacy. the data was shared with the qin collaborators for research purpose complying with collaborative data sharing policy of the h. lee moffitt total cancer care (tcc) .about the nci qinthe mission of the qin is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. more information is available on the quantitative imaging network collections page. interested investigators can apply to the qin at: quantitative imaging for evaluation of responses to cancer therapies (u01) par-11-150. non-small cell lung cancer lung ct qin\"},{\"id\":43335,\"type\":\"collection\",\"slug\":\"qin-pet-phantom\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/qin-pet-phantom\\\/\",\"title\":\"QIN PET Phantom\",\"short_title\":\"QIN PET Phantom\",\"doi\":\"10.7937\\\/k9\\\/tcia.2015.zpukhckb\",\"summary\":\"This collection consists of positron emission tomography (PET) phantom scans originally utilized by the Quantitative Imaging Network (QIN) PET Segmentation Challenge to assess the variability of segmentations and subsequently derived quantitative analysis results on phantom PET scans with known ground truth.The phantom was provided by Dr. Sunderland at the University of Iowa (supported by grant R01CA169072 - Harmonized PET Reconstructions for Cancer Clinical Trials) and is based on the NEMA IEC Body Phantom SetTM (Model PET\\\/IEC-BODY\\\/P) with a set of 6 custom made (via rapid prototyping) spheres & ellipses (Fig. 1). The phantom was scanned at two QIN sites (University of Iowa and University of Washington) with different scanners, following a protocol that yields four image sets (Fig. 2) per site. The DICOM PET images are organized as shown in Fig. 3. \\u00a0These figures are located in the Detailed Description tab below.\\n\\nAbout the NCI QIN\\nThe mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the\\u00a0Quantitative Imaging Network Collections\\u00a0page. Interested investigators can apply to the QIN at:\\u00a0Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01) PAR-11-150.\",\"image\":false,\"program\":\"QIN\",\"cancer_types\":[\"Phantom\"],\"cancer_locations\":[\"Phantom\"],\"data_types\":[\"PT\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":2,\"date_updated\":\"2014-09-04\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=QIN%20PET%20Phantom\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_QIN_PET_Phantom_06-22-2015.tcia\",\"blob\":\"qin pet phantom qin pet phantom this collection consists of positron emission tomography (pet) phantom scans originally utilized by the quantitative imaging network (qin) pet segmentation challenge to assess the variability of segmentations and subsequently derived quantitative analysis results on phantom pet scans with known ground truth.the phantom was provided by dr. sunderland at the university of iowa (supported by grant r01ca169072 - harmonized pet reconstructions for cancer clinical trials) and is based on the nema iec body phantom settm (model pet\\\/iec-body\\\/p) with a set of 6 custom made (via rapid prototyping) spheres & ellipses (fig. 1). the phantom was scanned at two qin sites (university of iowa and university of washington) with different scanners, following a protocol that yields four image sets (fig. 2) per site. the dicom pet images are organized as shown in fig. 3. \\u00a0these figures are located in the detailed description tab below.\\n\\nabout the nci qin\\nthe mission of the qin is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. more information is available on the\\u00a0quantitative imaging network collections\\u00a0page. interested investigators can apply to the qin at:\\u00a0quantitative imaging for evaluation of responses to cancer therapies (u01) par-11-150. phantom phantom pt other qin\"},{\"id\":43345,\"type\":\"collection\",\"slug\":\"qin-prostate\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/qin-prostate\\\/\",\"title\":\"QIN PROSTATE\",\"short_title\":\"QIN PROSTATE\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.fADs26kG\",\"summary\":\"This collection contains multiparametric MRI images collected for the purposes of detection and\\\/or staging of prostate cancer. The MRI parameters include T1- and T2-weighted sequences as well as Diffusion Weighted and Dynamic Contrast-Enhanced MRI. The images were obtained using endorectal and phased array surface coils at 3.0T (GE Signa HDx 15.0) The value of this collection is to provide clinical image data for the development and evaluation of quantitative methods for prostate cancer characterization using multiparametric MRI.\\u00a0 Data was provided by Brigham and Women's Hospital, PI Dr. Fiona Fennessy.MR imaging exam was performed on a GE Signa HDx 3.0\\u00a0T magnet (GE Healthcare, Waukesha, WI) using a combination of 8-channel abdominal array and endorectal coil (Medrad, Pittsburgh, PA). The MR sequences included\\u00a0T1- and\\u00a0T2-weighted imaging, diffusion weighted (DW) imaging, and DCE MRI.\\u00a0T1-weighted imaging was performed with a spoiled gradient recalled echo (SPGR) sequence with TR\\\/TE\\\/\\u03b1\\u00a0=\\u00a0385\\u00a0ms\\\/6.2\\u00a0ms\\\/65\\u00b0 over a (16\\u00a0cm)2\\u00a0field of view (FOV).\\u00a0T2-weighted imaging was performed with a FRFSE (Fast Recovery Fast Spin Echo) sequence with TR\\\/TE\\u00a0=\\u00a03500\\\/102\\u00a0ms, FOV\\u00a0=\\u00a0(16\\u00a0cm)2. A DW echo planar imaging sequence with trace diffusion sensitization and\\u00a0b-values of 0 and 500\\u00a0s\\\/mm2, and TR\\\/TE\\u00a0=\\u00a02500\\\/65\\u00a0ms provided data for an Apparent Diffusion Coefficient (ADC) map. Finally, DCE MRI utilized a 3D SPGR sequence with TR\\\/TE\\\/\\u03b1\\u00a0=\\u00a03.6\\u00a0ms\\\/1.3\\u00a0ms\\\/15\\u00b0, FOV\\u00a0=\\u00a0(26\\u00a0cm)2, with full gland coverage and reconstructed image voxel size of 1\\u00d71\\u00d76\\u00a0mm (interpolated to 256\\u00d7256 matrix). DCE MRI frames were acquired at approximately 5\\u00a0s intervals (the number of frames varied between 12 and 16 slices resulting in the time resolution between 4.4 and 5.3\\u00a0seconds) to achieve a clinically appropriate compromise between spatial and temporal resolutions. Gadopentetate dimeglumine (Magnevist, Berlex Laboratories, Wayne, New Jersey) was injected intravenously using a syringe pump (0.15\\u00a0mmol\\\/kg) at the rate of 3\\u00a0ml\\\/s followed by 20\\u00a0ml saline flush at the same rate. The protocol included ~\\u00a05 baseline scans prior to contrast injection for estimation of baseline tissue properties.About the NCI QINThe mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the Quantitative Imaging Network Collections page. Interested investigators can apply to the QIN at: Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01) PAR-11-150.\",\"image\":false,\"program\":\"QIN\",\"cancer_types\":[\"Prostate Cancer\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"MR\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":22,\"date_updated\":\"2014-07-02\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=QIN%20PROSTATE\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/QIN-Prostate_2014-07-02.tcia\",\"blob\":\"qin prostate qin prostate this collection contains multiparametric mri images collected for the purposes of detection and\\\/or staging of prostate cancer. the mri parameters include t1- and t2-weighted sequences as well as diffusion weighted and dynamic contrast-enhanced mri. the images were obtained using endorectal and phased array surface coils at 3.0t (ge signa hdx 15.0) the value of this collection is to provide clinical image data for the development and evaluation of quantitative methods for prostate cancer characterization using multiparametric mri.\\u00a0 data was provided by brigham and women's hospital, pi dr. fiona fennessy.mr imaging exam was performed on a ge signa hdx 3.0\\u00a0t magnet (ge healthcare, waukesha, wi) using a combination of 8-channel abdominal array and endorectal coil (medrad, pittsburgh, pa). the mr sequences included\\u00a0t1- and\\u00a0t2-weighted imaging, diffusion weighted (dw) imaging, and dce mri.\\u00a0t1-weighted imaging was performed with a spoiled gradient recalled echo (spgr) sequence with tr\\\/te\\\/\\u03b1\\u00a0=\\u00a0385\\u00a0ms\\\/6.2\\u00a0ms\\\/65\\u00b0 over a (16\\u00a0cm)2\\u00a0field of view (fov).\\u00a0t2-weighted imaging was performed with a frfse (fast recovery fast spin echo) sequence with tr\\\/te\\u00a0=\\u00a03500\\\/102\\u00a0ms, fov\\u00a0=\\u00a0(16\\u00a0cm)2. a dw echo planar imaging sequence with trace diffusion sensitization and\\u00a0b-values of 0 and 500\\u00a0s\\\/mm2, and tr\\\/te\\u00a0=\\u00a02500\\\/65\\u00a0ms provided data for an apparent diffusion coefficient (adc) map. finally, dce mri utilized a 3d spgr sequence with tr\\\/te\\\/\\u03b1\\u00a0=\\u00a03.6\\u00a0ms\\\/1.3\\u00a0ms\\\/15\\u00b0, fov\\u00a0=\\u00a0(26\\u00a0cm)2, with full gland coverage and reconstructed image voxel size of 1\\u00d71\\u00d76\\u00a0mm (interpolated to 256\\u00d7256 matrix). dce mri frames were acquired at approximately 5\\u00a0s intervals (the number of frames varied between 12 and 16 slices resulting in the time resolution between 4.4 and 5.3\\u00a0seconds) to achieve a clinically appropriate compromise between spatial and temporal resolutions. gadopentetate dimeglumine (magnevist, berlex laboratories, wayne, new jersey) was injected intravenously using a syringe pump (0.15\\u00a0mmol\\\/kg) at the rate of 3\\u00a0ml\\\/s followed by 20\\u00a0ml saline flush at the same rate. the protocol included ~\\u00a05 baseline scans prior to contrast injection for estimation of baseline tissue properties.about the nci qinthe mission of the qin is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. more information is available on the quantitative imaging network collections page. interested investigators can apply to the qin at: quantitative imaging for evaluation of responses to cancer therapies (u01) par-11-150. prostate cancer prostate mr qin\"},{\"id\":43365,\"type\":\"collection\",\"slug\":\"qin-prostate-repeatability\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/qin-prostate-repeatability\\\/\",\"title\":\"QIN-PROSTATE-Repeatability\",\"short_title\":\"QIN-PROSTATE-Repeatability\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2018.MR1CKGND\",\"summary\":\"This is a dataset with multiparametric prostate MRI applied in a test-retest setting, allowing to evaluate repeatability of the MRI-based measurements in the prostate. There is very limited data about the repeatability in mpMRI of the prostate, while such information is critical for establishing technical characteristics of mpMRI as imaging biomarker of prostate cancer. Data was provided by the Brigham and Women's Hospital team. Data collection was supported by U01 CA151261 (PI Fiona Fennessy). Preparation of data for public sharing was supported by U24 CA180918 (http:\\\/\\\/qiicr.org) (MPI Andrey Fedorov and Ron Kikinis).\\nType of cancer: Confirmed or suspected prostate cancer\\nAcquisition Protocol: Standard prostate mpMRI protocol implemented at Brigham and Women's Hospital was used in this study. For a given patient, we aimed to maintain similar protocol settings, and used the same scanner hardware and software configurations for both the baseline and repeat examinations, which were acquired within 2 weeks of time. All of the imaging studies were acquired at 3 Tesla magnet strength.\\u00a0 Due to the scanner hardware upgrade in the middle of the study, 6 of the patients had baseline and repeat study performed on a GE Signa HDxt platform, software release 15.0_M4A_097.a, while the remaining 7 patients were scanned on a GE Discovery MR750w, software release DV24.0_R01_1344 (General Electric Healthcare, Milwaukee, WI). Transrectal coil within an air-filled balloon (Medrad Inc., Warrendale, PA) was used in all imaging studies. mpMRI protocol included T2-weighted, Diffusion Weighted (DW) (b-values of 0 and 1400 mm\\\/s2) and Dynamic Contrast Enhanced (DCE) sequences. Detailed acquisition parameters are listed in Table 1 of [1]. DWI Apparent Diffusion Coefficient (ADC) and DCE subtract maps (further referred to as SUB; computed as the difference between the phase corresponding to the contrast bolus arrival and the baseline phase) were generated using the scanner software. \\nThe imaging data is accompanied by the following types of derived data:\\nmanual segmentations of the total prostate gland, peripheral zone of the prostate gland, suspected tumor and normal regions (where applicable). Segmentations were done by a radiologist with the expertise in prostate MRIvolume measurements (for axial T2w images and ADC images) and mean ADC (for ADC images) corresponding to the segmented regions.\\nBoth segmentations and segmentation-based measurements are stored as DICOM objects (DICOM Segmentation images and DICOM Structured Reports that follow DICOM SR TID 1500). For the details about data representation and tools available to convert and visualize the data see [2].\\nIn the future we plan to augment this dataset with the parametric\\u00a0maps obtained using that analysis (in DICOM), and potentially (pending\\u00a0IRB clearance) clinical data (demographics, PSA), pathology sampling\\u00a0data (biopsy Gleason score) and results of PI-RADS interpretation.\\nReferences:\\n[1] Fedorov A, Vangel MG, Tempany CM, Fennessy FM. Multiparametric\\u00a0Magnetic Resonance Imaging of the Prostate: Repeatability of Volume\\u00a0and Apparent Diffusion Coefficient Quantification. Investigative\\u00a0Radiology. 52, 538\\u2013546 (2017). DOI:\\u00a0\\u00a010.1097\\\/RLI.0000000000000382\\n[2] Fedorov, A., Schwier, M., Clunie, D., Herz, C., Pieper, S.,\\u00a0Kikinis,R., Tempany, C. & Fennessy, F. An annotated test-retest\\u00a0collection of prostate multiparametric MRI. Scientific Data 5, 180281\\u00a0(2018). DOI:\\u00a010.1038\\\/sdata.2018.281\\nAbout the NCI QIN\\nThe mission of the QIN is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. More information is available on the Quantitative Imaging Network Collections page. Interested investigators can apply to the QIN at: Quantitative Imaging for Evaluation of Responses to Cancer Therapies (U01) PAR-11-150.\",\"image\":false,\"program\":\"QIN\",\"cancer_types\":[\"Prostate Cancer\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"SEG\",\"MR\",\"SR\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":15,\"date_updated\":\"2020-11-09\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=QIN-PROSTATE-Repeatability\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/QIN-PROSTATE-Repeatability_v2_20180510.tcia\",\"blob\":\"qin-prostate-repeatability qin-prostate-repeatability this is a dataset with multiparametric prostate mri applied in a test-retest setting, allowing to evaluate repeatability of the mri-based measurements in the prostate. there is very limited data about the repeatability in mpmri of the prostate, while such information is critical for establishing technical characteristics of mpmri as imaging biomarker of prostate cancer. data was provided by the brigham and women's hospital team. data collection was supported by u01 ca151261 (pi fiona fennessy). preparation of data for public sharing was supported by u24 ca180918 (http:\\\/\\\/qiicr.org) (mpi andrey fedorov and ron kikinis).\\ntype of cancer: confirmed or suspected prostate cancer\\nacquisition protocol: standard prostate mpmri protocol implemented at brigham and women's hospital was used in this study. for a given patient, we aimed to maintain similar protocol settings, and used the same scanner hardware and software configurations for both the baseline and repeat examinations, which were acquired within 2 weeks of time. all of the imaging studies were acquired at 3 tesla magnet strength.\\u00a0 due to the scanner hardware upgrade in the middle of the study, 6 of the patients had baseline and repeat study performed on a ge signa hdxt platform, software release 15.0_m4a_097.a, while the remaining 7 patients were scanned on a ge discovery mr750w, software release dv24.0_r01_1344 (general electric healthcare, milwaukee, wi). transrectal coil within an air-filled balloon (medrad inc., warrendale, pa) was used in all imaging studies. mpmri protocol included t2-weighted, diffusion weighted (dw) (b-values of 0 and 1400 mm\\\/s2) and dynamic contrast enhanced (dce) sequences. detailed acquisition parameters are listed in table 1 of [1]. dwi apparent diffusion coefficient (adc) and dce subtract maps (further referred to as sub; computed as the difference between the phase corresponding to the contrast bolus arrival and the baseline phase) were generated using the scanner software. \\nthe imaging data is accompanied by the following types of derived data:\\nmanual segmentations of the total prostate gland, peripheral zone of the prostate gland, suspected tumor and normal regions (where applicable). segmentations were done by a radiologist with the expertise in prostate mrivolume measurements (for axial t2w images and adc images) and mean adc (for adc images) corresponding to the segmented regions.\\nboth segmentations and segmentation-based measurements are stored as dicom objects (dicom segmentation images and dicom structured reports that follow dicom sr tid 1500). for the details about data representation and tools available to convert and visualize the data see [2].\\nin the future we plan to augment this dataset with the parametric\\u00a0maps obtained using that analysis (in dicom), and potentially (pending\\u00a0irb clearance) clinical data (demographics, psa), pathology sampling\\u00a0data (biopsy gleason score) and results of pi-rads interpretation.\\nreferences:\\n[1] fedorov a, vangel mg, tempany cm, fennessy fm. multiparametric\\u00a0magnetic resonance imaging of the prostate: repeatability of volume\\u00a0and apparent diffusion coefficient quantification. investigative\\u00a0radiology. 52, 538\\u2013546 (2017). doi:\\u00a0\\u00a010.1097\\\/rli.0000000000000382\\n[2] fedorov, a., schwier, m., clunie, d., herz, c., pieper, s.,\\u00a0kikinis,r., tempany, c. & fennessy, f. an annotated test-retest\\u00a0collection of prostate multiparametric mri. scientific data 5, 180281\\u00a0(2018). doi:\\u00a010.1038\\\/sdata.2018.281\\nabout the nci qin\\nthe mission of the qin is to improve the role of quantitative imaging for clinical decision making in oncology by developing and validating data acquisition, analysis methods, and tools to tailor treatment for individual patients and predict or monitor the response to drug or radiation therapy. more information is available on the quantitative imaging network collections page. interested investigators can apply to the qin at: quantitative imaging for evaluation of responses to cancer therapies (u01) par-11-150. prostate cancer prostate seg mr sr image analyses qin\"},{\"id\":43375,\"type\":\"collection\",\"slug\":\"qin-sarcoma\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/qin-sarcoma\\\/\",\"title\":\"QIN-SARCOMA\",\"short_title\":\"QIN-SARCOMA\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.fXL9sESs\",\"summary\":\"This collection of soft-tissue sarcoma dynamic contrast-enhanced (DCE) MRI data contains images from a longitudinal study to assess soft-tissue sarcoma response to preoperative chemoradiation treatment. Images were acquired at three time points: prior to the start of treatment (Visit 1, V1), after the first cycle of chemotherapy (Visit 2, V2), and after ~ 8 more weeks of chemoradiation (prior to surgery) (Visit 3, V3).\\u00a0 Not every patient was able to complete all three MRI studies. The value of this collection is to provide clinical imaging data for the development and validation of quantitative imaging methods for assessment of soft-tissue sarcoma response to preoperative treatment.\\u00a0 Initial findings of this study have been published and the data is provided by Oregon Health & Science University, PI Dr. Wei Huang.The MRI data consist of DCE-MRI images only, which were acquired using a Siemens 3T TIM Trio system with the body coil as the transmitter and a body matrix phased array (combined with a spine matrix phased array) coil as the receiver. Following scout and axial T2-weighted MRI, a RF-spoiled gradient-echo sequence was used to acquire sagittal DCE-MRI images covering the entire tumor, with 100 flip angle, TE\\\/TR = 1.5\\\/6.0 ms, 24-26 cm field of view (FOV), and 5 mm slice thickness with 1 mm gap. A parallel imaging acceleration factor of 2 was used for DCE-MRI, resulting in 7-16 s temporal resolutions depending on tumor size. The total DCE acquisition time was approximately 10 min with gadolinium contrast agent (Prohance\\u00ae) IV injection (0.1 mmol\\\/kg at 2 mL\\\/s) carried out following acquisition of five baseline image volumes, \\u00a0followed by a 20-mL saline flush.\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\",\"image\":false,\"program\":\"QIN\",\"cancer_types\":[\"Sarcomas\"],\"cancer_locations\":[\"Breast\",\"Calf\",\"Chest\",\"Elbow\",\"Knee\",\"Leg\",\"Shoulder\",\"Thigh\"],\"data_types\":[\"MR\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":15,\"date_updated\":\"2014-09-04\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=QIN-SARCOMA\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/QIN-SARCOMA_2014-09-04.tcia\",\"blob\":\"qin-sarcoma qin-sarcoma this collection of soft-tissue sarcoma dynamic contrast-enhanced (dce) mri data contains images from a longitudinal study to assess soft-tissue sarcoma response to preoperative chemoradiation treatment. images were acquired at three time points: prior to the start of treatment (visit 1, v1), after the first cycle of chemotherapy (visit 2, v2), and after ~ 8 more weeks of chemoradiation (prior to surgery) (visit 3, v3).\\u00a0 not every patient was able to complete all three mri studies. the value of this collection is to provide clinical imaging data for the development and validation of quantitative imaging methods for assessment of soft-tissue sarcoma response to preoperative treatment.\\u00a0 initial findings of this study have been published and the data is provided by oregon health & science university, pi dr. wei huang.the mri data consist of dce-mri images only, which were acquired using a siemens 3t tim trio system with the body coil as the transmitter and a body matrix phased array (combined with a spine matrix phased array) coil as the receiver. following scout and axial t2-weighted mri, a rf-spoiled gradient-echo sequence was used to acquire sagittal dce-mri images covering the entire tumor, with 100 flip angle, te\\\/tr = 1.5\\\/6.0 ms, 24-26 cm field of view (fov), and 5 mm slice thickness with 1 mm gap. a parallel imaging acceleration factor of 2 was used for dce-mri, resulting in 7-16 s temporal resolutions depending on tumor size. the total dce acquisition time was approximately 10 min with gadolinium contrast agent (prohance\\u00ae) iv injection (0.1 mmol\\\/kg at 2 ml\\\/s) carried out following acquisition of five baseline image volumes, \\u00a0followed by a 20-ml saline flush.\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0\\u00a0 sarcomas breast calf chest elbow knee leg shoulder thigh mr qin\"},{\"id\":41883,\"type\":\"collection\",\"slug\":\"radcure\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/radcure\\\/\",\"title\":\"Computed Tomography Images from Large Head and Neck Cohort\",\"short_title\":\"RADCURE\",\"doi\":\"10.7937\\\/J47W-NM11\",\"summary\":\"The RADCURE dataset was collected clinically for radiation therapy treatment planning and retrospectively reconstructed for quantitative imaging research.\\u00a0\\u00a0Inclusion: The dataset used for this study consists of 3,346 head and neck cancer CT image volumes collected from 2005-2017 treated with definitive RT at the University Health Network (UHN) in Toronto, CanadaAcquisition and Validation Methods: RADCURE contains computed tomography (CT) images with corresponding normal and non-normal tissue contours. CT scans were collected using systems from three different manufacturers. Standard clinical imaging protocols were followed, and contours were generated and reviewed at weekly quality assurance rounds. RADCURE imaging and structure set data was extracted from our institution\\u2019s radiation treatment planning and oncology systems using an in-house data mining and processing system. Furthermore, images are linked to clinical data for each patient and include demographic, clinical and treatment information based on the 7th edition TNM staging system. The median patient age is 63, with the final dataset including 80% males. Oropharyngeal cancer makes up 50% of the population with larynx, nasopharynx, and hypopharynx cancer, comprising 25, 12, and 5% respectively. Median follow-up was 5 years with 60% of the patients alive at last follow-up. \\u00a0\\u00a0Data Format and Usage Notes: During extraction of images and contours from our institution\\u2019s radiation treatment planning and oncology systems, the data was converted to DICOM and RTSTRUCT formats, respectively. To improve the usability of the RTSTRUCT files, individual contour names were standardized for primary tumor volumes and 19 organs-at-risk. Demographic, clinical, and treatment information is provided as a comma-separated values (csv) file. This dataset is a superset of the Radiomic Biomarkers in Oropharyngeal Carcinoma (OPC-Radiomics) dataset and fully encapsulates all previous data; this dataset replaces the OPC-Radiomics dataset. The RTSTRUCTs from OPC-Radiomics have been standardized to adhere to the TG263 nomenclature.\\u00a0Age of 90 years or greater is considered PHI and set to 90 years to minimize impact to privacy. Both radiological and clinical metadata were offset by an undisclosed number of days for anonymization and should be noted for downstream analysis.\\u00a0The TG263-standardized RTSTRUCTs include only the GTVp (primary gross tumor volume) contours. Patients without corresponding GTVp contours will not have RTSTRUCTs.Potential Applications:\\u00a0The availability of imaging, clinical, demographic and treatment data in RADCURE makes it a viable option for a variety of quantitative image analysis research initiatives. This includes the application of machine learning or artificial intelligence methods to expedite routine clinical practices, discover new non-invasive biomarkers, or develop prognostic models. \\u00a0\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Oropharyngeal Cancer\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"CT\",\"RTSTRUCT\",\"Demographic\",\"Diagnosis\",\"Exposure\",\"Follow-Up\",\"Treatment\",\"Other\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":3346,\"date_updated\":\"2024-12-19\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?saved-cart=nbia-66941734617657304\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/RADCURE_v04_20241219.tcia\",\"blob\":\"computed tomography images from large head and neck cohort radcure the radcure dataset was collected clinically for radiation therapy treatment planning and retrospectively reconstructed for quantitative imaging research.\\u00a0\\u00a0inclusion: the dataset used for this study consists of 3,346 head and neck cancer ct image volumes collected from 2005-2017 treated with definitive rt at the university health network (uhn) in toronto, canadaacquisition and validation methods: radcure contains computed tomography (ct) images with corresponding normal and non-normal tissue contours. ct scans were collected using systems from three different manufacturers. standard clinical imaging protocols were followed, and contours were generated and reviewed at weekly quality assurance rounds. radcure imaging and structure set data was extracted from our institution\\u2019s radiation treatment planning and oncology systems using an in-house data mining and processing system. furthermore, images are linked to clinical data for each patient and include demographic, clinical and treatment information based on the 7th edition tnm staging system. the median patient age is 63, with the final dataset including 80% males. oropharyngeal cancer makes up 50% of the population with larynx, nasopharynx, and hypopharynx cancer, comprising 25, 12, and 5% respectively. median follow-up was 5 years with 60% of the patients alive at last follow-up. \\u00a0\\u00a0data format and usage notes: during extraction of images and contours from our institution\\u2019s radiation treatment planning and oncology systems, the data was converted to dicom and rtstruct formats, respectively. to improve the usability of the rtstruct files, individual contour names were standardized for primary tumor volumes and 19 organs-at-risk. demographic, clinical, and treatment information is provided as a comma-separated values (csv) file. this dataset is a superset of the radiomic biomarkers in oropharyngeal carcinoma (opc-radiomics) dataset and fully encapsulates all previous data; this dataset replaces the opc-radiomics dataset. the rtstructs from opc-radiomics have been standardized to adhere to the tg263 nomenclature.\\u00a0age of 90 years or greater is considered phi and set to 90 years to minimize impact to privacy. both radiological and clinical metadata were offset by an undisclosed number of days for anonymization and should be noted for downstream analysis.\\u00a0the tg263-standardized rtstructs include only the gtvp (primary gross tumor volume) contours. patients without corresponding gtvp contours will not have rtstructs.potential applications:\\u00a0the availability of imaging, clinical, demographic and treatment data in radcure makes it a viable option for a variety of quantitative image analysis research initiatives. this includes the application of machine learning or artificial intelligence methods to expedite routine clinical practices, discover new non-invasive biomarkers, or develop prognostic models. \\u00a0 oropharyngeal cancer head-neck ct rtstruct demographic diagnosis exposure follow-up treatment other clinical community\"},{\"id\":43429,\"type\":\"collection\",\"slug\":\"rembrandt\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/rembrandt\\\/\",\"title\":\"REMBRANDT\",\"short_title\":\"REMBRANDT\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.588OZUZB\",\"summary\":\"Finding better therapies for the treatment of brain tumors is hampered by the lack of consistently obtained molecular data in a large sample set and the ability to integrate biomedical data from disparate sources enabling translation of therapies from bench to bedside. Hence, a critical factor in the advancement of biomedical research and clinical translation is the ease with which data can be integrated, redistributed, and analyzed both within and across functional domains. Novel biomedical informatics infrastructure and tools are essential for developing individualized patient treatment based on the specific genomic signatures in each patient's tumor. The Repository of Molecular Brain Neoplasia Data (REMBRANDT) is aimed at facilitating discovery by connecting the dots between clinical information and genomic characterization data.REMBRANDT contains data generated through the Glioma Molecular Diagnostic Initiative from 874 glioma specimens comprising approximately 566 gene expression arrays, 834 copy number arrays, and 13,472 clinical phenotype data points. These data are currently housed in  Georgetown University's G-DOC System  and are described in a  related manuscript . \\u00a0This image collection was created as a companion data set to augment the larger\\u00a0REMBRANDT\\u00a0project. It contains the pre-surgical magnetic resonance (MR) multi-sequence images from 130 REMBRANDT patients.\\u00a0\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Low & High Grade Glioma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"Demographic\",\"Diagnosis\",\"Follow-Up\",\"Classification\",\"Treatment\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":130,\"date_updated\":\"2021-08-17\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=REMBRANDT\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_REMBRANDT_06-22-2015.tcia\",\"blob\":\"rembrandt rembrandt finding better therapies for the treatment of brain tumors is hampered by the lack of consistently obtained molecular data in a large sample set and the ability to integrate biomedical data from disparate sources enabling translation of therapies from bench to bedside. hence, a critical factor in the advancement of biomedical research and clinical translation is the ease with which data can be integrated, redistributed, and analyzed both within and across functional domains. novel biomedical informatics infrastructure and tools are essential for developing individualized patient treatment based on the specific genomic signatures in each patient's tumor. the repository of molecular brain neoplasia data (rembrandt) is aimed at facilitating discovery by connecting the dots between clinical information and genomic characterization data.rembrandt contains data generated through the glioma molecular diagnostic initiative from 874 glioma specimens comprising approximately 566 gene expression arrays, 834 copy number arrays, and 13,472 clinical phenotype data points. these data are currently housed in  georgetown university's g-doc system  and are described in a  related manuscript . \\u00a0this image collection was created as a companion data set to augment the larger\\u00a0rembrandt\\u00a0project. it contains the pre-surgical magnetic resonance (mr) multi-sequence images from 130 rembrandt patients.\\u00a0 low & high grade glioma brain mr demographic diagnosis follow-up classification treatment clinical genomics image analyses community\"},{\"id\":43729,\"type\":\"collection\",\"slug\":\"remind\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/remind\\\/\",\"title\":\"The Brain Resection Multimodal Imaging Database\",\"short_title\":\"ReMIND\",\"doi\":\"10.7937\\\/3RAG-D070\",\"summary\":\"Introduction: The Brain Resection Multimodal Imaging Database (ReMIND) contains pre- and intra-operative data collected on 114 consecutive patients who were surgically treated with image-guided tumor resection between 2018 and 2022. For all patients, preoperative MRI, 3D intraoperative ultrasound series, and intraoperative MRI are available. Additionally, each case typically contains segmentations, including the preoperative tumor, the pre-resection cerebrum, the previous resection cavity derived from the preoperative MRI (if applicable), and any residual tumor identified on the intraoperative MRI. In total, this collection contains 369 preoperative MRI series, 320 3D intraoperative ultrasound series, 301 intraoperative MRI series, and 356 segmentations. We expect this data to be a resource for computational research in brain shift and image analysis as well as for neurosurgical training in the interpretation of intraoperative ultrasound and intraoperative MRI.Imaging data:Preoperative MRI comprises four structural MRI sequences: native T1-weighted (T1), contrast-enhanced T1-weighted (ceT1), native T2-weighted (T2), and T2-weighted fluid-attenuated inversion recovery (T2-FLAIR). These scans were acquired before surgery using various scanners at multiple institutions, making their acquisition parameters heterogeneous. Unlike preoperative MRI, all intraoperative MRI were acquired using a 3T wide-bore (70 cm) MRI scanner. All iUS series were acquired using a tracked 2D neuro-cranial curvilinear transducer. The transducer was swept unidirectionally through the craniotomy at a slow, consistent speed. This specific motion, in conjunction with the tracking, enabled the reconstruction of a 3D volume from the tracked 2D sweeps.Segmentation data:Various segmentations were created to assist the surgical resection. These include manual segmentations of the preoperative whole tumor, preoperative tumor target (i.e., the radiologically identifiable tumor specifically targeted for resection), resection cavity resulting from prior surgery (i.e., in case of reoperation), intraoperative residual tumor, and the automatic segmentations of cerebrum and ventricles (Brainlab AG, Munich, Germany). Only structures deemed necessary for the surgical resection by the attending neurosurgeon were segmented. Specifically, segmentations of the manual preoperative whole tumor (113 cases), preoperative tumor target segmentations (3 cases), manual previous resection cavity segmentations (21 cases), residual tumor segmentations (58 cases), and automated segmentations of the cerebrum (89 cases) and ventricles (54 cases). All cerebrum, ventricle, and tumor segmentations were created preoperatively during the surgical planning stage. In contrast, residual tumor segmentations were created intraoperatively from iMRI.Clinical metadata:Demographic information, including age, sex, and ethnicity, was obtained from the corresponding patient medical records. The age range of the included population was 20\\u201376. The ratio of male:female was equal to 61:53. Moreover, clinico-pathologic data such as the tumor type, tumor grade, radiological characteristics upon contrast administration, tumor location, and the reoperation status were assessed by the treating neurosurgeons. Tumor type and grade were specified according to the World Health Organization (WHO) 2021 Classification of Tumors of the Central Nervous System. Additionally, tumors were classified into one of 3 categories based on proximity to the functional cortex (non-eloquent, near eloquent, and eloquent).\\u00a0Pre-processing:The MRI and ultrasound images are provided in DICOM format. Segmentation files are provided in NRRD format (original format) and DICOM SEG (converted from NRRD). Data was converted from NRRD format to DICOM format\\u00a0using\\u00a03D Slicer\\u00a0(MR data),\\u00a0dicom3tools\\u00a0software (iUS data) and\\u00a0dcmqi (segmentation data).\\u00a0All MRI images were defaced using automatic affine registration or manual landmark registration with\\u00a0NiftyReg and the template and face mask provided\\u00a0in pydeface. The\\u00a0code of the algorithm is publicly available.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ReMIND_WIKI_image_V2.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Brain Cancer\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"SEG\",\"US\",\"Segmentation\",\"Demographic\",\"Histopathology\",\"Molecular Test\",\"Follow-Up\",\"Classification\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":114,\"date_updated\":\"2023-09-26\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=ReMIND\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ReMIND-Manifest-Sept-2023.tcia\",\"blob\":\"the brain resection multimodal imaging database remind introduction: the brain resection multimodal imaging database (remind) contains pre- and intra-operative data collected on 114 consecutive patients who were surgically treated with image-guided tumor resection between 2018 and 2022. for all patients, preoperative mri, 3d intraoperative ultrasound series, and intraoperative mri are available. additionally, each case typically contains segmentations, including the preoperative tumor, the pre-resection cerebrum, the previous resection cavity derived from the preoperative mri (if applicable), and any residual tumor identified on the intraoperative mri. in total, this collection contains 369 preoperative mri series, 320 3d intraoperative ultrasound series, 301 intraoperative mri series, and 356 segmentations. we expect this data to be a resource for computational research in brain shift and image analysis as well as for neurosurgical training in the interpretation of intraoperative ultrasound and intraoperative mri.imaging data:preoperative mri comprises four structural mri sequences: native t1-weighted (t1), contrast-enhanced t1-weighted (cet1), native t2-weighted (t2), and t2-weighted fluid-attenuated inversion recovery (t2-flair). these scans were acquired before surgery using various scanners at multiple institutions, making their acquisition parameters heterogeneous. unlike preoperative mri, all intraoperative mri were acquired using a 3t wide-bore (70 cm) mri scanner. all ius series were acquired using a tracked 2d neuro-cranial curvilinear transducer. the transducer was swept unidirectionally through the craniotomy at a slow, consistent speed. this specific motion, in conjunction with the tracking, enabled the reconstruction of a 3d volume from the tracked 2d sweeps.segmentation data:various segmentations were created to assist the surgical resection. these include manual segmentations of the preoperative whole tumor, preoperative tumor target (i.e., the radiologically identifiable tumor specifically targeted for resection), resection cavity resulting from prior surgery (i.e., in case of reoperation), intraoperative residual tumor, and the automatic segmentations of cerebrum and ventricles (brainlab ag, munich, germany). only structures deemed necessary for the surgical resection by the attending neurosurgeon were segmented. specifically, segmentations of the manual preoperative whole tumor (113 cases), preoperative tumor target segmentations (3 cases), manual previous resection cavity segmentations (21 cases), residual tumor segmentations (58 cases), and automated segmentations of the cerebrum (89 cases) and ventricles (54 cases). all cerebrum, ventricle, and tumor segmentations were created preoperatively during the surgical planning stage. in contrast, residual tumor segmentations were created intraoperatively from imri.clinical metadata:demographic information, including age, sex, and ethnicity, was obtained from the corresponding patient medical records. the age range of the included population was 20\\u201376. the ratio of male:female was equal to 61:53. moreover, clinico-pathologic data such as the tumor type, tumor grade, radiological characteristics upon contrast administration, tumor location, and the reoperation status were assessed by the treating neurosurgeons. tumor type and grade were specified according to the world health organization (who) 2021 classification of tumors of the central nervous system. additionally, tumors were classified into one of 3 categories based on proximity to the functional cortex (non-eloquent, near eloquent, and eloquent).\\u00a0pre-processing:the mri and ultrasound images are provided in dicom format. segmentation files are provided in nrrd format (original format) and dicom seg (converted from nrrd). data was converted from nrrd format to dicom format\\u00a0using\\u00a03d slicer\\u00a0(mr data),\\u00a0dicom3tools\\u00a0software (ius data) and\\u00a0dcmqi (segmentation data).\\u00a0all mri images were defaced using automatic affine registration or manual landmark registration with\\u00a0niftyreg and the template and face mask provided\\u00a0in pydeface. the\\u00a0code of the algorithm is publicly available.\\u00a0 brain cancer brain mr seg us segmentation demographic histopathology molecular test follow-up classification clinical community\"},{\"id\":45351,\"type\":\"collection\",\"slug\":\"rhuh-gbm\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/rhuh-gbm\\\/\",\"title\":\"The R\\u00edo Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans\",\"short_title\":\"RHUH-GBM\",\"doi\":\"10.7937\\\/4545-c905\",\"summary\":\"\\u00a0This data collection consists of multiparametric MRI scans of 40 adult patients with histopathologically confirmed WHO grade 4 astrocytoma, who underwent surgery at the R\\u00edo Hortega University Hospital in Valladolid, Spain, between January 2018 and December 2022. The dataset encompasses 600 MRI series, covering three time points: preoperative, early post-operative (less than 72 hours after surgery), and the follow-up scan, at which recurrence is diagnosed. Patients included in the sample underwent gross total resection (GTR) or near total resection (NTR), defined as having no residual tumor enhancement and an extent of resection of more than 95% of the initial enhancing volume, respectively. The modified Response Assessment in Neuro-Oncology criteria (RANO) were used to define tumor progression.The dataset contains T1-weighted (T1w), T2-weighted (T2w), Fluid Attenuated Inversion Recovery (FLAIR), T1w contrast-enhanced (T1ce) sequences, and diffusion-weighted imaging-derived apparent diffusion coefficient (ADC) maps. It also includes clinical and demographic data, IDH status, treatment information, and volumetric assessment of the extent of the resection. Moreover, the dataset comprises expert-validated segmentations of tumor subregions (e.g., enhancing tumor, necrosis, peritumoral region), generated through computer-aided methods from preoperative, postoperative, and follow-up scans.This dataset is unique in its inclusion of patients who underwent extensive resection of > 95% of the enhancing tumor. It also stands out from other publicly available datasets by providing early postoperative studies and segmentations, filling the gap in preoperative-focused datasets. By making these data publicly available, the scientific community can analyze recurrence patterns in patients who underwent total or near-total resection and develop new registration and segmentation algorithms focused on post-surgical and follow-up studies.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/tcia-rhuh-gbm.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Astrocytoma\"],\"cancer_locations\":[\"Head\"],\"data_types\":[\"MR\",\"Segmentation\",\"Treatment\",\"Demographic\",\"Molecular Test\",\"Measurement\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":40,\"date_updated\":\"2023-06-09\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=RHUH-GBM\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/RHUH-GBM_v1_20230606.tcia\",\"blob\":\"the r\\u00edo hortega university hospital glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence mri scans rhuh-gbm \\u00a0this data collection consists of multiparametric mri scans of 40 adult patients with histopathologically confirmed who grade 4 astrocytoma, who underwent surgery at the r\\u00edo hortega university hospital in valladolid, spain, between january 2018 and december 2022. the dataset encompasses 600 mri series, covering three time points: preoperative, early post-operative (less than 72 hours after surgery), and the follow-up scan, at which recurrence is diagnosed. patients included in the sample underwent gross total resection (gtr) or near total resection (ntr), defined as having no residual tumor enhancement and an extent of resection of more than 95% of the initial enhancing volume, respectively. the modified response assessment in neuro-oncology criteria (rano) were used to define tumor progression.the dataset contains t1-weighted (t1w), t2-weighted (t2w), fluid attenuated inversion recovery (flair), t1w contrast-enhanced (t1ce) sequences, and diffusion-weighted imaging-derived apparent diffusion coefficient (adc) maps. it also includes clinical and demographic data, idh status, treatment information, and volumetric assessment of the extent of the resection. moreover, the dataset comprises expert-validated segmentations of tumor subregions (e.g., enhancing tumor, necrosis, peritumoral region), generated through computer-aided methods from preoperative, postoperative, and follow-up scans.this dataset is unique in its inclusion of patients who underwent extensive resection of > 95% of the enhancing tumor. it also stands out from other publicly available datasets by providing early postoperative studies and segmentations, filling the gap in preoperative-focused datasets. by making these data publicly available, the scientific community can analyze recurrence patterns in patients who underwent total or near-total resection and develop new registration and segmentation algorithms focused on post-surgical and follow-up studies. astrocytoma head mr segmentation treatment demographic molecular test measurement clinical image analyses software\\\/source code community\"},{\"id\":43437,\"type\":\"collection\",\"slug\":\"rider-breast-mri\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/rider-breast-mri\\\/\",\"title\":\"RIDER Breast MRI\",\"short_title\":\"RIDER Breast MRI\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.H1SXNUXL\",\"summary\":\"Ideally a patient\\u2019s response to neoadjuvant chemotherapy could be observed noninvasively, in the first 2-3 weeks of treatment using an imaging to provide feedback related to the effectiveness of the chosen chemotherapy regimen. This capability would permit individuation of patient care by supporting the opportunity to tailor chemotherapy to a each patient\\u2019s response. Functional diffusion mapping (fDM), now called Parametric Response Mapping (PRM) has been proposed as an MRI imaging biomarker for quantifying early brain tumor response to therapy [1-3]. This approach quantifies local apparent diffusion coefficient (ADC) changes in tumors using a voxel-based analysis implemented by rigid registration of the patient\\u2019s head between interval exams. The RIDER Breast MRI data set extended this approach by demonstrating ADC changes in 3 of 5 primary breast cancer patients measured in response to onset of neoadjuvant chemotherapy from interval exams separated by only 8-11 days.\\nThis\\u00a0 ISMRM 2009 poster \\u00a0demonstrates how each of the \\\"coffee break\\\" exams were used as an estimate of each patient's null hypothesis, i.e. distribution associated with no change, and thus supports the estimate of the nulls 97.5 percentile for subsequent estimation of early response to neoadjuvant chemotherapy on an individual patient basis.\\n\\nAbout the RIDER projectThe Reference Image Database to Evaluate Therapy Response (RIDER) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. \\u00a0The National Cancer Institute (NCI) has exercised a series of contracts with specific academic sites for collection of repeat \\\"coffee break,\\\" longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [CT]\\u00a0positron emission tomography [PET]\\u00a0CT, dynamic contrast-enhanced magnetic resonance imaging [DCE MRI], diffusion-weighted [DW] MRI) and organ sites (currently lung, breast, and neuro). The methods for data collection, analysis, and results are described in the new Combined RIDER White Paper Report (Sept 2008):RIDER White Paper: Combined contracts report ( Sept 2008) PDFThe long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. Thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the\\u00a0first RIDER white paper\\u00a0report (2006): RIDER White Paper: Executive Summary PDF   RIDER White Paper: Editorial in Nature.com\",\"image\":false,\"program\":\"RIDER\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MR\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":5,\"date_updated\":\"2011-11-08\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=RIDER%20Breast%20MRI\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/doiJNLP-Fo0H1NtD.tcia\",\"blob\":\"rider breast mri rider breast mri ideally a patient\\u2019s response to neoadjuvant chemotherapy could be observed noninvasively, in the first 2-3 weeks of treatment using an imaging to provide feedback related to the effectiveness of the chosen chemotherapy regimen. this capability would permit individuation of patient care by supporting the opportunity to tailor chemotherapy to a each patient\\u2019s response. functional diffusion mapping (fdm), now called parametric response mapping (prm) has been proposed as an mri imaging biomarker for quantifying early brain tumor response to therapy [1-3]. this approach quantifies local apparent diffusion coefficient (adc) changes in tumors using a voxel-based analysis implemented by rigid registration of the patient\\u2019s head between interval exams. the rider breast mri data set extended this approach by demonstrating adc changes in 3 of 5 primary breast cancer patients measured in response to onset of neoadjuvant chemotherapy from interval exams separated by only 8-11 days.\\nthis\\u00a0 ismrm 2009 poster \\u00a0demonstrates how each of the \\\"coffee break\\\" exams were used as an estimate of each patient's null hypothesis, i.e. distribution associated with no change, and thus supports the estimate of the nulls 97.5 percentile for subsequent estimation of early response to neoadjuvant chemotherapy on an individual patient basis.\\n\\nabout the rider projectthe reference image database to evaluate therapy response (rider) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. \\u00a0the national cancer institute (nci) has exercised a series of contracts with specific academic sites for collection of repeat \\\"coffee break,\\\" longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [ct]\\u00a0positron emission tomography [pet]\\u00a0ct, dynamic contrast-enhanced magnetic resonance imaging [dce mri], diffusion-weighted [dw] mri) and organ sites (currently lung, breast, and neuro). the methods for data collection, analysis, and results are described in the new combined rider white paper report (sept 2008):rider white paper: combined contracts report ( sept 2008) pdfthe long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the\\u00a0first rider white paper\\u00a0report (2006): rider white paper: executive summary pdf   rider white paper: editorial in nature.com breast cancer breast mr other rider\"},{\"id\":43451,\"type\":\"collection\",\"slug\":\"rider-lung-ct\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/rider-lung-ct\\\/\",\"title\":\"Coffee-break lung CT collection with scan images reconstructed at multiple imaging parameters\",\"short_title\":\"RIDER Lung CT\",\"doi\":\"10.7937\\\/k9\\\/tcia.2015.u1x8a5nr\",\"summary\":\"Quantitative imaging biomarkers (QIB) are increasingly used in clinical research to advance precision medicine approaches in oncology. Unlike biopsy-based biomarkers, QIBs are non-invasive and can estimate the spatial and temporal heterogeneity of total tumor burden. Computed tomography (CT) is a modality of choice for cancer diagnosis, prognosis, and response assessment due to its reliability and global accessibility.\\u00a0In recent years, despite overwhelmingly increased awareness of the reproducibility and robustness in quantitative imaging studies, lack of precious clinical image data limits our investigation and algorithm development. Here, we contribute to the cancer imaging community with our investigator-initiated, same-day repeat CT scan images of 32 non\\u2013small cell lung cancer (NSCLC) patients, along with radiologist\\u2019s annotated lesion contours as the reference standard. Each scan was reconstructed into 6 image settings using various combinations of three slice thicknesses (1.25 mm, 2.5 mm, 5 mm) and two reconstruction kernels (lung, standard; GE CT equipment), which spans a wide range of CT imaging reconstruction parameters commonly used in lung cancer clinical practice and clinical trials. One of the 6-settings, i.e., the setting of 1.25mm slice thickness and lung reconstruction (1.25L), was published as part of the\\u00a0Reference Image Database to Evaluate Therapy Response (RIDER) project in\\u00a02012.\\u00a0We believe that this entire dataset, comprising CT lung cancer images reconstructed on the same day at six different image settings, holds considerable value for advancing the development of robust Artificial Intelligence (AI) and machine learning (ML) methods. Additionally, it provides a valuable resource for comparing QIBs derived from a wide range of CT imaging parameter settings, for investigating data harmonization approaches, and for identifying specific CT imaging parameters most suitable for studying radiomics in lung cancer.Design Type(s)database creation objective \\u2022 data integration objective \\u2022 image analysis objectiveMeasurement Type(s)non-small cell lung carcinomaTechnology Type(s)computed tomography scanner \\u2022 image segmentationFactor Type(s)repeat scans \\u2022 image reconstruction settingsSample Characteristic(s)Homo sapiens \\u2022 lung\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/RiderLungCT-Image2.jpg\",\"program\":\"RIDER\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Chest\"],\"data_types\":[\"CT\",\"SEG\",\"Measurement\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":32,\"date_updated\":\"2024-06-25\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=RIDER%20Lung%20CT\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/RIDER-Lung-CT_v3_20240625.tcia\",\"blob\":\"coffee-break lung ct collection with scan images reconstructed at multiple imaging parameters rider lung ct quantitative imaging biomarkers (qib) are increasingly used in clinical research to advance precision medicine approaches in oncology. unlike biopsy-based biomarkers, qibs are non-invasive and can estimate the spatial and temporal heterogeneity of total tumor burden. computed tomography (ct) is a modality of choice for cancer diagnosis, prognosis, and response assessment due to its reliability and global accessibility.\\u00a0in recent years, despite overwhelmingly increased awareness of the reproducibility and robustness in quantitative imaging studies, lack of precious clinical image data limits our investigation and algorithm development. here, we contribute to the cancer imaging community with our investigator-initiated, same-day repeat ct scan images of 32 non\\u2013small cell lung cancer (nsclc) patients, along with radiologist\\u2019s annotated lesion contours as the reference standard. each scan was reconstructed into 6 image settings using various combinations of three slice thicknesses (1.25 mm, 2.5 mm, 5 mm) and two reconstruction kernels (lung, standard; ge ct equipment), which spans a wide range of ct imaging reconstruction parameters commonly used in lung cancer clinical practice and clinical trials. one of the 6-settings, i.e., the setting of 1.25mm slice thickness and lung reconstruction (1.25l), was published as part of the\\u00a0reference image database to evaluate therapy response (rider) project in\\u00a02012.\\u00a0we believe that this entire dataset, comprising ct lung cancer images reconstructed on the same day at six different image settings, holds considerable value for advancing the development of robust artificial intelligence (ai) and machine learning (ml) methods. additionally, it provides a valuable resource for comparing qibs derived from a wide range of ct imaging parameter settings, for investigating data harmonization approaches, and for identifying specific ct imaging parameters most suitable for studying radiomics in lung cancer.design type(s)database creation objective \\u2022 data integration objective \\u2022 image analysis objectivemeasurement type(s)non-small cell lung carcinomatechnology type(s)computed tomography scanner \\u2022 image segmentationfactor type(s)repeat scans \\u2022 image reconstruction settingssample characteristic(s)homo sapiens \\u2022 lung\\u00a0 lung cancer chest ct seg measurement image analyses rider\"},{\"id\":43461,\"type\":\"collection\",\"slug\":\"rider-lung-pet-ct\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/rider-lung-pet-ct\\\/\",\"title\":\"RIDER Lung PET-CT\",\"short_title\":\"RIDER Lung PET-CT\",\"doi\":\"10.7937\\\/k9\\\/tcia.2015.ofip7tvm\",\"summary\":\"\\u00a0The RIDER Lung PET-CT collection was shared to facilitate the RIDER PET\\\/CT subgroup activities. The PET\\\/CT subgroup was responsible for: (1) archiving de-identified DICOM serial PET\\\/CT phantom and lung cancer patient data in a public database to provide a resource for the testing and development of algorithms and imaging tools used for assessing response to therapy, (2) conducting multiple serial imaging studies of a long half-life phantom to assess systemic variance in serial PET\\\/CT scans that is unrelated to response, and (3) identifying and recommending methods for quantifying sources of variance in PET\\\/CT imaging with the goal of defining the change in PET measurements that may be unrelated to response to therapy, thus defining the absolute minimum effect size that should be used in the design of clinical trials using PET measurements as end points.About the RIDER projectThe Reference Image Database to Evaluate Therapy Response (RIDER) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. \\u00a0The National Cancer Institute (NCI) has exercised a series of contracts with specific academic sites for collection of repeat \\\"coffee break,\\\" longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [CT]\\u00a0positron emission tomography [PET]\\u00a0CT, dynamic contrast-enhanced magnetic resonance imaging [DCE MRI], diffusion-weighted [DW] MRI) and organ sites (currently lung, breast, and neuro). The methods for data collection, analysis, and results are described in the new Combined RIDER White Paper Report (Sept 2008):RIDER White Paper: Combined contracts report (Sept 2008) PDFThe long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. Thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the\\u00a0first RIDER white paper\\u00a0report (2006):RIDER White Paper: Executive Summary PDFRIDER White Paper: Editorial in Nature.com\",\"image\":false,\"program\":\"RIDER\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"PT\",\"CT\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":244,\"date_updated\":\"2015-12-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=RIDER%20Lung%20PET-CT\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/doiJNLP-UfVG4Zfc.tcia\",\"blob\":\"rider lung pet-ct rider lung pet-ct \\u00a0the rider lung pet-ct collection was shared to facilitate the rider pet\\\/ct subgroup activities. the pet\\\/ct subgroup was responsible for: (1) archiving de-identified dicom serial pet\\\/ct phantom and lung cancer patient data in a public database to provide a resource for the testing and development of algorithms and imaging tools used for assessing response to therapy, (2) conducting multiple serial imaging studies of a long half-life phantom to assess systemic variance in serial pet\\\/ct scans that is unrelated to response, and (3) identifying and recommending methods for quantifying sources of variance in pet\\\/ct imaging with the goal of defining the change in pet measurements that may be unrelated to response to therapy, thus defining the absolute minimum effect size that should be used in the design of clinical trials using pet measurements as end points.about the rider projectthe reference image database to evaluate therapy response (rider) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. \\u00a0the national cancer institute (nci) has exercised a series of contracts with specific academic sites for collection of repeat \\\"coffee break,\\\" longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [ct]\\u00a0positron emission tomography [pet]\\u00a0ct, dynamic contrast-enhanced magnetic resonance imaging [dce mri], diffusion-weighted [dw] mri) and organ sites (currently lung, breast, and neuro). the methods for data collection, analysis, and results are described in the new combined rider white paper report (sept 2008):rider white paper: combined contracts report (sept 2008) pdfthe long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the\\u00a0first rider white paper\\u00a0report (2006):rider white paper: executive summary pdfrider white paper: editorial in nature.com lung cancer lung pt ct other rider\"},{\"id\":43469,\"type\":\"collection\",\"slug\":\"rider-neuro-mri\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/rider-neuro-mri\\\/\",\"title\":\"RIDER NEURO MRI\",\"short_title\":\"RIDER NEURO MRI\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.VOSN3HN1\",\"summary\":\"RIDER Neuro MRI contains imaging data on 19 patients with recurrent glioblastoma who underwent repeat imaging sets.\\u00a0\\u00a0These images were obtained approximately 2 days apart (with the exception of one patient, RIDER Neuro MRI-1086100996, whose images were obtained one day apart).\\u00a0\\u00a0\\u00a0\\nDCE\\u2010MRI: \\u00a0All 19 patients had repeat dynamic contrast\\u2010enhanced MRI (DCE\\u2010MRI) datasets on the same 1.5T imaging magnet.\\u00a0\\u00a0On the basis of T2\\u2010weighted images, technologists chose 16 image locations using 5mm thick contiguous slices for the imaging.\\u00a0\\u00a0For T1 mapping, multi\\u2010flip 3D FLASH images were obtained using flip angles of 5, 10, 15, 20, 25 and 30 degrees, TR of 4.43 ms, TE of 2.1 ms, 2 signal averages.\\u00a0\\u00a0Dynamic images were obtained during the intravenous injection of 0.1mmol\\\/kg of Magnevist intravenous at 3ccs\\\/second, started 24 seconds after the scan had begun.\\u00a0\\u00a0The dynamic images were acquired using a 3D FLASH technique, using a flip angle of 25 degrees, TR of 3.8 ms, TE of 1.8 ms using a 1 x1 x 5mm voxel size.\\u00a0\\u00a0The 16 slice imaging set was obtained every 4.8 sec.\\nDTI: Seventeen of the 19 patients also obtained repeat diffusion tensor imaging (DTI) sets.\\u00a0\\u00a0Whole brain DTI were obtained using TR 6000ms, TE 100 ms, 90 degree flip angle, 4 signal averages, matrix 128 x 128,\\u00a0\\u00a01.72 x 1.72 x 5 mm voxel size, 12 tensor directions, iPAT 2, b value of 1000 sec\\\/mm2 .\\nContrast\\u2010enhanced 3D FLASH: All 19 patients underwent whole brain 3D FLASH imaging in the sagittal plane after the administration of Magnevist.\\u00a0\\u00a0For this sequence, TR was 8.6 ms, TE 4.1 ms, 20 degree flip angle, 1 signal average, matrix 256 x 256; 1mm isotropic\\u00a0\\u00a0voxel\\u00a0\\u00a0size.\\nContrast\\u2010enhanced 3D FLAIR: All 17 patients who had repeat DTI sets also had 3D FLAIR sequences in the sagittal plane after the administration of Magnevist.\\u00a0\\u00a0For this sequence, the TR was 6000 ms, TE 353 ms, and TI 2200ms; 180 degree flip angle, 1 signal average, matrix 256 x 216; 1 mm isotropic voxel size. Note: before transmission to NCIA, all image sets with 1mm isotropic voxel size were \\u201cdefaced\\u201d using MIPAV software or manually.\\nAbout the RIDER projectThe Reference Image Database to Evaluate Therapy Response (RIDER) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. \\u00a0The National Cancer Institute (NCI) has exercised a series of contracts with specific academic sites for collection of repeat \\\"coffee break,\\\" longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [CT]\\u00a0positron emission tomography [PET]\\u00a0CT, dynamic contrast-enhanced magnetic resonance imaging [DCE MRI], diffusion-weighted [DW] MRI) and organ sites (currently lung, breast, and neuro). The methods for data collection, analysis, and results are described in the new Combined RIDER White Paper Report (Sept 2008):RIDER White Paper: Combined contracts report ( Sept 2008) PDFThe long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. Thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the\\u00a0first RIDER white paper\\u00a0report (2006):RIDER White Paper: Executive Summary PDFRIDER White Paper: Editorial in Nature.com\",\"image\":false,\"program\":\"RIDER\",\"cancer_types\":[\"Brain Cancer\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":19,\"date_updated\":\"2011-11-08\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=RIDER%20NEURO%20MRI\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_RIDER_NEURO_MRI_06-22-2015.tcia\",\"blob\":\"rider neuro mri rider neuro mri rider neuro mri contains imaging data on 19 patients with recurrent glioblastoma who underwent repeat imaging sets.\\u00a0\\u00a0these images were obtained approximately 2 days apart (with the exception of one patient, rider neuro mri-1086100996, whose images were obtained one day apart).\\u00a0\\u00a0\\u00a0\\ndce\\u2010mri: \\u00a0all 19 patients had repeat dynamic contrast\\u2010enhanced mri (dce\\u2010mri) datasets on the same 1.5t imaging magnet.\\u00a0\\u00a0on the basis of t2\\u2010weighted images, technologists chose 16 image locations using 5mm thick contiguous slices for the imaging.\\u00a0\\u00a0for t1 mapping, multi\\u2010flip 3d flash images were obtained using flip angles of 5, 10, 15, 20, 25 and 30 degrees, tr of 4.43 ms, te of 2.1 ms, 2 signal averages.\\u00a0\\u00a0dynamic images were obtained during the intravenous injection of 0.1mmol\\\/kg of magnevist intravenous at 3ccs\\\/second, started 24 seconds after the scan had begun.\\u00a0\\u00a0the dynamic images were acquired using a 3d flash technique, using a flip angle of 25 degrees, tr of 3.8 ms, te of 1.8 ms using a 1 x1 x 5mm voxel size.\\u00a0\\u00a0the 16 slice imaging set was obtained every 4.8 sec.\\ndti: seventeen of the 19 patients also obtained repeat diffusion tensor imaging (dti) sets.\\u00a0\\u00a0whole brain dti were obtained using tr 6000ms, te 100 ms, 90 degree flip angle, 4 signal averages, matrix 128 x 128,\\u00a0\\u00a01.72 x 1.72 x 5 mm voxel size, 12 tensor directions, ipat 2, b value of 1000 sec\\\/mm2 .\\ncontrast\\u2010enhanced 3d flash: all 19 patients underwent whole brain 3d flash imaging in the sagittal plane after the administration of magnevist.\\u00a0\\u00a0for this sequence, tr was 8.6 ms, te 4.1 ms, 20 degree flip angle, 1 signal average, matrix 256 x 256; 1mm isotropic\\u00a0\\u00a0voxel\\u00a0\\u00a0size.\\ncontrast\\u2010enhanced 3d flair: all 17 patients who had repeat dti sets also had 3d flair sequences in the sagittal plane after the administration of magnevist.\\u00a0\\u00a0for this sequence, the tr was 6000 ms, te 353 ms, and ti 2200ms; 180 degree flip angle, 1 signal average, matrix 256 x 216; 1 mm isotropic voxel size. note: before transmission to ncia, all image sets with 1mm isotropic voxel size were \\u201cdefaced\\u201d using mipav software or manually.\\nabout the rider projectthe reference image database to evaluate therapy response (rider) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. \\u00a0the national cancer institute (nci) has exercised a series of contracts with specific academic sites for collection of repeat \\\"coffee break,\\\" longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [ct]\\u00a0positron emission tomography [pet]\\u00a0ct, dynamic contrast-enhanced magnetic resonance imaging [dce mri], diffusion-weighted [dw] mri) and organ sites (currently lung, breast, and neuro). the methods for data collection, analysis, and results are described in the new combined rider white paper report (sept 2008):rider white paper: combined contracts report ( sept 2008) pdfthe long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the\\u00a0first rider white paper\\u00a0report (2006):rider white paper: executive summary pdfrider white paper: editorial in nature.com brain cancer brain mr other rider\"},{\"id\":43483,\"type\":\"collection\",\"slug\":\"rider-phantom-mri\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/rider-phantom-mri\\\/\",\"title\":\"RIDER PHANTOM MRI\",\"short_title\":\"RIDER PHANTOM MRI\",\"doi\":\"10.7937\\\/k9\\\/tcia.2015.mi4qddhu\",\"summary\":\"The RIDER Phantom MRI data set contains repeat phantom studies. The phantom used for all data acquisitions was a version of the EuroSpin II Test Object 5 as distributed by Diagnostic Sonar, Ltd (Livingston, West Lothian, Scotland). The phantom was comprised of 18 25-mm doped gel filled tubes and 1 20-mm tube containing 0.25 mM GdDTPA.Scanners evaluated:Scanner A \\u2013 1.5T GE 8-channel HD with BRM gradient subsystem (33 mT\\\/m amplitude; 120 T\\\/m-s)Scanner B \\u2013 1.5T GE 8-channel HD with CRM gradient subsystem (50 mT\\\/m amplitude; 150 T\\\/m-s)Scanner C \\u2013 1.5T Siemens Espree (VB13) with 33 mT\\\/m amplitude, 100 T\\\/m-s gradient subsystemScanner D \\u2013 3.0T GE 8-channel HD with TwinSpeed gradients (40 mT\\\/m; 150 T\\\/m-s in zoom mode) For all measurements, an 8-channel phased array head coil was used.About the RIDER projectThe Reference Image Database to Evaluate Therapy Response (RIDER) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. \\u00a0The National Cancer Institute (NCI) has exercised a series of contracts with specific academic sites for collection of repeat \\\"coffee break,\\\" longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [CT]\\u00a0positron emission tomography [PET]\\u00a0CT, dynamic contrast-enhanced magnetic resonance imaging [DCE MRI], diffusion-weighted [DW] MRI) and organ sites (currently lung, breast, and neuro). The methods for data collection, analysis, and results are described in the new Combined RIDER White Paper Report (Sept 2008):RIDER White Paper: Combined contracts report ( Sept 2008) PDFThe long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. Thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the\\u00a0first RIDER white paper\\u00a0report (2006): RIDER White Paper: Executive Summary PDF  RIDER White Paper: Editorial in Nature.com\",\"image\":false,\"program\":\"RIDER\",\"cancer_types\":[\"Phantom\"],\"cancer_locations\":[\"Phantom\"],\"data_types\":[\"MR\",\"Other\",\"Measurement\",\"Protocol\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":10,\"date_updated\":\"2011-11-09\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=RIDER%20PHANTOM%20MRI\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_RIDER_PHANTOM_MRI_06-22-2015.tcia\",\"blob\":\"rider phantom mri rider phantom mri the rider phantom mri data set contains repeat phantom studies. the phantom used for all data acquisitions was a version of the eurospin ii test object 5 as distributed by diagnostic sonar, ltd (livingston, west lothian, scotland). the phantom was comprised of 18 25-mm doped gel filled tubes and 1 20-mm tube containing 0.25 mm gddtpa.scanners evaluated:scanner a \\u2013 1.5t ge 8-channel hd with brm gradient subsystem (33 mt\\\/m amplitude; 120 t\\\/m-s)scanner b \\u2013 1.5t ge 8-channel hd with crm gradient subsystem (50 mt\\\/m amplitude; 150 t\\\/m-s)scanner c \\u2013 1.5t siemens espree (vb13) with 33 mt\\\/m amplitude, 100 t\\\/m-s gradient subsystemscanner d \\u2013 3.0t ge 8-channel hd with twinspeed gradients (40 mt\\\/m; 150 t\\\/m-s in zoom mode) for all measurements, an 8-channel phased array head coil was used.about the rider projectthe reference image database to evaluate therapy response (rider) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. \\u00a0the national cancer institute (nci) has exercised a series of contracts with specific academic sites for collection of repeat \\\"coffee break,\\\" longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [ct]\\u00a0positron emission tomography [pet]\\u00a0ct, dynamic contrast-enhanced magnetic resonance imaging [dce mri], diffusion-weighted [dw] mri) and organ sites (currently lung, breast, and neuro). the methods for data collection, analysis, and results are described in the new combined rider white paper report (sept 2008):rider white paper: combined contracts report ( sept 2008) pdfthe long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the\\u00a0first rider white paper\\u00a0report (2006): rider white paper: executive summary pdf  rider white paper: editorial in nature.com phantom phantom mr other measurement protocol rider\"},{\"id\":43495,\"type\":\"collection\",\"slug\":\"rider-phantom-pet-ct\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/rider-phantom-pet-ct\\\/\",\"title\":\"RIDER PHANTOM PET-CT\",\"short_title\":\"RIDER PHANTOM PET-CT\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.8WG2KN4W\",\"summary\":\"The RIDER PHANTOM PET-CT collection consists of repeat measurement PET\\\/CT phantom scan collections carried out under the aegis of the Society of Nuclear Medicine (SNM) to discern the uniformity of clinical imaging instrumentation at various sites. They were obtained in cooperation with SNM as a resource for increased quantitative understanding of machine acquisition, analytic reproducibility and image processing.The phantom was manufactured by Sanders Medical (www.sandersmedical.com) in December of 2006. The phantom was based on a NEMA NU-2 IQ phantom (manufactured by Data Spectrum, Durham NC), but with the central 5 cm diameter 'lung' cylinder of the IQ phantom removed. In addition the two larger fillable spheres were changed to hot spheres, as opposed to cold spheres as in the NEMA NU-2 specifications. Nominal target\\\/background ratio was 4:1 with the initial background activity level set to be equivalent to 15 mCi in a 70 Kg patient, With the 271 day half-life of Ge-68 after 6 months the activity will be about 9.5 mCi. After a year it was 6 mCi.About the RIDER projectThe Reference Image Database to Evaluate Therapy Response (RIDER) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. \\u00a0The National Cancer Institute (NCI) has exercised a series of contracts with specific academic sites for collection of repeat \\\"coffee break,\\\" longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [CT]\\u00a0positron emission tomography [PET]\\u00a0CT, dynamic contrast-enhanced magnetic resonance imaging [DCE MRI], diffusion-weighted [DW] MRI) and organ sites (currently lung, breast, and neuro). The methods for data collection, analysis, and results are described in the new Combined RIDER White Paper Report (Sept 2008):RIDER White Paper: Combined contracts report ( Sept 2008) PDFThe long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. Thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the\\u00a0first RIDER white paper\\u00a0report (2006):RIDER White Paper: Executive Summary PDFRIDER White Paper: Editorial in Nature.com\",\"image\":false,\"program\":\"RIDER\",\"cancer_types\":[\"Phantom\"],\"cancer_locations\":[\"Phantom\"],\"data_types\":[\"PT\",\"CT\",\"Other\",\"Measurement\",\"Protocol\"],\"supporting_data\":[\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":20,\"date_updated\":\"2015-01-26\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=RIDER%20PHANTOM%20PET-CT\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_RIDER_PHANTOM_PET-CT_06-22-2015.tcia\",\"blob\":\"rider phantom pet-ct rider phantom pet-ct the rider phantom pet-ct collection consists of repeat measurement pet\\\/ct phantom scan collections carried out under the aegis of the society of nuclear medicine (snm) to discern the uniformity of clinical imaging instrumentation at various sites. they were obtained in cooperation with snm as a resource for increased quantitative understanding of machine acquisition, analytic reproducibility and image processing.the phantom was manufactured by sanders medical (www.sandersmedical.com) in december of 2006. the phantom was based on a nema nu-2 iq phantom (manufactured by data spectrum, durham nc), but with the central 5 cm diameter 'lung' cylinder of the iq phantom removed. in addition the two larger fillable spheres were changed to hot spheres, as opposed to cold spheres as in the nema nu-2 specifications. nominal target\\\/background ratio was 4:1 with the initial background activity level set to be equivalent to 15 mci in a 70 kg patient, with the 271 day half-life of ge-68 after 6 months the activity will be about 9.5 mci. after a year it was 6 mci.about the rider projectthe reference image database to evaluate therapy response (rider) is a targeted data collection used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. \\u00a0the national cancer institute (nci) has exercised a series of contracts with specific academic sites for collection of repeat \\\"coffee break,\\\" longitudinal phantom, and patient data for a range of imaging modalities (currently computed tomography [ct]\\u00a0positron emission tomography [pet]\\u00a0ct, dynamic contrast-enhanced magnetic resonance imaging [dce mri], diffusion-weighted [dw] mri) and organ sites (currently lung, breast, and neuro). the methods for data collection, analysis, and results are described in the new combined rider white paper report (sept 2008):rider white paper: combined contracts report ( sept 2008) pdfthe long term goal is to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. thus, the database should permit an objective comparison of methods for data collection and analysis as a national and international resource as described in the\\u00a0first rider white paper\\u00a0report (2006):rider white paper: executive summary pdfrider white paper: editorial in nature.com phantom phantom pt ct other measurement protocol image analyses rider\"},{\"id\":43501,\"type\":\"collection\",\"slug\":\"rider-pilot\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/rider-pilot\\\/\",\"title\":\"RIDER Pilot\",\"short_title\":\"RIDER Pilot\",\"doi\":\"10.7937\\\/m87f-mz83\",\"summary\":\"The Reference Image Database to Evaluate Therapy Response (RIDER) was a targeted data collection initiative used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. The long term goal was to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. \\u00a0See RIDER Collections for more information and other datasets that were part of this project.RIDER Pilot was originally supported under supplemental funding for the Data from The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans (LIDC-IDRI) U01 project and focused on the collection of longitudinal studies using X-ray CT for monitoring the response to therapy. The data came primarily from the M.D. Anderson Cancer Center and several of the LIDC academic sites. The original dataset contained 322 subjects and was originally published on the NCI CBIIT instance of NBIA before it was eventually retired in 2011.\\u00a0 However, a small subset of the RIDER Pilot data (8 subjects) which were analyzed in QIBA VolCT Group 1B Round 2 No Change Size Measurements (QIBA-VolCT-1B) have been re-released on TCIA.\",\"image\":false,\"program\":\"RIDER\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"CR\",\"DX\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":8,\"date_updated\":\"2023-05-24\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=RIDER%20Pilot\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/RIDER_Pilot_20230524_v1.tcia\",\"blob\":\"rider pilot rider pilot the reference image database to evaluate therapy response (rider) was a targeted data collection initiative used to generate an initial consensus on how to harmonize data collection and analysis for quantitative imaging methods applied to measure the response to drug or radiation therapy. the long term goal was to provide a resource to permit harmonized methods for data collection and analysis across different commercial imaging platforms to support multi-site clinical trials, using imaging as a biomarker for therapy response. \\u00a0see rider collections for more information and other datasets that were part of this project.rider pilot was originally supported under supplemental funding for the data from the lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans (lidc-idri) u01 project and focused on the collection of longitudinal studies using x-ray ct for monitoring the response to therapy. the data came primarily from the m.d. anderson cancer center and several of the lidc academic sites. the original dataset contained 322 subjects and was originally published on the nci cbiit instance of nbia before it was eventually retired in 2011.\\u00a0 however, a small subset of the rider pilot data (8 subjects) which were analyzed in qiba volct group 1b round 2 no change size measurements (qiba-volct-1b) have been re-released on tcia. lung cancer lung ct cr dx rider\"},{\"id\":47755,\"type\":\"collection\",\"slug\":\"rpa-head-and-neck-lymph-nodes\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/rpa-head-and-neck-lymph-nodes\\\/\",\"title\":\"The Head and Neck Lymph Nodes Dataset, Definitive, Non-contrast\",\"short_title\":\"RPA-Head-and-Neck-Lymph-Nodes\",\"doi\":\"10.7937\\\/fe3t-za58\",\"summary\":\"This dataset was generated to train models for research in the Radiation Planning Assistant (RPA), aimed at auto-contouring cervical lymph node levels in the head and neck.Patients:\\u00a0Training = 32 (31 unique); Test = 15Acquisition Protocol:\\u00a0See accompanying spreadsheet (TCIA_RPA_HN_LNs_Aquisition_Protocols) for more details. All patients were imaged supine with head holder and thermoplastic mask specified in our institution\\u2019s simulation protocol for radiotherapy head and neck.Scanner Details:\\u00a0Philips\\\/Brilliance Big Bore (89.4%) | SIEMENS\\\/SOMATOM Definition Edge (10.6%)Patient Inclusion Criteria:\\u00a0CT scans\\u00a0of 46 unique patients who retrospectively received head and neck radiotherapy at MD Anderson Cancer Center from April 2019 to June 2021 were curated. All data was gathered under an approved institutional review board protocol. These patients received definitive radiotherapy to the oropharynx with no nodal dissection.Segmentation Generation:\\u00a0First, five head and neck sub-specialized radiation oncologists manually contoured seven lymph node levels (IA, IB, II, III, IV, V, RP (Retropharyngeal or VIIA)) on 3 patients each, totaling 15 patients. This resulted in a collection of 105 lymph node level contours (5 physicians x 3 patients x 7 contours). Left and right contours for each nodal level were combined into one volume to prevent misclassification from left-right flipping augmentations. Contours were anatomically drawn without margin according to institutional practice. This dataset was comprised of images and contours of definitive Oropharynx Cancer (OPX) patients only and did not contain images and contours of those who had nodal level dissections at the time of radiotherapy. These 15 patients\\u2019 contours were used to train an initial model. This initial model was then used to generate lymph node level contours on 32 additional CT scans (31 unique patients), which were subsequently edited and reviewed for quality by a radiation oncology resident to be training data for the final model. The 15-patient cohort was then used as test data for the final model. The contours were split into left and right contours in post-processing.Structures of interest:\\u00a0LN_Neck_IA_L, LN_Neck_IA_R, LN_Neck_IB_L, LN_Neck_IB_R, LN_Neck_II_L, LN_Neck_II_R, LN_Neck_III_L, LN_Neck_III_R, LN_Neck_IV_L, LN_Neck_IV_R, LN_Neck_V_L, LN_Neck_V_R, LN_Neck_RP_L, LN_Neck_RP_RNote:\\u00a0In processing the image data, a single patient was accidentally anonymized more than once. The dataset for this model used a duplicate of one of those patients as 2 training subjects. The duplication was discovered after the journal publication. The duplicate anonymized patient IDs are RPA-HN-Lymph-Nodes-006 and RPA-HN-Lymph-Nodes-017. The duplicate was removed from the TCIA dataset; RPA-HN-Lymph-Nodes-006 was removed. Patient ID RPA-HN-Lymph-Nodes-017 may be used twice if the user wishes to duplicate the findings in the journal publication.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/tcia_coverimage.png\",\"program\":\"Community\",\"cancer_types\":[\"Head and Neck Cancer\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"CT\",\"RTSTRUCT\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":46,\"date_updated\":\"2024-10-23\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?saved-cart=nbia-28291728935625780\",\"download_url\":\"https:\\\/\\\/www.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/RPA-Head-and-Neck-Lymph-Nodes_v01_20241014_Restricted.tcia\",\"blob\":\"the head and neck lymph nodes dataset, definitive, non-contrast rpa-head-and-neck-lymph-nodes this dataset was generated to train models for research in the radiation planning assistant (rpa), aimed at auto-contouring cervical lymph node levels in the head and neck.patients:\\u00a0training = 32 (31 unique); test = 15acquisition protocol:\\u00a0see accompanying spreadsheet (tcia_rpa_hn_lns_aquisition_protocols) for more details. all patients were imaged supine with head holder and thermoplastic mask specified in our institution\\u2019s simulation protocol for radiotherapy head and neck.scanner details:\\u00a0philips\\\/brilliance big bore (89.4%) | siemens\\\/somatom definition edge (10.6%)patient inclusion criteria:\\u00a0ct scans\\u00a0of 46 unique patients who retrospectively received head and neck radiotherapy at md anderson cancer center from april 2019 to june 2021 were curated. all data was gathered under an approved institutional review board protocol. these patients received definitive radiotherapy to the oropharynx with no nodal dissection.segmentation generation:\\u00a0first, five head and neck sub-specialized radiation oncologists manually contoured seven lymph node levels (ia, ib, ii, iii, iv, v, rp (retropharyngeal or viia)) on 3 patients each, totaling 15 patients. this resulted in a collection of 105 lymph node level contours (5 physicians x 3 patients x 7 contours). left and right contours for each nodal level were combined into one volume to prevent misclassification from left-right flipping augmentations. contours were anatomically drawn without margin according to institutional practice. this dataset was comprised of images and contours of definitive oropharynx cancer (opx) patients only and did not contain images and contours of those who had nodal level dissections at the time of radiotherapy. these 15 patients\\u2019 contours were used to train an initial model. this initial model was then used to generate lymph node level contours on 32 additional ct scans (31 unique patients), which were subsequently edited and reviewed for quality by a radiation oncology resident to be training data for the final model. the 15-patient cohort was then used as test data for the final model. the contours were split into left and right contours in post-processing.structures of interest:\\u00a0ln_neck_ia_l, ln_neck_ia_r, ln_neck_ib_l, ln_neck_ib_r, ln_neck_ii_l, ln_neck_ii_r, ln_neck_iii_l, ln_neck_iii_r, ln_neck_iv_l, ln_neck_iv_r, ln_neck_v_l, ln_neck_v_r, ln_neck_rp_l, ln_neck_rp_rnote:\\u00a0in processing the image data, a single patient was accidentally anonymized more than once. the dataset for this model used a duplicate of one of those patients as 2 training subjects. the duplication was discovered after the journal publication. the duplicate anonymized patient ids are rpa-hn-lymph-nodes-006 and rpa-hn-lymph-nodes-017. the duplicate was removed from the tcia dataset; rpa-hn-lymph-nodes-006 was removed. patient id rpa-hn-lymph-nodes-017 may be used twice if the user wishes to duplicate the findings in the journal publication. head and neck cancer head-neck ct rtstruct other community\"},{\"id\":41481,\"type\":\"collection\",\"slug\":\"s0819\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/s0819\\\/\",\"title\":\"A Randomized Phase III Study Comparing Carboplatin\\\/Paclitaxel or Carboplatin\\\/Paclitaxel\\\/Bevacizumab With or Without Concurrent Cetuximab in Patients With Advanced Non-small Cell Lung Cancer\",\"short_title\":\"S0819\",\"doi\":\"10.7937\\\/DT39-JS04\",\"summary\":\"This collection contains data from the Southwest Oncology Group (SWOG) Clinical Trial\\u00a0NCT00946712, \\u201cCarboplatin and Paclitaxel With or Without Bevacizumab and\\\/or Cetuximab in Treating Patients With Stage IV or Recurrent Non-Small Cell Lung Cancer\\\". Principal Investigator: Roy Herbst. It was sponsored by NCI and performed by the\\u00a0Southwest Oncology Group\\u00a0under study number\\u00a0S0819.\\u00a0This randomized phase III trial studies carboplatin and paclitaxel to compare how well they work with or without bevacizumab and\\\/or cetuximab in treating patients with stage IV or non-small cell lung cancer that has returned after a period of improvement (recurrent). Drugs used in chemotherapy, such as carboplatin and paclitaxel, work in different ways to stop the growth of tumor cells, either by killing the cells, by stopping them from dividing, or by stopping them from spreading. Bevacizumab may prevent the growth of new blood vessels that tumor needs to grow. Cetuximab may also stop cancer cells from growing by binding and interfering with a protein on the surface of the tumor cell that is needed for tumor growth. It is not yet known whether giving carboplatin and paclitaxel are more effective with or without bevacizumab and\\\/or cetuximab in treating patients with non-small cell lung cancer.\\u00a0Select patient-level data from this trial is available via the following link:\\u00a0https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/850.\\u00a0\\u00a0\\u00a0Trial Outcomes\\u00a0\\u00a0Results of the trial have been reported in the following publications:\\u00a0Hirsch FR, Redman MW, Moon J, Agustoni F, Herbst RS, Semrad TJ, Varella-Garcia M, Rivard CJ, Kelly K, Gandara DR, Mack PC. EGFR High Copy Number Together With High EGFR Protein Expression Predicts Improved Outcome for Cetuximab-based Therapy in Squamous Cell Lung Cancer: Analysis From SWOG S0819, a Phase III Trial of Chemotherapy With or Without Cetuximab in Advanced NSCLC. Clin Lung Cancer. 2022 Jan;23(1):60-71. doi: 10.1016\\\/j.cllc.2021.10.002. Epub 2021 Oct 10.\\u00a0Herbst RS, Redman MW, Kim ES, Semrad TJ, Bazhenova L, Masters G, Oettel K, Guaglianone P, Reynolds C, Karnad A, Arnold SM, Varella-Garcia M, Moon J, Mack PC, Blanke CD, Hirsch FR, Kelly K, Gandara DR. Cetuximab plus carboplatin and paclitaxel with or without bevacizumab versus carboplatin and paclitaxel with or without bevacizumab in advanced NSCLC (SWOG S0819): a randomised, phase 3 study. Lancet Oncol. 2018 Jan;19(1):101-114. doi: 10.1016\\\/S1470-2045(17)30694-0. Epub 2017 Nov 20.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/nctn-logo.png\",\"program\":\"NCTN\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"PT\",\"MR\",\"SC\",\"NM\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":1299,\"date_updated\":\"2023-05-24\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=S0819\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/S0819-manifest-March-30-2023.tcia\",\"blob\":\"a randomized phase iii study comparing carboplatin\\\/paclitaxel or carboplatin\\\/paclitaxel\\\/bevacizumab with or without concurrent cetuximab in patients with advanced non-small cell lung cancer s0819 this collection contains data from the southwest oncology group (swog) clinical trial\\u00a0nct00946712, \\u201ccarboplatin and paclitaxel with or without bevacizumab and\\\/or cetuximab in treating patients with stage iv or recurrent non-small cell lung cancer\\\". principal investigator: roy herbst. it was sponsored by nci and performed by the\\u00a0southwest oncology group\\u00a0under study number\\u00a0s0819.\\u00a0this randomized phase iii trial studies carboplatin and paclitaxel to compare how well they work with or without bevacizumab and\\\/or cetuximab in treating patients with stage iv or non-small cell lung cancer that has returned after a period of improvement (recurrent). drugs used in chemotherapy, such as carboplatin and paclitaxel, work in different ways to stop the growth of tumor cells, either by killing the cells, by stopping them from dividing, or by stopping them from spreading. bevacizumab may prevent the growth of new blood vessels that tumor needs to grow. cetuximab may also stop cancer cells from growing by binding and interfering with a protein on the surface of the tumor cell that is needed for tumor growth. it is not yet known whether giving carboplatin and paclitaxel are more effective with or without bevacizumab and\\\/or cetuximab in treating patients with non-small cell lung cancer.\\u00a0select patient-level data from this trial is available via the following link:\\u00a0https:\\\/\\\/nctn-data-archive.nci.nih.gov\\\/node\\\/850.\\u00a0\\u00a0\\u00a0trial outcomes\\u00a0\\u00a0results of the trial have been reported in the following publications:\\u00a0hirsch fr, redman mw, moon j, agustoni f, herbst rs, semrad tj, varella-garcia m, rivard cj, kelly k, gandara dr, mack pc. egfr high copy number together with high egfr protein expression predicts improved outcome for cetuximab-based therapy in squamous cell lung cancer: analysis from swog s0819, a phase iii trial of chemotherapy with or without cetuximab in advanced nsclc. clin lung cancer. 2022 jan;23(1):60-71. doi: 10.1016\\\/j.cllc.2021.10.002. epub 2021 oct 10.\\u00a0herbst rs, redman mw, kim es, semrad tj, bazhenova l, masters g, oettel k, guaglianone p, reynolds c, karnad a, arnold sm, varella-garcia m, moon j, mack pc, blanke cd, hirsch fr, kelly k, gandara dr. cetuximab plus carboplatin and paclitaxel with or without bevacizumab versus carboplatin and paclitaxel with or without bevacizumab in advanced nsclc (swog s0819): a randomised, phase 3 study. lancet oncol. 2018 jan;19(1):101-114. doi: 10.1016\\\/s1470-2045(17)30694-0. epub 2017 nov 20.\\u00a0 lung cancer lung ct pt mr sc nm clinical nctn\"},{\"id\":41617,\"type\":\"collection\",\"slug\":\"sln-breast\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/sln-breast\\\/\",\"title\":\"Breast Metastases to Axillary Lymph Nodes\",\"short_title\":\"SLN-Breast\",\"doi\":\"10.7937\\\/tcia.2019.3xbn2jcc\",\"summary\":\"The detection of breast cancer metastases to lymph nodes is of great prognostic value for patient treatment. Using machine learning to detect metastatic breast cancer to lymph nodes can increase efficiency of pathologist diagnosis and ultimately ensure patients are accurately staged for prospective treatment. This dataset allows for the objective comparison of breast cancer metastases detection algorithms.The dataset consists of 130 de-identified whole slide images of H&E stained axillary lymph node specimens from 78 patients. Metastatic breast carcinoma is present in 36 of the WSI from 27 patients. No patient inclusion\\\/exclusion criteria were followed. No slide inclusion\\\/exclusion criteria were followed. The slides were scanned at Memorial Sloan Kettering Cancer Center (MSKCC) with Leica Aperio AT2 scanners at 20x equivalent magnification (0.5 microns per pixel). Together with the slides, the class label of each slide, either positive or negative for breast carcinoma, is given. The slide class label was obtained from the pathology report of the respective case.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\",\"Metastatic disease\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"Histopathology\",\"Classification\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":78,\"date_updated\":\"2019-07-18\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22SLN-Breast%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc0OSIsInBhc3Njb2RlIjoiMDZjYTgzNjczMjE3MDAzNzM4MzhlNDBhNzg2MTgxNmU4MDQ3ZmE2OSIsInBhY2thZ2VfaWQiOiI3NDkiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"breast metastases to axillary lymph nodes sln-breast the detection of breast cancer metastases to lymph nodes is of great prognostic value for patient treatment. using machine learning to detect metastatic breast cancer to lymph nodes can increase efficiency of pathologist diagnosis and ultimately ensure patients are accurately staged for prospective treatment. this dataset allows for the objective comparison of breast cancer metastases detection algorithms.the dataset consists of 130 de-identified whole slide images of h&e stained axillary lymph node specimens from 78 patients. metastatic breast carcinoma is present in 36 of the wsi from 27 patients. no patient inclusion\\\/exclusion criteria were followed. no slide inclusion\\\/exclusion criteria were followed. the slides were scanned at memorial sloan kettering cancer center (mskcc) with leica aperio at2 scanners at 20x equivalent magnification (0.5 microns per pixel). together with the slides, the class label of each slide, either positive or negative for breast carcinoma, is given. the slide class label was obtained from the pathology report of the respective case. breast cancer metastatic disease breast histopathology classification community\"},{\"id\":43613,\"type\":\"collection\",\"slug\":\"sn-am\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/sn-am\\\/\",\"title\":\"SN-AM Dataset: White Blood cancer dataset of B-ALL and MM for stain normalization\",\"short_title\":\"SN-AM\",\"doi\":\"10.7937\\\/tcia.2019.of2w8lxr\",\"summary\":\"Microscopic images were captured from bone marrow aspirate slides of patients diagnosed with B-lineage Acute Lymphoid Leukemia (B-ALL) and Multiple Myeloma (MM) as per the standard guidelines. Slides were stained using Jenner-Giemsa stain. Images were captured at 1000x magnification using Nikon Eclipse-200 microscope equipped with a digital camera. Images were captured in raw BMP format with a size of 2560x1920 pixels. In all, this dataset consists of 90 images of B-ALL and 100 images of MM. Both MM and B-ALL images have sufficient variability from one image to another image to rigorously test any stain normalization methodology developed.\\u00a0 More information about each subset are provided on the Detailed Description tab below.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Leukemia\",\"Multiple Myeloma\"],\"cancer_locations\":[\"Blood\",\"Bone\"],\"data_types\":[\"Histopathology\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":60,\"date_updated\":\"2019-03-26\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=%2Fsystem%2Ffiles%2Fcollectionmetadata%2F202401%2Fcohort_builder_01-27-2024.json&filterState=%5B%7B%22id%22%3A%22collection%22%2C%22title%22%3A%22TCIA+Collection%22%2C%22field%22%3A%22collection%22%2C%22operation%22%3A%22eq%22%2C%22values%22%3A%22SN-AM%22%7D%5D\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc0OCIsInBhc3Njb2RlIjoiNGY0NjA5NjQ0NzZiMjc2NGI1MGI4MGE2NWZiYzA0MWQ4NjIzMjEyOSIsInBhY2thZ2VfaWQiOiI3NDgiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"sn-am dataset: white blood cancer dataset of b-all and mm for stain normalization sn-am microscopic images were captured from bone marrow aspirate slides of patients diagnosed with b-lineage acute lymphoid leukemia (b-all) and multiple myeloma (mm) as per the standard guidelines. slides were stained using jenner-giemsa stain. images were captured at 1000x magnification using nikon eclipse-200 microscope equipped with a digital camera. images were captured in raw bmp format with a size of 2560x1920 pixels. in all, this dataset consists of 90 images of b-all and 100 images of mm. both mm and b-all images have sufficient variability from one image to another image to rigorously test any stain normalization methodology developed.\\u00a0 more information about each subset are provided on the detailed description tab below. leukemia multiple myeloma blood bone histopathology community\"},{\"id\":41469,\"type\":\"collection\",\"slug\":\"soft-tissue-sarcoma\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/soft-tissue-sarcoma\\\/\",\"title\":\"A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities\",\"short_title\":\"Soft-tissue-Sarcoma\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.7GO2GSKS\",\"summary\":\"This collection contains FDG-PET\\\/CT and anatomical MR (T1-weighted, T2-weighted with fat-suppression) imaging data from 51 patients with histologically proven soft-tissue sarcomas (STSs) of the extremities. All patients had pre-treatment FDG-PET\\\/CT and MRI scans between November 2004 and November 2011. (Note: date in the TCIA images have been changed in the interest of de-identification; the same change was applied across all images, preserving the time intervals between serial scans). During the follow-up period, 19 patients developed lung metastases. Imaging data and lung metastases development status were used in the following study:Valli\\u00e8res, M. et al. (2015). A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities.\\u00a0Physics in Medicine and Biology, 60(14), 5471-5496. doi:10.1088\\\/0031-9155\\\/60\\\/14\\\/5471.Imaging data, tumor contours (RTstruct DICOM objects), clinical data and source code is available for this study. See the DOI below for more details and links to access the whole dataset. Please contact the TCIA Helpdesk\\u00a0for any scientific or other inquiries about this dataset.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Soft-tissue Sarcoma\"],\"cancer_locations\":[\"Extremities\"],\"data_types\":[\"CT\",\"RTSTRUCT\",\"PT\",\"MR\",\"Follow-Up\",\"Treatment\",\"Demographic\",\"Diagnosis\",\"Classification\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":51,\"date_updated\":\"2015-06-01\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=Soft-tissue-Sarcoma\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/doiJNLP-zgVcrK7I.tcia\",\"blob\":\"a radiomics model from joint fdg-pet and mri texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities soft-tissue-sarcoma this collection contains fdg-pet\\\/ct and anatomical mr (t1-weighted, t2-weighted with fat-suppression) imaging data from 51 patients with histologically proven soft-tissue sarcomas (stss) of the extremities. all patients had pre-treatment fdg-pet\\\/ct and mri scans between november 2004 and november 2011. (note: date in the tcia images have been changed in the interest of de-identification; the same change was applied across all images, preserving the time intervals between serial scans). during the follow-up period, 19 patients developed lung metastases. imaging data and lung metastases development status were used in the following study:valli\\u00e8res, m. et al. (2015). a radiomics model from joint fdg-pet and mri texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities.\\u00a0physics in medicine and biology, 60(14), 5471-5496. doi:10.1088\\\/0031-9155\\\/60\\\/14\\\/5471.imaging data, tumor contours (rtstruct dicom objects), clinical data and source code is available for this study. see the doi below for more details and links to access the whole dataset. please contact the tcia helpdesk\\u00a0for any scientific or other inquiries about this dataset. soft-tissue sarcoma extremities ct rtstruct pt mr follow-up treatment demographic diagnosis classification clinical image analyses software\\\/source code community\"},{\"id\":43643,\"type\":\"collection\",\"slug\":\"spie-aapm-lung-ct-challenge\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/spie-aapm-lung-ct-challenge\\\/\",\"title\":\"SPIE-AAPM-NCI Lung Nodule Classification Challenge Dataset\",\"short_title\":\"SPIE-AAPM Lung CT Challenge\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.UZLSU3FL\",\"summary\":\"As part of the 2015 SPIE Medical Imaging Conference, SPIE \\u2013 with the support of American Association of Physicists in Medicine (AAPM) and the National Cancer Institute (NCI) \\u2013 will conduct a \\u201cGrand Challenge\\u201d on quantitative image analysis methods for the diagnostic classification of malignant and benign lung nodules. The LUNGx Challenge will provide a unique opportunity for participants to compare their algorithms to those of others from academia, industry, and government in a structured, direct way using the same data sets.\\nRelease date of calibration set cases with truth: \\u00a0November 21, 2014 Release date of test set cases without truth: \\u00a0January 9, 2015 Submission date for participants to submit test set classification results: \\u00a0February 6, 2015 SPIE Medical Imaging meeting: \\u00a0February 21 to 26, 2015\\nFor more information please refer to:\\u00a0LUNGx SPIE-AAPM-NCI Lung Nodule Classification Challenge, the\\u00a0related SPIE Guest Editorial, and corresponding scientific manuscript.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image2014-11-21-103431.png\",\"program\":\"Community\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"Diagnosis\",\"Measurement\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":70,\"date_updated\":\"2016-09-23\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=SPIE-AAPM%20Lung%20CT%20Challenge\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_SPIE-AAPM_Lung_CT_Challenge_06-22-2015.tcia\",\"blob\":\"spie-aapm-nci lung nodule classification challenge dataset spie-aapm lung ct challenge as part of the 2015 spie medical imaging conference, spie \\u2013 with the support of american association of physicists in medicine (aapm) and the national cancer institute (nci) \\u2013 will conduct a \\u201cgrand challenge\\u201d on quantitative image analysis methods for the diagnostic classification of malignant and benign lung nodules. the lungx challenge will provide a unique opportunity for participants to compare their algorithms to those of others from academia, industry, and government in a structured, direct way using the same data sets.\\nrelease date of calibration set cases with truth: \\u00a0november 21, 2014 release date of test set cases without truth: \\u00a0january 9, 2015 submission date for participants to submit test set classification results: \\u00a0february 6, 2015 spie medical imaging meeting: \\u00a0february 21 to 26, 2015\\nfor more information please refer to:\\u00a0lungx spie-aapm-nci lung nodule classification challenge, the\\u00a0related spie guest editorial, and corresponding scientific manuscript. lung cancer lung ct diagnosis measurement clinical image analyses community\"},{\"id\":41163,\"type\":\"collection\",\"slug\":\"stageii-colorectal-ct\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/stageii-colorectal-ct\\\/\",\"title\":\"Abdominal or pelvic enhanced CT images within 10 days before surgery of 230 patients with stage II colorectal cancer\",\"short_title\":\"StageII-Colorectal-CT\",\"doi\":\"10.7937\\\/p5k5-tg43\",\"summary\":\"This dataset includes abdominal or pelvic enhanced CT images within 10 days before surgery of 230 patients with stage II colorectal cancer (CRC). The inclusion criteria were as follows: (i) patients with radical surgery for CRC (complete removal of the original tumor and regional lymphadenectomy); (ii) patients with stage II CRC confirmed by histology and pathology; (iii) abdominal or pelvic enhanced CT examination was performed within 10 days before surgery, with complete CT enhanced scan images. The exclusion criteria were as follows: (i) preoperative treatment (neoadjuvant chemotherapy or radiotherapy); (ii) patients with another tumor diseases at the same time; (iii) patients who died within 1 month after surgery due to acute surgical complications.\\u00a0All CT images were enhanced abdominal or pelvic CT scan, which were scanned by using a Sensation 64 (Siemens Healthcare, Erlangen, Germany) CT scanner or Brilliance (Philips Healthcare, Best, The Netherlands) CT scanner. The specific scanning parameters were as follows: 120kV tube voltage, 200mA tube current, 5 mm slice thickness, 0.5s\\\/week rack speed, 1.4 or 0.9 pitch, 4.11 cm field of view and a 512\\u00d7512 matrix. 80-100ml iodine contrast agent ioprolamine was injected through the cubital vein at a speed of 2-3mL\\\/s. CT enhanced images were collected after 65-75s contrast agent injection.The digital CT images were retrieved from the picture archiving and communication system (PACS), and exported with digital imaging and communication in medicine (DICOM) format. Patient identifying information has been removed. The publishing of this dataset follows the ethical and privacy rules of China. Other researchers can further analyze these CT images of stage II colorectal cancer or use the data as validation sets for their studies.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Colorectal Cancer\"],\"cancer_locations\":[\"Abdomen\",\"Pelvis\"],\"data_types\":[\"CT\"],\"supporting_data\":[\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":230,\"date_updated\":\"2022-04-11\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=StageII-Colorectal-CT\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/StageII-Colorectal-CT_v1.tcia\",\"blob\":\"abdominal or pelvic enhanced ct images within 10 days before surgery of 230 patients with stage ii colorectal cancer stageii-colorectal-ct this dataset includes abdominal or pelvic enhanced ct images within 10 days before surgery of 230 patients with stage ii colorectal cancer (crc). the inclusion criteria were as follows: (i) patients with radical surgery for crc (complete removal of the original tumor and regional lymphadenectomy); (ii) patients with stage ii crc confirmed by histology and pathology; (iii) abdominal or pelvic enhanced ct examination was performed within 10 days before surgery, with complete ct enhanced scan images. the exclusion criteria were as follows: (i) preoperative treatment (neoadjuvant chemotherapy or radiotherapy); (ii) patients with another tumor diseases at the same time; (iii) patients who died within 1 month after surgery due to acute surgical complications.\\u00a0all ct images were enhanced abdominal or pelvic ct scan, which were scanned by using a sensation 64 (siemens healthcare, erlangen, germany) ct scanner or brilliance (philips healthcare, best, the netherlands) ct scanner. the specific scanning parameters were as follows: 120kv tube voltage, 200ma tube current, 5 mm slice thickness, 0.5s\\\/week rack speed, 1.4 or 0.9 pitch, 4.11 cm field of view and a 512\\u00d7512 matrix. 80-100ml iodine contrast agent ioprolamine was injected through the cubital vein at a speed of 2-3ml\\\/s. ct enhanced images were collected after 65-75s contrast agent injection.the digital ct images were retrieved from the picture archiving and communication system (pacs), and exported with digital imaging and communication in medicine (dicom) format. patient identifying information has been removed. the publishing of this dataset follows the ethical and privacy rules of china. other researchers can further analyze these ct images of stage ii colorectal cancer or use the data as validation sets for their studies. colorectal cancer abdomen pelvis ct software\\\/source code community\"},{\"id\":44347,\"type\":\"collection\",\"slug\":\"tcga-blca\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-blca\\\/\",\"title\":\"The Cancer Genome Atlas Urothelial Bladder Carcinoma Collection\",\"short_title\":\"TCGA-BLCA\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.8LNG8XDR\",\"summary\":\"The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal\\u00a0while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the\\u00a0TCGA Bladder Phenotype Research Group.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Bladder Endothelial Carcinoma\"],\"cancer_locations\":[\"Bladder\"],\"data_types\":[\"CT\",\"CR\",\"MR\",\"PT\",\"DX\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":120,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=TCGA-BLCA\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCGA-BLCA-August-30-2019-NBIA-manifest.tcia\",\"blob\":\"the cancer genome atlas urothelial bladder carcinoma collection tcga-blca the cancer genome atlas urothelial bladder carcinoma (tcga-blca) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal\\u00a0while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. learn more about the\\u00a0tcga bladder phenotype research group. bladder endothelial carcinoma bladder ct cr mr pt dx clinical genomics histopathology tcga\"},{\"id\":43753,\"type\":\"collection\",\"slug\":\"tcga-brca\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-brca\\\/\",\"title\":\"The Cancer Genome Atlas Breast Invasive Carcinoma Collection\",\"short_title\":\"TCGA-BRCA\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.AB2NAZRP\",\"summary\":\"The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0TCGA Breast Phenotype Research Group.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MR\",\"MG\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Image Analyses\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":139,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-BRCA\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_TCGA-BRCA_09-16-2015.tcia\",\"blob\":\"the cancer genome atlas breast invasive carcinoma collection tcga-brca the cancer genome atlas breast invasive carcinoma (tcga-brca) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0tcga breast phenotype research group. breast cancer breast mr mg clinical genomics image analyses histopathology tcga\"},{\"id\":43811,\"type\":\"collection\",\"slug\":\"tcga-cesc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-cesc\\\/\",\"title\":\"The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma Collection\",\"short_title\":\"TCGA-CESC\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.SQ4M8YP4\",\"summary\":\"The Cancer Genome Atlas Cervical Squamous Cell Carcinoma and Endocervical Adenocarcinoma (TCGA-CESC) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the\\u00a0CIP TCGA Radiology Initiative.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Cervical Squamous Cell Carcinoma\",\"Endocervical Adenocarcinoma\"],\"cancer_locations\":[\"Cervix\"],\"data_types\":[\"MR\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":54,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-CESC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_TCGA-CESC_09-16-2015.tcia\",\"blob\":\"the cancer genome atlas cervical squamous cell carcinoma and endocervical adenocarcinoma collection tcga-cesc the cancer genome atlas cervical squamous cell carcinoma and endocervical adenocarcinoma (tcga-cesc) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. learn more about the\\u00a0cip tcga radiology initiative. cervical squamous cell carcinoma endocervical adenocarcinoma cervix mr clinical genomics histopathology tcga\"},{\"id\":43835,\"type\":\"collection\",\"slug\":\"tcga-coad\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-coad\\\/\",\"title\":\"The Cancer Genome Atlas Colon Adenocarcinoma Collection\",\"short_title\":\"TCGA-COAD\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.HJJHBOXZ\",\"summary\":\"The Cancer Genome Atlas Colon\\u00a0Adenocarcinoma (TCGA-COAD) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0CIP TCGA Radiology Initiative.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Colon adenocarcinoma\"],\"cancer_locations\":[\"Colon\"],\"data_types\":[\"CT\",\"OT\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":25,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-COAD\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_TCGA-COAD_09-16-2015.tcia\",\"blob\":\"the cancer genome atlas colon adenocarcinoma collection tcga-coad the cancer genome atlas colon\\u00a0adenocarcinoma (tcga-coad) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0cip tcga radiology initiative. colon adenocarcinoma colon ct ot clinical genomics histopathology tcga\"},{\"id\":43859,\"type\":\"collection\",\"slug\":\"tcga-esca\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-esca\\\/\",\"title\":\"The Cancer Genome Atlas Esophageal Carcinoma Collection\",\"short_title\":\"TCGA-ESCA\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.VPTNRGFY\",\"summary\":\"The Cancer Genome Atlas Esophageal Carcinoma (TCGA-ESCA) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0CIP TCGA Radiology Initiative.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Esophageal Carcinoma\"],\"cancer_locations\":[\"Esophagus\"],\"data_types\":[\"CT\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":16,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-ESCA\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_TCGA-ESCA-09-16-2015.tcia\",\"blob\":\"the cancer genome atlas esophageal carcinoma collection tcga-esca the cancer genome atlas esophageal carcinoma (tcga-esca) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0cip tcga radiology initiative. esophageal carcinoma esophagus ct clinical genomics histopathology tcga\"},{\"id\":43903,\"type\":\"collection\",\"slug\":\"tcga-gbm\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-gbm\\\/\",\"title\":\"The Cancer Genome Atlas Glioblastoma Multiforme Collection\",\"short_title\":\"TCGA-GBM\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.RNYFUYE9\",\"summary\":\"The Cancer Genome Atlas Glioblastoma Multiforme (TCGA-GBM) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0TCGA Glioma Phenotype Research Group.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Glioblastoma Multiforme\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"CT\",\"DX\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Image Analyses\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":262,\"date_updated\":\"2023-08-07\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-GBM\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_TCGA-GBM_09-16-2015.tcia\",\"blob\":\"the cancer genome atlas glioblastoma multiforme collection tcga-gbm the cancer genome atlas glioblastoma multiforme (tcga-gbm) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0tcga glioma phenotype research group. glioblastoma multiforme brain mr ct dx clinical genomics image analyses histopathology tcga\"},{\"id\":43955,\"type\":\"collection\",\"slug\":\"tcga-hnsc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-hnsc\\\/\",\"title\":\"The Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma Collection\",\"short_title\":\"TCGA-HNSC\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.LXKQ47MS\",\"summary\":\"The Cancer Genome Atlas Head-Neck Squamous Cell Carcinoma (TCGA-HNSC) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0TCGA Head-Neck Phenotype Research Group.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Head and Neck Squamous Cell Carcinoma\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"CT\",\"PT\",\"MR\",\"RTDOSE\",\"RTSTRUCT\",\"RTPLAN\",\"OT\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":227,\"date_updated\":\"2023-05-24\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-HNSC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCGA-HNSC-08-30-2018-doiJNLP-PMK5AdS1.tcia\",\"blob\":\"the cancer genome atlas head-neck squamous cell carcinoma collection tcga-hnsc the cancer genome atlas head-neck squamous cell carcinoma (tcga-hnsc) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0tcga head-neck phenotype research group. head and neck squamous cell carcinoma head-neck ct pt mr rtdose rtstruct rtplan ot clinical genomics histopathology tcga\"},{\"id\":43979,\"type\":\"collection\",\"slug\":\"tcga-kich\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-kich\\\/\",\"title\":\"The Cancer Genome Atlas Kidney Chromophobe Collection\",\"short_title\":\"TCGA-KICH\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.YU3RBCZN\",\"summary\":\"The Cancer Genome Atlas Kidney Chromophobe (TCGA-KICH) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0TCGA Renal Phenotype Research Group.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Kidney Chromophobe\"],\"cancer_locations\":[\"Kidney\"],\"data_types\":[\"MR\",\"CT\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":15,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-KICH\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_TCGA-KICH_09-16-2015.tcia\",\"blob\":\"the cancer genome atlas kidney chromophobe collection tcga-kich the cancer genome atlas kidney chromophobe (tcga-kich) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0tcga renal phenotype research group. kidney chromophobe kidney mr ct clinical genomics histopathology tcga\"},{\"id\":44001,\"type\":\"collection\",\"slug\":\"tcga-kirc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-kirc\\\/\",\"title\":\"The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma Collection\",\"short_title\":\"TCGA-KIRC\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.V6PBVTDR\",\"summary\":\"The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0TCGA Renal Phenotype Research Group.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Kidney Renal Clear Cell Carcinoma\"],\"cancer_locations\":[\"Kidney\"],\"data_types\":[\"CT\",\"MR\",\"CR\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Image Analyses\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":267,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-KIRC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_TCGA-KIRC_09-16-2015.tcia\",\"blob\":\"the cancer genome atlas kidney renal clear cell carcinoma collection tcga-kirc the cancer genome atlas kidney renal clear cell carcinoma (tcga-kirc) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0tcga renal phenotype research group. kidney renal clear cell carcinoma kidney ct mr cr clinical genomics image analyses histopathology tcga\"},{\"id\":43785,\"type\":\"collection\",\"slug\":\"tcga-kirp\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-kirp\\\/\",\"title\":\"The Cancer Genome Atlas Cervical Kidney Renal Papillary Cell Carcinoma Collection\",\"short_title\":\"TCGA-KIRP\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.ACWOGBEF\",\"summary\":\"The Cancer Genome Atlas Cervical Kidney renal papillary cell carcinoma (KIRP) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0TCGA Renal Phenotype Research Group.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Kidney Renal Papillary Cell Carcinoma\"],\"cancer_locations\":[\"Kidney\"],\"data_types\":[\"CT\",\"MR\",\"PT\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":33,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-KIRP\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/doiJNLP-TCGA-KIRP-01-30-2017.tcia\",\"blob\":\"the cancer genome atlas cervical kidney renal papillary cell carcinoma collection tcga-kirp the cancer genome atlas cervical kidney renal papillary cell carcinoma (kirp) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0tcga renal phenotype research group. kidney renal papillary cell carcinoma kidney ct mr pt clinical genomics histopathology tcga\"},{\"id\":44065,\"type\":\"collection\",\"slug\":\"tcga-lgg\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-lgg\\\/\",\"title\":\"The Cancer Genome Atlas Low Grade Glioma Collection\",\"short_title\":\"TCGA-LGG\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.L4LTD3TK\",\"summary\":\"The Cancer Genome Atlas Low Grade Glioma (TCGA-LGG) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0TCGA Glioma Phenotype Research Group.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Low Grade Glioma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"CT\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":199,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-LGG\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_TCGA-LGG_09-16-2015.tcia\",\"blob\":\"the cancer genome atlas low grade glioma collection tcga-lgg the cancer genome atlas low grade glioma (tcga-lgg) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0tcga glioma phenotype research group. low grade glioma brain mr ct clinical genomics histopathology image analyses tcga\"},{\"id\":44041,\"type\":\"collection\",\"slug\":\"tcga-lihc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-lihc\\\/\",\"title\":\"The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection\",\"short_title\":\"TCGA-LIHC\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.IMMQW8UQ\",\"summary\":\"The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0CIP TCGA Radiology Initiative.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Liver Hepatocellular Carcinoma\"],\"cancer_locations\":[\"Liver\"],\"data_types\":[\"CT\",\"MR\",\"PT\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":97,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-LIHC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/doiJNLP-TCGA-LIHC-01-30-2017.tcia\",\"blob\":\"the cancer genome atlas liver hepatocellular carcinoma collection tcga-lihc the cancer genome atlas liver hepatocellular carcinoma (tcga-lihc) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0cip tcga radiology initiative. liver hepatocellular carcinoma liver ct mr pt clinical genomics histopathology image analyses tcga\"},{\"id\":44097,\"type\":\"collection\",\"slug\":\"tcga-luad\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-luad\\\/\",\"title\":\"The Cancer Genome Atlas Lung Adenocarcinoma Collection\",\"short_title\":\"TCGA-LUAD\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.JGNIHEP5\",\"summary\":\"The Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0TCGA Lung Phenotype Research Group.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Lung Adenocarcinoma\"],\"cancer_locations\":[\"Chest\"],\"data_types\":[\"PT\",\"CT\",\"NM\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":69,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-LUAD\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/doiJNLP-TCGA-LUAD-01-30-2017.tcia\",\"blob\":\"the cancer genome atlas lung adenocarcinoma collection tcga-luad the cancer genome atlas lung adenocarcinoma (tcga-luad) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0tcga lung phenotype research group. lung adenocarcinoma chest pt ct nm clinical genomics histopathology image analyses tcga\"},{\"id\":44129,\"type\":\"collection\",\"slug\":\"tcga-lusc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-lusc\\\/\",\"title\":\"The Cancer Genome Atlas Lung Squamous Cell Carcinoma Collection\",\"short_title\":\"TCGA-LUSC\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.TYGKKFMQ\",\"summary\":\"The Cancer Genome Atlas Lung Squamous Cell Carcinoma (TCGA-LUSC) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0TCGA Lung Phenotype Research Group.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Lung Squamous Cell Carcinoma\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"CT\",\"PT\",\"NM\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":37,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-LUSC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/doiJNLP-TCGA-LUSC-01-30-2017.tcia\",\"blob\":\"the cancer genome atlas lung squamous cell carcinoma collection tcga-lusc the cancer genome atlas lung squamous cell carcinoma (tcga-lusc) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0tcga lung phenotype research group. lung squamous cell carcinoma lung ct pt nm clinical genomics histopathology tcga\"},{\"id\":44155,\"type\":\"collection\",\"slug\":\"tcga-ov\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-ov\\\/\",\"title\":\"The Cancer Genome Atlas Ovarian Cancer Collection\",\"short_title\":\"TCGA-OV\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.NDO1MDFQ\",\"summary\":\"The Cancer Genome Atlas Ovarian Cancer (TCGA-OV) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the\\u00a0TCGA Ovarian Phenotype Research Group.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Ovarian Serous Cystadenocarcinoma\"],\"cancer_locations\":[\"Ovary\"],\"data_types\":[\"CT\",\"MR\",\"OT\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Image Analyses\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":143,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-OV\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_TCGA-OV_09-16-2015.tcia\",\"blob\":\"the cancer genome atlas ovarian cancer collection tcga-ov the cancer genome atlas ovarian cancer (tcga-ov) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. learn more about the\\u00a0tcga ovarian phenotype research group. ovarian serous cystadenocarcinoma ovary ct mr ot clinical genomics image analyses histopathology tcga\"},{\"id\":44187,\"type\":\"collection\",\"slug\":\"tcga-prad\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-prad\\\/\",\"title\":\"The Cancer Genome Atlas Prostate Adenocarcinoma Collection\",\"short_title\":\"TCGA-PRAD\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.YXOGLM4Y\",\"summary\":\"The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0CIP TCGA Radiology Initiative.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Prostate Cancer\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"MR\",\"CT\",\"PT\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":14,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-PRAD\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/doiJNLP-Pz8ET39p.tcia\",\"blob\":\"the cancer genome atlas prostate adenocarcinoma collection tcga-prad the cancer genome atlas prostate adenocarcinoma (tcga-prad) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0cip tcga radiology initiative. prostate cancer prostate mr ct pt clinical genomics histopathology tcga\"},{\"id\":44211,\"type\":\"collection\",\"slug\":\"tcga-read\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-read\\\/\",\"title\":\"The Cancer Genome Atlas Rectum Adenocarcinoma Collection\",\"short_title\":\"TCGA-READ\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.F7PPNPNU\",\"summary\":\"The Cancer Genome Atlas Rectum Adenocarcinoma (TCGA-READ) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the\\u00a0CIP TCGA Radiology Initiative.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Rectum Adenocarcinoma\"],\"cancer_locations\":[\"Rectum\"],\"data_types\":[\"MR\",\"CT\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":3,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-READ\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_TCGA-READ_09-16-2015.tcia\",\"blob\":\"the cancer genome atlas rectum adenocarcinoma collection tcga-read the cancer genome atlas rectum adenocarcinoma (tcga-read) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. learn more about the\\u00a0cip tcga radiology initiative. rectum adenocarcinoma rectum mr ct clinical genomics histopathology tcga\"},{\"id\":44233,\"type\":\"collection\",\"slug\":\"tcga-sarc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-sarc\\\/\",\"title\":\"The Cancer Genome Atlas Sarcoma Collection\",\"short_title\":\"TCGA-SARC\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.CX6YLSUX\",\"summary\":\"The Cancer Genome Atlas Sarcoma (TCGA-SARC) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the\\u00a0CIP TCGA Radiology Initiative.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Sarcomas\"],\"cancer_locations\":[\"Chest-Abdomen-Pelvis\",\"Leg\",\"TSpine\"],\"data_types\":[\"CT\",\"MR\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":5,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-SARC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_TCGA-SARC_09-16-2015.tcia\",\"blob\":\"the cancer genome atlas sarcoma collection tcga-sarc the cancer genome atlas sarcoma (tcga-sarc) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. learn more about the\\u00a0cip tcga radiology initiative. sarcomas chest-abdomen-pelvis leg tspine ct mr clinical genomics histopathology tcga\"},{\"id\":44257,\"type\":\"collection\",\"slug\":\"tcga-stad\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-stad\\\/\",\"title\":\"The Cancer Genome Atlas Stomach Adenocarcinoma Collection\",\"short_title\":\"TCGA-STAD\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.GDHL9KIM\",\"summary\":\"The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0CIP TCGA Radiology Initiative.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Stomach Adenocarcinoma\"],\"cancer_locations\":[\"Stomach\"],\"data_types\":[\"CT\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":46,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-STAD\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_TCGA-STAD_09-16-2015.tcia\",\"blob\":\"the cancer genome atlas stomach adenocarcinoma collection tcga-stad the cancer genome atlas stomach adenocarcinoma (tcga-stad) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0cip tcga radiology initiative. stomach adenocarcinoma stomach ct clinical genomics histopathology tcga\"},{\"id\":44281,\"type\":\"collection\",\"slug\":\"tcga-thca\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-thca\\\/\",\"title\":\"The Cancer Genome Atlas Thyroid Cancer Collection\",\"short_title\":\"TCGA-THCA\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.9ZFRVF1B\",\"summary\":\"The Cancer Genome Atlas Thyroid Cancer (TCGA-THCA) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0Learn more about the\\u00a0CIP TCGA Radiology Initiative.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Thyroid Cancer\"],\"cancer_locations\":[\"Thyroid\"],\"data_types\":[\"CT\",\"PT\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":6,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-THCA\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_TCGA-THCA_09-16-2015.tcia\",\"blob\":\"the cancer genome atlas thyroid cancer collection tcga-thca the cancer genome atlas thyroid cancer (tcga-thca) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. \\u00a0tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. \\u00a0for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. \\u00a0in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. \\u00a0learn more about the\\u00a0cip tcga radiology initiative. thyroid cancer thyroid ct pt clinical genomics histopathology tcga\"},{\"id\":44377,\"type\":\"collection\",\"slug\":\"tcga-ucec\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/tcga-ucec\\\/\",\"title\":\"The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma Collection\",\"short_title\":\"TCGA-UCEC\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.GKJ0ZWAC\",\"summary\":\"The Cancer Genome Atlas Uterine Corpus Endometrial Carcinoma (TCGA-UCEC) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0The Cancer Genome Atlas (TCGA). Clinical, genetic, and pathological data resides in the\\u00a0Genomic Data Commons (GDC) Data Portal while the radiological data is stored on The Cancer Imaging Archive (TCIA).\\u00a0Matched TCGA patient identifiers allow researchers to explore the TCGA\\\/TCIA databases for correlations between tissue genotype, radiological phenotype and patient outcomes. Tissues for TCGA were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. For this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. In most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0CIP TCGA Radiology InitiativeImaging Source Site (ISS) Groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. Modeled after TCGA analysis groups, ISS groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. This opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. Learn more about the\\u00a0CIP TCGA Radiology Initiative.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Uterine Corpus Endometrial Carcinoma\"],\"cancer_locations\":[\"Uterus\"],\"data_types\":[\"CT\",\"PT\",\"MR\",\"CR\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Histopathology\"],\"species\":[\"Human\"],\"subjects\":65,\"date_updated\":\"2020-05-29\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-UCEC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCIA_TCGA-UCEC-2018-10-24.tcia\",\"blob\":\"the cancer genome atlas uterine corpus endometrial carcinoma collection tcga-ucec the cancer genome atlas uterine corpus endometrial carcinoma (tcga-ucec) data collection is part of a larger effort to\\u00a0build a research community focused on connecting cancer phenotypes to genotypes by providing clinical images matched to subjects from\\u00a0the cancer genome atlas (tcga). clinical, genetic, and pathological data resides in the\\u00a0genomic data commons (gdc) data portal while the radiological data is stored on the cancer imaging archive (tcia).\\u00a0matched tcga patient identifiers allow researchers to explore the tcga\\\/tcia databases for correlations between tissue genotype, radiological phenotype and patient outcomes. tissues for tcga were collected from many sites all over the world in order to reach their accrual targets, usually around 500 specimens per cancer type. for this reason the image data sets are also extremely heterogeneous in terms of scanner modalities, manufacturers and acquisition protocols. in most cases the images were acquired as part of routine care and not as part of a controlled research study or clinical trial.\\u00a0cip tcga radiology initiativeimaging source site (iss) groups are being populated and governed by participants from institutions that have provided imaging data to the archive for a given cancer type. modeled after tcga analysis groups, iss groups are given the opportunity to publish a marker paper for a given cancer type\\u00a0per\\u00a0the guidelines in the table above. this opportunity will generate increased participation in building these multi-institutional data sets as they become an open community resource. learn more about the\\u00a0cip tcga radiology initiative. uterine corpus endometrial carcinoma uterus ct pt mr cr clinical genomics histopathology tcga\"},{\"id\":48725,\"type\":\"collection\",\"slug\":\"testing-page\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/testing-page\\\/\",\"title\":\"Testing Page\",\"short_title\":\"TEST-PAGE\",\"doi\":\"10.25737\\\/pzaf-wa11\",\"summary\":\"test\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[],\"cancer_locations\":[],\"data_types\":[\"CT\",\"MR\",\"PT\",\"US\",\"NM\",\"XA\",\"MG\",\"DX\",\"Histopathology\",\"Other\"],\"supporting_data\":[],\"species\":[],\"subjects\":0,\"date_updated\":\"2025-02-20\",\"status\":\"Ongoing\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/APOLLO-5-DA-RAD-3.tcia\",\"blob\":\"testing page test-page test ct mr pt us nm xa mg dx histopathology other community\"},{\"id\":45385,\"type\":\"collection\",\"slug\":\"ucsf-pdgm\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/ucsf-pdgm\\\/\",\"title\":\"The University of California San Francisco Preoperative Diffuse Glioma MRI\",\"short_title\":\"UCSF-PDGM\",\"doi\":\"10.7937\\\/tcia.bdgf-8v37\",\"summary\":\"IntroductionMRI-based artificial intelligence (AI) research on patients with brain gliomas has been rapidly increasing in popularity in recent years in part due to a growing number of publicly available MRI datasets. Notable examples include The Cancer Genome Atlas Glioblastoma dataset (TCGA-GBM) consisting of 262 subjects and the International Brain Tumor Segmentation (BraTS) challenge dataset consisting of 542 subjects (including 243 preoperative cases from TCGA-GBM). The public availability of these glioma MRI datasets has fostered the growth of numerous emerging AI techniques including automated tumor segmentation, radiogenomics, and MRI-based survival prediction. Despite these advances, existing publicly available glioma MRI datasets have been largely limited to only 4 MRI contrasts (T2, T2\\\/FLAIR, and T1 pre- and post-contrast) and imaging protocols vary significantly in terms of magnetic field strength and acquisition parameters. Here we present the University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) dataset. The UCSF-PDGM dataset includes 501 subjects with histopathologically-proven diffuse gliomas who were imaged with a standardized 3 Tesla preoperative brain tumor MRI protocol featuring predominantly 3D imaging, as well as advanced diffusion and perfusion imaging techniques. The dataset also includes isocitrate dehydrogenase (IDH) mutation status for all cases and O[6]-methylguanine-DNA methyltransferase (MGMT) promotor methylation status for World Health Organization (WHO) grade III and IV gliomas. The UCSF-PDGM has been made publicly available in the hopes that researchers around the world will use these data to continue to push the boundaries of AI applications for diffuse gliomas.MethodsPatient PopulationData collection was performed in accordance with relevant guidelines and regulations and was approved by the University of California San Francisco institutional review board with a waiver for consent. The dataset population consisted of 501* adult patients with histopathologically confirmed grade II-IV diffuse gliomas who underwent preoperative MRI, initial tumor resection, and tumor genetic testing at a single medical center between 2015 and 2021. Patients with any prior history of brain tumor treatment were excluded; however, history of tumor biopsy was not considered an exclusion criterion.Genetic Biomarker TestingAll subjects\\u2019 tumors were tested for IDH mutations by genetic sequencing of tissue acquired during biopsy or resection. All grade III and IV tumors were tested for MGMT methylation status using a methylation sensitive quantitative PCR assay.Study participant demographic dataThe 501* cases included in the UCSF-PDGM include 55 (11%) grade II, 42 (9%) grade III, and 403 (80%) grade IV tumors. There was a male predominance for all tumor grades (56%, 60%, and 60%, respectively for grades II-IV). IDH mutations were identified in a majority of grade II (83%) and grade III (67%) tumors and a small minority of grade IV tumors (8%). MGMT promoter hypermethylation was detected in 63% of grade IV gliomas and was not tested for in a majority of lower grade gliomas. 1p\\\/19q codeletion was detected in 20% of grade II tumors and a small minority of grade III (5%) and IV (<1%) tumors. Tabulated details and glossary are available in the Data Access and Detailed Description tabs below.Image AcquisitionAll preoperative MRI was performed on a 3.0 tesla scanner (Discovery 750, GE Healthcare, Waukesha, Wisconsin, USA) and a dedicated 8-channel head coil (Invivo, Gainesville, Florida, USA). The imaging protocol included 3D T2-weighted, T2\\\/FLAIR-weighted, susceptibility-weighted (SWI), diffusion-weighted (DWI), pre- and post-contrast T1-weighted images, 3D arterial spin labeling (ASL) perfusion images, and 2D 55-direction high angular resolution diffusion imaging (HARDI). Over the study period, two gadolinium-based contrast agents were used: gadobutrol (Gadovist, Bayer, LOC) at a dose of 0.1 mL\\\/kg and gadoterate (Dotarem, Guerbet, Aulnay-sous-Bois, France) at a dose of 0.2 mL\\\/kg.Image Pre-ProcessingHARDI data were eddy current corrected and processed using the Eddy and DTIFIT modules from FSL 6.0.2 yielding isotropic diffusion weighted images (DWI) and several quantitative diffusivity maps: mean diffusivity (MD), axial diffusivity (AD), radial diffusivity (RD), and fractional anisotropy (FA). Eddy correction was performed with outlier replacement on and topup correction off. DTIFIT was performed with simple least squares regression. Each image contrast was registered and resampled to the 3D space defined by the T2\\\/FLAIR image (1 mm isotropic resolution) using automated non-linear registration (Advanced Normalization Tools). Resampled co-registered data were then skull stripped using a previously described and publicly available deep-learning algorithm:\\u00a0https:\\\/\\\/www.github.com\\\/ecalabr\\\/brain_mask\\\/.Tumor SegmentationMulticompartment tumor segmentation of study data was undertaken as part of the 2021 BraTS challenge. Briefly, image data first underwent automated segmentation using an ensemble model consisting of prior BraTS challenge winning segmentation algorithms. Images were then manually corrected by trained radiologists and approved by 2 expert reviewers. Segmentation included three major tumor compartments: enhancing tumor, non-enhancing\\\/necrotic tumor, and surrounding FLAIR abnormality (sometimes referred to as edema).The UCSF-PDGM adds to on an existing body of publicly available diffuse glioma MRI datasets that are commonly used in AI research applications. As MRI-based AI research applications continue to grow, new data are needed to foster development of new techniques and increase the generalizability of existing algorithms. The UCSF-PDGM not only significantly increases the total number of publicly available diffuse glioma MRI cases, but also provides a unique contribution in terms of MRI technique. The inclusion of 3D sequences and advanced MRI techniques like ASL and HARDI provides a new opportunity for researchers to explore the potential utility of cutting-edge clinical diagnostics for AI applications. In addition, these advanced imaging techniques may prove useful for radiogenomic studies focused on identification of IDH mutations or MGMT promoter methylation.The UCSF-PDGM dataset, particularly when combined with existing publicly available datasets, has the potential to fuel the next phase of radiologic AI research on diffuse gliomas. However, the UCSF-PDGM dataset\\u2019s potential will only be realized if the radiology AI research community takes advantage of this new data resource. We hope that this dataset sparks inspiration in the next generation of AI researchers, and we look forward to the new techniques and discoveries that the UCSF-PDGM will generate.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/UCSF-PDGM_figure_2.png\",\"program\":\"Community\",\"cancer_types\":[\"Diffuse Glioma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"Demographic\",\"Molecular Test\",\"Diagnosis\",\"Classification\",\"Other\",\"Follow-Up\",\"Measurement\"],\"supporting_data\":[\"Clinical\",\"Genomics\",\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":495,\"date_updated\":\"2023-04-07\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc1OCIsInBhc3Njb2RlIjoiYTQ5MTc4MTEzZWZmY2EwNjY3ZTlhY2JmODA2NDM2NzE0MGY3YTQxYyIsInBhY2thZ2VfaWQiOiI3NTgiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"the university of california san francisco preoperative diffuse glioma mri ucsf-pdgm introductionmri-based artificial intelligence (ai) research on patients with brain gliomas has been rapidly increasing in popularity in recent years in part due to a growing number of publicly available mri datasets. notable examples include the cancer genome atlas glioblastoma dataset (tcga-gbm) consisting of 262 subjects and the international brain tumor segmentation (brats) challenge dataset consisting of 542 subjects (including 243 preoperative cases from tcga-gbm). the public availability of these glioma mri datasets has fostered the growth of numerous emerging ai techniques including automated tumor segmentation, radiogenomics, and mri-based survival prediction. despite these advances, existing publicly available glioma mri datasets have been largely limited to only 4 mri contrasts (t2, t2\\\/flair, and t1 pre- and post-contrast) and imaging protocols vary significantly in terms of magnetic field strength and acquisition parameters. here we present the university of california san francisco preoperative diffuse glioma mri (ucsf-pdgm) dataset. the ucsf-pdgm dataset includes 501 subjects with histopathologically-proven diffuse gliomas who were imaged with a standardized 3 tesla preoperative brain tumor mri protocol featuring predominantly 3d imaging, as well as advanced diffusion and perfusion imaging techniques. the dataset also includes isocitrate dehydrogenase (idh) mutation status for all cases and o[6]-methylguanine-dna methyltransferase (mgmt) promotor methylation status for world health organization (who) grade iii and iv gliomas. the ucsf-pdgm has been made publicly available in the hopes that researchers around the world will use these data to continue to push the boundaries of ai applications for diffuse gliomas.methodspatient populationdata collection was performed in accordance with relevant guidelines and regulations and was approved by the university of california san francisco institutional review board with a waiver for consent. the dataset population consisted of 501* adult patients with histopathologically confirmed grade ii-iv diffuse gliomas who underwent preoperative mri, initial tumor resection, and tumor genetic testing at a single medical center between 2015 and 2021. patients with any prior history of brain tumor treatment were excluded; however, history of tumor biopsy was not considered an exclusion criterion.genetic biomarker testingall subjects\\u2019 tumors were tested for idh mutations by genetic sequencing of tissue acquired during biopsy or resection. all grade iii and iv tumors were tested for mgmt methylation status using a methylation sensitive quantitative pcr assay.study participant demographic datathe 501* cases included in the ucsf-pdgm include 55 (11%) grade ii, 42 (9%) grade iii, and 403 (80%) grade iv tumors. there was a male predominance for all tumor grades (56%, 60%, and 60%, respectively for grades ii-iv). idh mutations were identified in a majority of grade ii (83%) and grade iii (67%) tumors and a small minority of grade iv tumors (8%). mgmt promoter hypermethylation was detected in 63% of grade iv gliomas and was not tested for in a majority of lower grade gliomas. 1p\\\/19q codeletion was detected in 20% of grade ii tumors and a small minority of grade iii (5%) and iv (<1%) tumors. tabulated details and glossary are available in the data access and detailed description tabs below.image acquisitionall preoperative mri was performed on a 3.0 tesla scanner (discovery 750, ge healthcare, waukesha, wisconsin, usa) and a dedicated 8-channel head coil (invivo, gainesville, florida, usa). the imaging protocol included 3d t2-weighted, t2\\\/flair-weighted, susceptibility-weighted (swi), diffusion-weighted (dwi), pre- and post-contrast t1-weighted images, 3d arterial spin labeling (asl) perfusion images, and 2d 55-direction high angular resolution diffusion imaging (hardi). over the study period, two gadolinium-based contrast agents were used: gadobutrol (gadovist, bayer, loc) at a dose of 0.1 ml\\\/kg and gadoterate (dotarem, guerbet, aulnay-sous-bois, france) at a dose of 0.2 ml\\\/kg.image pre-processinghardi data were eddy current corrected and processed using the eddy and dtifit modules from fsl 6.0.2 yielding isotropic diffusion weighted images (dwi) and several quantitative diffusivity maps: mean diffusivity (md), axial diffusivity (ad), radial diffusivity (rd), and fractional anisotropy (fa). eddy correction was performed with outlier replacement on and topup correction off. dtifit was performed with simple least squares regression. each image contrast was registered and resampled to the 3d space defined by the t2\\\/flair image (1 mm isotropic resolution) using automated non-linear registration (advanced normalization tools). resampled co-registered data were then skull stripped using a previously described and publicly available deep-learning algorithm:\\u00a0https:\\\/\\\/www.github.com\\\/ecalabr\\\/brain_mask\\\/.tumor segmentationmulticompartment tumor segmentation of study data was undertaken as part of the 2021 brats challenge. briefly, image data first underwent automated segmentation using an ensemble model consisting of prior brats challenge winning segmentation algorithms. images were then manually corrected by trained radiologists and approved by 2 expert reviewers. segmentation included three major tumor compartments: enhancing tumor, non-enhancing\\\/necrotic tumor, and surrounding flair abnormality (sometimes referred to as edema).the ucsf-pdgm adds to on an existing body of publicly available diffuse glioma mri datasets that are commonly used in ai research applications. as mri-based ai research applications continue to grow, new data are needed to foster development of new techniques and increase the generalizability of existing algorithms. the ucsf-pdgm not only significantly increases the total number of publicly available diffuse glioma mri cases, but also provides a unique contribution in terms of mri technique. the inclusion of 3d sequences and advanced mri techniques like asl and hardi provides a new opportunity for researchers to explore the potential utility of cutting-edge clinical diagnostics for ai applications. in addition, these advanced imaging techniques may prove useful for radiogenomic studies focused on identification of idh mutations or mgmt promoter methylation.the ucsf-pdgm dataset, particularly when combined with existing publicly available datasets, has the potential to fuel the next phase of radiologic ai research on diffuse gliomas. however, the ucsf-pdgm dataset\\u2019s potential will only be realized if the radiology ai research community takes advantage of this new data resource. we hope that this dataset sparks inspiration in the next generation of ai researchers, and we look forward to the new techniques and discoveries that the ucsf-pdgm will generate. diffuse glioma brain mr demographic molecular test diagnosis classification other follow-up measurement clinical genomics image analyses software\\\/source code community\"},{\"id\":42917,\"type\":\"collection\",\"slug\":\"upenn-gbm\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/upenn-gbm\\\/\",\"title\":\"Multi-parametric magnetic resonance imaging (mpMRI) scans for de novo Glioblastoma (GBM) patients from the University of Pennsylvania Health System\",\"short_title\":\"UPENN-GBM\",\"doi\":\"10.7937\\\/TCIA.709X-DN49\",\"summary\":\"This collection comprises multi-parametric magnetic resonance imaging (mpMRI) scans for de novo Glioblastoma (GBM) patients from the University of Pennsylvania Health System, coupled with patient demographics, clinical outcome (e.g., overall survival, genomic information, tumor progression), as well as computer-aided and manually-corrected segmentation labels of multiple histologically distinct tumor sub-regions, computer-aided and manually-corrected segmentations of the whole brain, a rich panel of radiomic features along with their corresponding co-registered mpMRI volumes in NIfTI format. Scans were initially skull-stripped and co-registered, before their tumor segmentation labels were produced by an automated computational method. These segmentation labels were revised and any label misclassifications were manually corrected\\\/approved by expert board-certified neuroradiologists. The final labels were used to extract a rich panel of imaging features, including intensity, volumetric, morphologic, histogram-based and textural parameters. The segmentation labels enable quantitative computational and clinical studies without the need to repeat manual annotations whilst allowing for comparison across studies. They can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational challenges. The provided panel of radiomic features may facilitate research integrative of the molecular characterization offered, and hence allow associations with molecular markers (radiogenomic biomarker research), clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features. Additional data accompanying the UPENN-GBM data collection include H&E-stained digitized tissue sections from resected tumor specimens of matched de novo and recurrent cases for a few of the patients in this collection.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image2021-7-15_9-51-22.png\",\"program\":\"Community\",\"cancer_types\":[\"Glioblastoma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"Segmentation\",\"Histopathology\",\"Molecular Test\",\"Demographic\",\"Other\",\"Radiomic Feature\"],\"supporting_data\":[\"Clinical\",\"Image Analyses\"],\"species\":[\"Human\"],\"subjects\":630,\"date_updated\":\"2022-10-24\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=UPENN-GBM\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/UPENN-GBM_DownloadManifest20221129.tcia\",\"blob\":\"multi-parametric magnetic resonance imaging (mpmri) scans for de novo glioblastoma (gbm) patients from the university of pennsylvania health system upenn-gbm this collection comprises multi-parametric magnetic resonance imaging (mpmri) scans for de novo glioblastoma (gbm) patients from the university of pennsylvania health system, coupled with patient demographics, clinical outcome (e.g., overall survival, genomic information, tumor progression), as well as computer-aided and manually-corrected segmentation labels of multiple histologically distinct tumor sub-regions, computer-aided and manually-corrected segmentations of the whole brain, a rich panel of radiomic features along with their corresponding co-registered mpmri volumes in nifti format. scans were initially skull-stripped and co-registered, before their tumor segmentation labels were produced by an automated computational method. these segmentation labels were revised and any label misclassifications were manually corrected\\\/approved by expert board-certified neuroradiologists. the final labels were used to extract a rich panel of imaging features, including intensity, volumetric, morphologic, histogram-based and textural parameters. the segmentation labels enable quantitative computational and clinical studies without the need to repeat manual annotations whilst allowing for comparison across studies. they can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational challenges. the provided panel of radiomic features may facilitate research integrative of the molecular characterization offered, and hence allow associations with molecular markers (radiogenomic biomarker research), clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features. additional data accompanying the upenn-gbm data collection include h&e-stained digitized tissue sections from resected tumor specimens of matched de novo and recurrent cases for a few of the patients in this collection. glioblastoma brain mr segmentation histopathology molecular test demographic other radiomic feature clinical image analyses community\"},{\"id\":50047,\"type\":\"collection\",\"slug\":\"varepop-apollo\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/varepop-apollo\\\/\",\"title\":\"VA Research Precision Oncology Program - APOLLO\",\"short_title\":\"VAREPOP-APOLLO\",\"doi\":\"10.7937\\\/ghkn-md15\",\"summary\":\"The Research for Precision Oncology Program (RePOP) is a research activity that established a cohort of Veterans diagnosed with cancer and had genomic analyses performed on their tumor tissue as part of the standard of care. All data relevant to a patient\\u2019s cancer and cancer care were collected under RePOP, including patient demographics, comorbidities, genomic analysis, treatments, medications, lab values, imaging studies, and outcomes. All RePOP participants signed informed consent and signed HIPAA authorization to have their data stored and shared from RePOP\\u2019s Precision Oncology Program Data Repository (PODR).Cancer TypeModalitiesSubjectsEsophageal CarcinomaCT, PT2Lung Squamous Cell CarcinomaCT, MR, PT17Lung AdenocarcinomaCT, MR, PT9Lung OtherCT, PT2Pancreatic AdenocarcinomaCT, MR, PT1ThymomaCT, MR, PT1\",\"image\":false,\"program\":\"APOLLO\",\"cancer_types\":[\"Esophageal Carcinoma\",\"Lung Squamous Cell Carcinoma\",\"Lung Adenocarcinoma\",\"Lung Other\",\"Pancreatic adenocarcinoma\",\"Thymoma\"],\"cancer_locations\":[\"Esophagus\",\"Lung\",\"Pancreas\",\"Thymus\"],\"data_types\":[\"CT\",\"MR\",\"PT\",\"Diagnosis\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":32,\"date_updated\":\"2024-12-06\",\"status\":\"Ongoing\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=VAREPOP-APOLLO\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/VAREPOP-APOLLO-DA-RAD_1224.tcia\",\"blob\":\"va research precision oncology program - apollo varepop-apollo the research for precision oncology program (repop) is a research activity that established a cohort of veterans diagnosed with cancer and had genomic analyses performed on their tumor tissue as part of the standard of care. all data relevant to a patient\\u2019s cancer and cancer care were collected under repop, including patient demographics, comorbidities, genomic analysis, treatments, medications, lab values, imaging studies, and outcomes. all repop participants signed informed consent and signed hipaa authorization to have their data stored and shared from repop\\u2019s precision oncology program data repository (podr).cancer typemodalitiessubjectsesophageal carcinomact, pt2lung squamous cell carcinomact, mr, pt17lung adenocarcinomact, mr, pt9lung otherct, pt2pancreatic adenocarcinomact, mr, pt1thymomact, mr, pt1 esophageal carcinoma lung squamous cell carcinoma lung adenocarcinoma lung other pancreatic adenocarcinoma thymoma esophagus lung pancreas thymus ct mr pt diagnosis apollo\"},{\"id\":43543,\"type\":\"collection\",\"slug\":\"vestibular-schwannoma-mc-rc\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/vestibular-schwannoma-mc-rc\\\/\",\"title\":\"Segmentation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Annotated Multi-Center Routine Clinical Dataset\",\"short_title\":\"Vestibular-Schwannoma-MC-RC\",\"doi\":\"10.7937\\\/HRZH-2N82\",\"summary\":\"The Vestibular-Schwannoma-MC-RC (VS-MC-RC) dataset contains longitudinal MRI scans of 160 patients with a single sporadic Vestibular Schwannoma (VS) from 10 medical sites in the United Kingdom. For all patients either a T1-weighted (T1w) or T2-weighted (T2w) scan, or both, are available. The dataset provides binary segmentations of the VS for all scan sessions. The dataset comprises up to three time points for each patient resulting in a total of 427 timepoints and 487 3D-images. These timepoints represent the first, central, and last MRI exams that the patients underwent. For 60 timepoints, both T1w and T2w are available, for 64 timepoints only T1w, and for 303 timepoints only T2w. Manual ground truth segmentations were obtained in an iterative process in which segmentations were: 1) produced or amended by a specialized company; and 2) reviewed by one of three trained radiologists; and 3) validated by an expert team. Using this dataset, researchers can develop and validate methods, for example, for automatic surveillance of Vestibular Schwannoma, which work robustly on images acquired at different hospitals.Pre-processingThe MRI images are provided in DICOM format. All images were defaced using automatic affine registration or manual landmark registration with BRAINSFit and the template and face mask provided in pydeface. Segmentations are provided in NIfTI formatComparison to VS-SEG datasetCompared to the Vestibular-Schwannoma-SEG (VS-SEG) dataset which was acquired with a standardized protocol for radiation treatment planning on a single scanner at a single site, the VS-MC-RC dataset aims to capture the diversity of MRI scans encountered in a routine clinical setting; it contains a wide range of scan protocols and was acquired on multiple scanners by different manufacturers. In addition, the tumour manifestation in scans of this dataset is more diverse because it includes tumours at early stages and post-operative tumour residuals rather than only tumours specifically selected for radiation therapy.Patient overlap with VS-SEG dataset58 patients of the VS-MC-RC dataset were previously included in the VS-SEG dataset. These patients have a Patient ID that begins with \\u201cVS-SEG- \\u201c. Patient IDs of patients that are not part of the VS-SEG dataset begin with \\u201cVS-MC-RC-\\u201d. Please note that for most patients that are part of both datasets, the timepoints and MRI series included in one dataset are different from those included in the other dataset. However, 8 series are included in both datasets. These series are listed in the CSV file below. Please note that the VS-SEG dataset was not exported with the same software as the VS-MC-RC dataset. Therefore, the metadata of overlapping series might differ. Furthermore, different defacing algorithms were used so that pixel data of overlapping series are not the same in affected areas.Embargoed data This dataset is part of the challenge \\u201cCross-Modality Domain Adaptation for Medical Image Segmentation\\u201d (crossMoDA). 126 timepoints that were included in the validation and test set of the challenge are currently under embargo and will be released in August 2024. Until then, 301 timepoints from 124 patients are available for download.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/VS-MC-RC-data_annotation_pipeline-v2-scaled.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Vestibular Schwannoma (non-cancer)\"],\"cancer_locations\":[\"Ear\"],\"data_types\":[\"MR\",\"Segmentation\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":124,\"date_updated\":\"2023-08-23\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Vestibular-Schwannoma-MC-RC\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Vestibular-Schwannooma-MC-RC-manifest-August-2023.tcia\",\"blob\":\"segmentation of vestibular schwannoma from magnetic resonance imaging: an annotated multi-center routine clinical dataset vestibular-schwannoma-mc-rc the vestibular-schwannoma-mc-rc (vs-mc-rc) dataset contains longitudinal mri scans of 160 patients with a single sporadic vestibular schwannoma (vs) from 10 medical sites in the united kingdom. for all patients either a t1-weighted (t1w) or t2-weighted (t2w) scan, or both, are available. the dataset provides binary segmentations of the vs for all scan sessions. the dataset comprises up to three time points for each patient resulting in a total of 427 timepoints and 487 3d-images. these timepoints represent the first, central, and last mri exams that the patients underwent. for 60 timepoints, both t1w and t2w are available, for 64 timepoints only t1w, and for 303 timepoints only t2w. manual ground truth segmentations were obtained in an iterative process in which segmentations were: 1) produced or amended by a specialized company; and 2) reviewed by one of three trained radiologists; and 3) validated by an expert team. using this dataset, researchers can develop and validate methods, for example, for automatic surveillance of vestibular schwannoma, which work robustly on images acquired at different hospitals.pre-processingthe mri images are provided in dicom format. all images were defaced using automatic affine registration or manual landmark registration with brainsfit and the template and face mask provided in pydeface. segmentations are provided in nifti formatcomparison to vs-seg datasetcompared to the vestibular-schwannoma-seg (vs-seg) dataset which was acquired with a standardized protocol for radiation treatment planning on a single scanner at a single site, the vs-mc-rc dataset aims to capture the diversity of mri scans encountered in a routine clinical setting; it contains a wide range of scan protocols and was acquired on multiple scanners by different manufacturers. in addition, the tumour manifestation in scans of this dataset is more diverse because it includes tumours at early stages and post-operative tumour residuals rather than only tumours specifically selected for radiation therapy.patient overlap with vs-seg dataset58 patients of the vs-mc-rc dataset were previously included in the vs-seg dataset. these patients have a patient id that begins with \\u201cvs-seg- \\u201c. patient ids of patients that are not part of the vs-seg dataset begin with \\u201cvs-mc-rc-\\u201d. please note that for most patients that are part of both datasets, the timepoints and mri series included in one dataset are different from those included in the other dataset. however, 8 series are included in both datasets. these series are listed in the csv file below. please note that the vs-seg dataset was not exported with the same software as the vs-mc-rc dataset. therefore, the metadata of overlapping series might differ. furthermore, different defacing algorithms were used so that pixel data of overlapping series are not the same in affected areas.embargoed data this dataset is part of the challenge \\u201ccross-modality domain adaptation for medical image segmentation\\u201d (crossmoda). 126 timepoints that were included in the validation and test set of the challenge are currently under embargo and will be released in august 2024. until then, 301 timepoints from 124 patients are available for download. vestibular schwannoma (non-cancer) ear mr segmentation other community\"},{\"id\":43563,\"type\":\"collection\",\"slug\":\"vestibular-schwannoma-seg\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/vestibular-schwannoma-seg\\\/\",\"title\":\"Segmentation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Open Annotated Dataset and Baseline Algorithm\",\"short_title\":\"Vestibular-Schwannoma-SEG\",\"doi\":\"10.7937\\\/TCIA.9YTJ-5Q73\",\"summary\":\"This collection contains a labeled dataset of MRI images collected on 242\\u00a0consecutive patients with vestibular schwannoma (VS) undergoing Gamma Knife stereotactic radiosurgery (GK SRS). The structural images included contrast-enhanced T1-weighted (ceT1) images and high-resolution T2-weighted (hrT2) images. Each imaging dataset is accompanied by the patient\\u2019s radiation therapy (RT) dataset including the RTDose, RTStructures and RTPlan. Additionally, registration matrices (.tfm format) and segmentation contour lines (JSON format) are provided and described\\u00a0below.\\u00a0All structures were manually segmented in consensus by the treating neurosurgeon and physicist using both the ceT1 and hrT2 images. The value of this collection is to provide clinical image data including fully annotated tumour segmentations to facilitate the development and validation of automated segmentation frameworks. It may also be used for research relating to radiation treatment.Imaging data from consecutive patients with a single sporadic VS treated with GK SRS between the years of\\u00a02012 and 2018\\u00a0were screened for the study. All adult patients older than 18 years with a single unilateral VS treated with GK SRS were eligible for inclusion in the study, including patients who had previously undergone operative surgical treatment. In total, 248 patients met these initial inclusion criteria including 51 patients who had previously undergone surgery. Patients were only included in the study if their pre-treatment image acquisition dataset was complete;\\u00a04 patients were thus excluded because of incomplete datasets and 2 further patients were excluded because the tumour was not completely contained in the image field of view.\\u00a0The images were obtained on a 32-channel Siemens Avanto 1.5T scanner using a Siemens single-channel head coil.Contrast-enhanced T1-weighted imaging was performed with an MPRAGE sequence with TR \\\/ TE \\\/ TI = 1900 ms \\\/ 2.97 ms \\\/ 1100 ms, in-plane resolution of 0.4 \\u00d7 0.4 mm, in-plane matrix of 512 \\u00d7 512, and slice thickness of 1.0\\u20131.5 mmHigh-resolution T2-weighted imaging was performed with a 3D CISS or FIESTA sequence with TR \\\/ TE 9.4 ms \\\/ 4.23 ms, in-plane resolution of approximately 0.5 \\u00d7 0.5 mm, an in-plane matrix of 384 \\u00d7 384 or 448 \\u00d7 448, and slice thickness of 1.0\\u20131.5 mm.All manual segmentations were performed using Gamma Knife planning software (Leksell GammaPlan, Elekta, Sweden) that employs an in-plane semiautomated segmentation method. Using this software, each axial slice was manually segmented in turn.Registration matrices and\\u00a0JSON contoursRegistration Matrices:\\u00a0For each subject and each modality there is a text file named\\u00a0inv_T1_LPS_to_T2_LPS.tfm or\\u00a0inv_T2_LPS_to_T1_LPS.tfm.\\u00a0The files\\u00a0specify affine transformation matrices that can be used to co-register the T1 image to the\\u00a0T2 image and vice versa.\\u00a0The file format is a standard format defined by the Insight Toolkit (ITK) library.\\u00a0The matrices are\\u00a0the result of the co-registration of fiducials of the Leksell Stereotactic System MR Indicator box into which the patient\\u2019s head\\u00a0is fixed during image acquisition. The localization of fiducials and co-registration was performed automatically by the LeksellGammaPlan software.Contours:\\u00a0For each subject and each modality there is a text file named\\u00a0contours.json.\\u00a0These\\u00a0contour files in the T1 and T2 folder contain\\u00a0the contour points of the segmented structures in\\u00a0JavaScript Object Notation (JSON) format, mapped in the coordinate frames of the T1 image and the T2 image, respectively.\\u00a0There can be small differences between the contour points of the RTSTRUCT and the contour points of the JSON files as explained in the following:In most cases, the tumour was segmented on the T1 image while the cochlea was typically segmented on the T2 image. This\\u00a0meant that some contour lines (typically for the tumour) were coplanar with the slices of the T1 image while others (typically\\u00a0for the cochlea) were coplanar with T2 slices. After co-registration, the (un-resampled) slices of the T1 and T2 image generally\\u00a0did not coincide; for example,\\u00a0due to different image position and, occasionally, slice thickness. Therefore, the combined co-registered contour lines were neither jointly coplanar with the T1 nor with the T2 image slices.\\u00a0Upon export of the segmentations in a given target\\u00a0space, the\\u00a0LeksellGammaPlan\\u00a0software interpolates between the original contour lines to create new slice-aligned contour lines\\u00a0in the target image space (T1 or T2). This results in the interpolated slice-aligned contour lines found in the RTSTRUCTs. In contrast, the contours in the JSON files were not interpolated after co-registration, and therefore describe the original\\u00a0(potentially off-target-space-slice) manual segmentation accurately.Related softwarePlease see the github respository link which contains a script to organize the downloaded data into a more convenient folder structure and a script to\\u00a0perform\\u00a0co-registration based on the .tfm files\\u00a0and to convert the downloaded DICOM images and JSON contours into NIFTI format. Moreover, the repository contains an algorithm for automatic segmentation of VS with deep learning, adapted to this data set. The applied neural network is based on the 2.5D UNet described in \\\"An artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced T1-weighted and high-resolution T2-weighted MRI\\\" (Shapey, et al., 2021)\\u00a0and has been adapted to yield improved segmentation results. Our implementation uses MONAI, a freely available, PyTorch-based framework for deep learning in healthcare imaging (Project MONAI). This new implementation was devised to provide a starting point for researchers interested in automatic segmentation using state-of-the art deep learning frameworks for medical image processing.\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/VS_example_with_caption.png\",\"program\":\"Community\",\"cancer_types\":[\"Vestibular Schwannoma (non-cancer)\"],\"cancer_locations\":[\"Ear\"],\"data_types\":[\"MR\",\"RTDOSE\",\"RTSTRUCT\",\"RTPLAN\",\"Other\",\"Measurement\",\"Radiomic Feature\"],\"supporting_data\":[\"Image Analyses\",\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":242,\"date_updated\":\"2021-03-17\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=Vestibular-Schwannoma-SEG\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Vestibular-Schwannoma-SEG-Feb-2021-manifest.tcia\",\"blob\":\"segmentation of vestibular schwannoma from magnetic resonance imaging: an open annotated dataset and baseline algorithm vestibular-schwannoma-seg this collection contains a labeled dataset of mri images collected on 242\\u00a0consecutive patients with vestibular schwannoma (vs) undergoing gamma knife stereotactic radiosurgery (gk srs). the structural images included contrast-enhanced t1-weighted (cet1) images and high-resolution t2-weighted (hrt2) images. each imaging dataset is accompanied by the patient\\u2019s radiation therapy (rt) dataset including the rtdose, rtstructures and rtplan. additionally, registration matrices (.tfm format) and segmentation contour lines (json format) are provided and described\\u00a0below.\\u00a0all structures were manually segmented in consensus by the treating neurosurgeon and physicist using both the cet1 and hrt2 images. the value of this collection is to provide clinical image data including fully annotated tumour segmentations to facilitate the development and validation of automated segmentation frameworks. it may also be used for research relating to radiation treatment.imaging data from consecutive patients with a single sporadic vs treated with gk srs between the years of\\u00a02012 and 2018\\u00a0were screened for the study. all adult patients older than 18 years with a single unilateral vs treated with gk srs were eligible for inclusion in the study, including patients who had previously undergone operative surgical treatment. in total, 248 patients met these initial inclusion criteria including 51 patients who had previously undergone surgery. patients were only included in the study if their pre-treatment image acquisition dataset was complete;\\u00a04 patients were thus excluded because of incomplete datasets and 2 further patients were excluded because the tumour was not completely contained in the image field of view.\\u00a0the images were obtained on a 32-channel siemens avanto 1.5t scanner using a siemens single-channel head coil.contrast-enhanced t1-weighted imaging was performed with an mprage sequence with tr \\\/ te \\\/ ti = 1900 ms \\\/ 2.97 ms \\\/ 1100 ms, in-plane resolution of 0.4 \\u00d7 0.4 mm, in-plane matrix of 512 \\u00d7 512, and slice thickness of 1.0\\u20131.5 mmhigh-resolution t2-weighted imaging was performed with a 3d ciss or fiesta sequence with tr \\\/ te 9.4 ms \\\/ 4.23 ms, in-plane resolution of approximately 0.5 \\u00d7 0.5 mm, an in-plane matrix of 384 \\u00d7 384 or 448 \\u00d7 448, and slice thickness of 1.0\\u20131.5 mm.all manual segmentations were performed using gamma knife planning software (leksell gammaplan, elekta, sweden) that employs an in-plane semiautomated segmentation method. using this software, each axial slice was manually segmented in turn.registration matrices and\\u00a0json contoursregistration matrices:\\u00a0for each subject and each modality there is a text file named\\u00a0inv_t1_lps_to_t2_lps.tfm or\\u00a0inv_t2_lps_to_t1_lps.tfm.\\u00a0the files\\u00a0specify affine transformation matrices that can be used to co-register the t1 image to the\\u00a0t2 image and vice versa.\\u00a0the file format is a standard format defined by the insight toolkit (itk) library.\\u00a0the matrices are\\u00a0the result of the co-registration of fiducials of the leksell stereotactic system mr indicator box into which the patient\\u2019s head\\u00a0is fixed during image acquisition. the localization of fiducials and co-registration was performed automatically by the leksellgammaplan software.contours:\\u00a0for each subject and each modality there is a text file named\\u00a0contours.json.\\u00a0these\\u00a0contour files in the t1 and t2 folder contain\\u00a0the contour points of the segmented structures in\\u00a0javascript object notation (json) format, mapped in the coordinate frames of the t1 image and the t2 image, respectively.\\u00a0there can be small differences between the contour points of the rtstruct and the contour points of the json files as explained in the following:in most cases, the tumour was segmented on the t1 image while the cochlea was typically segmented on the t2 image. this\\u00a0meant that some contour lines (typically for the tumour) were coplanar with the slices of the t1 image while others (typically\\u00a0for the cochlea) were coplanar with t2 slices. after co-registration, the (un-resampled) slices of the t1 and t2 image generally\\u00a0did not coincide; for example,\\u00a0due to different image position and, occasionally, slice thickness. therefore, the combined co-registered contour lines were neither jointly coplanar with the t1 nor with the t2 image slices.\\u00a0upon export of the segmentations in a given target\\u00a0space, the\\u00a0leksellgammaplan\\u00a0software interpolates between the original contour lines to create new slice-aligned contour lines\\u00a0in the target image space (t1 or t2). this results in the interpolated slice-aligned contour lines found in the rtstructs. in contrast, the contours in the json files were not interpolated after co-registration, and therefore describe the original\\u00a0(potentially off-target-space-slice) manual segmentation accurately.related softwareplease see the github respository link which contains a script to organize the downloaded data into a more convenient folder structure and a script to\\u00a0perform\\u00a0co-registration based on the .tfm files\\u00a0and to convert the downloaded dicom images and json contours into nifti format. moreover, the repository contains an algorithm for automatic segmentation of vs with deep learning, adapted to this data set. the applied neural network is based on the 2.5d unet described in \\\"an artificial intelligence framework for automatic segmentation and volumetry of vestibular schwannomas from contrast-enhanced t1-weighted and high-resolution t2-weighted mri\\\" (shapey, et al., 2021)\\u00a0and has been adapted to yield improved segmentation results. our implementation uses monai, a freely available, pytorch-based framework for deep learning in healthcare imaging (project monai). this new implementation was devised to provide a starting point for researchers interested in automatic segmentation using state-of-the art deep learning frameworks for medical image processing.\\u00a0 vestibular schwannoma (non-cancer) ear mr rtdose rtstruct rtplan other measurement radiomic feature image analyses software\\\/source code community\"},{\"id\":45393,\"type\":\"collection\",\"slug\":\"victre\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/collection\\\/victre\\\/\",\"title\":\"The VICTRE Trial: Open-Source, In-Silico Clinical Trial For Evaluating Digital Breast Tomosynthesis\",\"short_title\":\"VICTRE\",\"doi\":\"10.7937\\\/TCIA.2019.ho23nxaw\",\"summary\":\"Expensive and lengthy clinical trials delay regulatory evaluation of innovative medical technologies affecting patient access to high-quality medical products. Sophisticated simulation tools are increasingly being used in device development, but are rarely used in regulatory applications. We investigate a new paradigm for evaluating digital breast tomosynthesis (DBT) as a replacement for digital mammography (DM), using exclusively in-silico methods.A total of 2986 subjects, with breast sizes and radiographic densities representative of a screening population and compressed thicknesses from 3.5 to 6 cm, were simulated and imaged on in-silico versions of DM and DBT systems using fast Monte Carlo x-ray transport. Images were interpreted by a computational reader detecting the presence of lesions. The in-silico trial (VICTRE) was designed to replicate a comparative trial from a previous regulatory submission. The endpoint was the difference in area under the receiver-operating-characteristic curve between modalities (delta-AUC) for lesion detection. Using a fully-crossed design, VICTRE was sized for a standard error (SE) of 0.01 in delta-AUC, half the uncertainty seen in the comparative trial.\\u00a0A 1-hour summary presentation of the project and findings was given at the FDA Grand Rounds on 3\\\/14\\\/2019 and can be found\\u00a0here.A systematic exploration of the trial parameters including lesion types and sizes is also possible and greatly facilitated by the availability of open-source, free software tools available at\\u00a0https:\\\/\\\/github.com\\\/DIDSR\\\/VICTRE.\",\"image\":false,\"program\":\"FDA\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MG\"],\"supporting_data\":[\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":2994,\"date_updated\":\"2019-03-08\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=VICTRE\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/VICTRE-NBIA-manifest.tcia\",\"blob\":\"the victre trial: open-source, in-silico clinical trial for evaluating digital breast tomosynthesis victre expensive and lengthy clinical trials delay regulatory evaluation of innovative medical technologies affecting patient access to high-quality medical products. sophisticated simulation tools are increasingly being used in device development, but are rarely used in regulatory applications. we investigate a new paradigm for evaluating digital breast tomosynthesis (dbt) as a replacement for digital mammography (dm), using exclusively in-silico methods.a total of 2986 subjects, with breast sizes and radiographic densities representative of a screening population and compressed thicknesses from 3.5 to 6 cm, were simulated and imaged on in-silico versions of dm and dbt systems using fast monte carlo x-ray transport. images were interpreted by a computational reader detecting the presence of lesions. the in-silico trial (victre) was designed to replicate a comparative trial from a previous regulatory submission. the endpoint was the difference in area under the receiver-operating-characteristic curve between modalities (delta-auc) for lesion detection. using a fully-crossed design, victre was sized for a standard error (se) of 0.01 in delta-auc, half the uncertainty seen in the comparative trial.\\u00a0a 1-hour summary presentation of the project and findings was given at the fda grand rounds on 3\\\/14\\\/2019 and can be found\\u00a0here.a systematic exploration of the trial parameters including lesion types and sizes is also possible and greatly facilitated by the availability of open-source, free software tools available at\\u00a0https:\\\/\\\/github.com\\\/didsr\\\/victre. breast cancer breast mg software\\\/source code fda\"},{\"id\":46395,\"type\":\"analysis\",\"slug\":\"a091105-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/a091105-tumor-annotations\\\/\",\"title\":\"Annotations for Sorafenib Tosylate in Treating Patients With Desmoid Tumors or Aggressive Fibromatosis\",\"short_title\":\"A091105-Tumor-Annotations\",\"doi\":\"10.7937\\\/T8RN-J447\",\"summary\":\"This dataset contains image annotations derived from the NCI Clinical Trial \\\"Sorafenib Tosylate in Treating Patients With Desmoid Tumors or Aggressive Fibromatosis (A091105)\\u201d. \\u00a0This dataset was generated as part of an NCI project to augment TCIA datasets with annotations that will improve their value for cancer researchers and AI developers.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient all available time points were annotated. The following annotation rules were followed:\\r\\n\\r\\n \\tRECIST 1.1 was otherwise generally followed for MR and CT imaging. A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point. If the primary lesion was <1 cm it was still annotated.\\r\\n \\tThree-dimensional segmentations of lesions were created in the axial plane. If no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tMRIs were annotated using the T1-weighted axial post contrast sequence, fat saturated if available. Occasionally, if the tumor was significantly better delineated on a STIR or T2 fat-sat sequence, it wase annotated on that sequence instead of the post contrast sequence.\\r\\n \\tCTs were annotated using the axial post contrast series. If not available, the non contrast series was annotated.\\r\\n \\tLesions were labeled separately.\\r\\n \\tThe volume of each annotated lesion was calculated and reported in cubic centimeters [cc] in the Annotation Metadata CSV.\\r\\n \\tSeed points were automatically generated, but reviewed by a radiologist.\\r\\n \\tA \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nAt each time point:\\r\\n\\r\\n \\tA seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.\\r\\n \\tSNOMED-CT \\u201cAnatomic Region Sequence\\u201d and \\u201cSegmented Property Category Code Sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201cTracking ID\\u201d and \\u201cTracking UID\\u201d tags will be inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\tImaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cClinical Trial Time Point ID\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tContent Item in \\u201cAcquisition Context Sequence\\u201d was added containing \\\"Time Point Type\\\" using Concept Code Sequence (0040,A168) selected from:\\r\\n\\r\\n \\t(255235001, SCT, \\u201cPre-dose\\u201d)\\r\\n \\t(262502001, SCT, \\\"Post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing key details about the annotations is available in the\\u00a0Data Access\\u00a0section below.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface\\u00a0application and the\\u00a0REST API\\u00a0to access these data can be found in the\\u00a0Additional Resources\\u00a0section below.\",\"image\":false,\"program\":\"NCI Trials\",\"cancer_types\":[\"Non-Cancer\"],\"cancer_locations\":[\"Various\"],\"data_types\":[\"RTSTRUCT\",\"Measurement\",\"Classification\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":83,\"date_updated\":\"2023-11-13\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/A091105_Tumor-Annotations-manifest_11-06-2023.tcia\",\"blob\":\"annotations for sorafenib tosylate in treating patients with desmoid tumors or aggressive fibromatosis a091105-tumor-annotations this dataset contains image annotations derived from the nci clinical trial \\\"sorafenib tosylate in treating patients with desmoid tumors or aggressive fibromatosis (a091105)\\u201d. \\u00a0this dataset was generated as part of an nci project to augment tcia datasets with annotations that will improve their value for cancer researchers and ai developers.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient all available time points were annotated. the following annotation rules were followed:\\r\\n\\r\\n \\trecist 1.1 was otherwise generally followed for mr and ct imaging. a maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. the same 5 lesions were annotated at each time point. if the primary lesion was <1 cm it was still annotated.\\r\\n \\tthree-dimensional segmentations of lesions were created in the axial plane. if no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tmris were annotated using the t1-weighted axial post contrast sequence, fat saturated if available. occasionally, if the tumor was significantly better delineated on a stir or t2 fat-sat sequence, it wase annotated on that sequence instead of the post contrast sequence.\\r\\n \\tcts were annotated using the axial post contrast series. if not available, the non contrast series was annotated.\\r\\n \\tlesions were labeled separately.\\r\\n \\tthe volume of each annotated lesion was calculated and reported in cubic centimeters [cc] in the annotation metadata csv.\\r\\n \\tseed points were automatically generated, but reviewed by a radiologist.\\r\\n \\ta \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nat each time point:\\r\\n\\r\\n \\ta seed point (kernel) was created for each segmented structure. the seed points for each segmentation are provided in a separate dicom rtstruct file.\\r\\n \\tsnomed-ct \\u201canatomic region sequence\\u201d and \\u201csegmented property category code sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201ctracking id\\u201d and \\u201ctracking uid\\u201d tags will be inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\timaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cclinical trial time point id\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tcontent item in \\u201cacquisition context sequence\\u201d was added containing \\\"time point type\\\" using concept code sequence (0040,a168) selected from:\\r\\n\\r\\n \\t(255235001, sct, \\u201cpre-dose\\u201d)\\r\\n \\t(262502001, sct, \\\"post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing key details about the annotations is available in the\\u00a0data access\\u00a0section below.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface\\u00a0application and the\\u00a0rest api\\u00a0to access these data can be found in the\\u00a0additional resources\\u00a0section below. non-cancer various rtstruct measurement classification nci trials\"},{\"id\":46199,\"type\":\"analysis\",\"slug\":\"acns0332-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/acns0332-tumor-annotations\\\/\",\"title\":\"Annotations for Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed, Previously Untreated, High-Risk Medulloblastoma\\\/PNET\",\"short_title\":\"ACNS0332-Tumor-Annotations\",\"doi\":\"10.7937\\\/D8A8-6252\",\"summary\":\"This dataset contains image annotations derived from the NCI Clinical Trial \\\"Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed, Previously Untreated, High-Risk Medulloblastoma\\\/PNET (ACNS0332)\\\". \\u00a0This curated dataset provides a comprehensive picture of imaging in pediatric patients with newly diagnosed primitive neuroectodermal tumors throughout their treatment and until any potential relapse. This is the largest known dataset of patients with supratentorial primitive neuroectodermal tumors and pineoblastomas. This dataset was generated as part of an NCI project to augment TCIA datasets with annotations that will improve their value for cancer researchers and AI developers.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient the following time points were annotated:\\r\\n\\r\\n \\tPre-surgical study\\r\\n \\tPost-surgical study [if applicable]\\r\\n \\tFollow-up study at the completion of radiotherapy.\\r\\n \\tFollow-up study at the end of chemotherapy.\\r\\n \\tFollow-up study relapse [if applicable]\\r\\n\\r\\nAt each time point, 3D segmentations (DICOM SEG), seed points (DICOM RTSTRUCT) and negative finding assessments (DICOM RTSTRUCT) were created:\\r\\n\\r\\n \\tEnhancing tumor on an axial 3D T1 post contrast sequence\\r\\n\\r\\n \\tIf not available, a 3D post contrast sequence in another plane was used.\\r\\n \\tIf no 3D post contrast sequence was available, the tumor was annotated in all 3 planes utilizing 2D post contrast sequences.\\r\\n \\tOn post-contrast sequences, the entire tumor, including the cystic and non enhancing components was annotated.\\r\\n \\tAny resection cavity or post-op changes\\\/products was excluded.\\r\\n\\r\\n\\r\\n \\tEdema on an axial T2 FLAIR sequence\\r\\n\\r\\n \\tIf not available, an axial T2 or other T2 weighted sequence was used.\\r\\n \\tThe segmentation mask contains both the edematous tissue and the tumor.\\r\\n\\r\\n\\r\\n \\tThe portion of the tumor demonstrating restricted diffusion on an ADC sequence\\r\\n \\tUp to 5 metastatic lesions within the brain and and up to 5 metastatic lesions in the spine as demonstrated on whatever T1 post contrast sequence they are visualized on\\r\\n\\r\\n \\tWhen present, the 5 largest lesions were annotated.\\r\\n\\r\\n\\r\\n \\tA manually placed seed point (kernel) were created for each segmented structure\\r\\n\\r\\n \\tThe seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.\\r\\n \\tSpinal metastases, which are too small to apply a volumetric mask to, only have a seed point annotation.\\r\\n\\r\\n\\r\\n \\tIf no seed points or segmentations were generated a \\\"Negative Assessment Report\\\" RTSTRUCT file was created to record this fact.\\r\\n \\tTo ensure a high standard of accuracy and data quality, each annotation was reviewed by a secondary reader.\\r\\n\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing a variety of useful metadata about the annotations, including calculated tumor volumes, is available in the Data Access section below.\\r\\n \\tImportant information about how to interpret the DICOM annotation data can be found on the Detailed Description section below.\\u00a0 It includes information about specific tags which document where the tumor was found, whether it was enhancing\\\/non-enhancing, which study time point the annotation relates to, details for lesion tracking across time points, etc.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface application and our REST API (with authentication) to access these data can be found in the Additional Resources section below.\\r\\n \\tInstructions for visualizing these data in 3D Slicer can be found in the Additional Resources section below.\",\"image\":false,\"program\":\"NCTN\",\"cancer_types\":[\"Medulloblastoma\"],\"cancer_locations\":[\"Head\"],\"data_types\":[\"SEG\",\"RTSTRUCT\",\"Segmentation\",\"Measurement\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":85,\"date_updated\":\"2023-08-03\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=ACNS0332&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ACNS0332-Tumor-Annotations-manifest-03-21-2023.tcia\",\"blob\":\"annotations for chemotherapy and radiation therapy in treating young patients with newly diagnosed, previously untreated, high-risk medulloblastoma\\\/pnet acns0332-tumor-annotations this dataset contains image annotations derived from the nci clinical trial \\\"chemotherapy and radiation therapy in treating young patients with newly diagnosed, previously untreated, high-risk medulloblastoma\\\/pnet (acns0332)\\\". \\u00a0this curated dataset provides a comprehensive picture of imaging in pediatric patients with newly diagnosed primitive neuroectodermal tumors throughout their treatment and until any potential relapse. this is the largest known dataset of patients with supratentorial primitive neuroectodermal tumors and pineoblastomas. this dataset was generated as part of an nci project to augment tcia datasets with annotations that will improve their value for cancer researchers and ai developers.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient the following time points were annotated:\\r\\n\\r\\n \\tpre-surgical study\\r\\n \\tpost-surgical study [if applicable]\\r\\n \\tfollow-up study at the completion of radiotherapy.\\r\\n \\tfollow-up study at the end of chemotherapy.\\r\\n \\tfollow-up study relapse [if applicable]\\r\\n\\r\\nat each time point, 3d segmentations (dicom seg), seed points (dicom rtstruct) and negative finding assessments (dicom rtstruct) were created:\\r\\n\\r\\n \\tenhancing tumor on an axial 3d t1 post contrast sequence\\r\\n\\r\\n \\tif not available, a 3d post contrast sequence in another plane was used.\\r\\n \\tif no 3d post contrast sequence was available, the tumor was annotated in all 3 planes utilizing 2d post contrast sequences.\\r\\n \\ton post-contrast sequences, the entire tumor, including the cystic and non enhancing components was annotated.\\r\\n \\tany resection cavity or post-op changes\\\/products was excluded.\\r\\n\\r\\n\\r\\n \\tedema on an axial t2 flair sequence\\r\\n\\r\\n \\tif not available, an axial t2 or other t2 weighted sequence was used.\\r\\n \\tthe segmentation mask contains both the edematous tissue and the tumor.\\r\\n\\r\\n\\r\\n \\tthe portion of the tumor demonstrating restricted diffusion on an adc sequence\\r\\n \\tup to 5 metastatic lesions within the brain and and up to 5 metastatic lesions in the spine as demonstrated on whatever t1 post contrast sequence they are visualized on\\r\\n\\r\\n \\twhen present, the 5 largest lesions were annotated.\\r\\n\\r\\n\\r\\n \\ta manually placed seed point (kernel) were created for each segmented structure\\r\\n\\r\\n \\tthe seed points for each segmentation are provided in a separate dicom rtstruct file.\\r\\n \\tspinal metastases, which are too small to apply a volumetric mask to, only have a seed point annotation.\\r\\n\\r\\n\\r\\n \\tif no seed points or segmentations were generated a \\\"negative assessment report\\\" rtstruct file was created to record this fact.\\r\\n \\tto ensure a high standard of accuracy and data quality, each annotation was reviewed by a secondary reader.\\r\\n\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing a variety of useful metadata about the annotations, including calculated tumor volumes, is available in the data access section below.\\r\\n \\timportant information about how to interpret the dicom annotation data can be found on the detailed description section below.\\u00a0 it includes information about specific tags which document where the tumor was found, whether it was enhancing\\\/non-enhancing, which study time point the annotation relates to, details for lesion tracking across time points, etc.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface application and our rest api (with authentication) to access these data can be found in the additional resources section below.\\r\\n \\tinstructions for visualizing these data in 3d slicer can be found in the additional resources section below. medulloblastoma head seg rtstruct segmentation measurement nctn\"},{\"id\":46373,\"type\":\"analysis\",\"slug\":\"acrin-6685-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/acrin-6685-tumor-annotations\\\/\",\"title\":\"Annotations for ACRIN-HNSCC-FDG-PET-CT Collection\",\"short_title\":\"ACRIN 6685-Tumor-Annotations\",\"doi\":\"10.7937\\\/JVGC-AQ36\",\"summary\":\"This dataset contains image annotations derived from the NCI Clinical Trial \\\"ACRIN-HNSCC-FDG-PET-CT (ACRIN 6685)\\u201d. \\u00a0This dataset was generated as part of an NCI project to augment TCIA datasets with annotations that will improve their value for cancer researchers and AI developers.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient all available time points were annotated. The following annotation rules were followed:\\r\\n\\r\\n \\tPERCIST criteria was followed for PET imaging. Specifically, the lesions estimated to have the most elevated SUVmax were annotated.\\r\\n \\tRECIST 1.1 was otherwise generally followed for MR and CT imaging. A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point. Lymph nodes were annotated if >1 cm in short axis. Other lesions were annotated if >1 cm. If the primary lesion is < 1 cm, it was still annotated.\\r\\n \\tThree-dimensional segmentations of lesions were created in the axial plane. If no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tMRIs were annotated using the T1-weighted axial post contrast sequence, fat saturated if available.\\r\\n \\tCTs were annotated using the axial post contrast series. If not available, the non contrast series were annotated.\\r\\n \\tPET\\\/CTs were annotated on the CT and attenuation corrected PET images.\\r\\n \\tIf the post contrast CT was performed the same day as the PET\\\/CT, the non contrast CT portion of the PET\\\/CT was not annotated.\\r\\n \\tLesions were labeled separately.\\r\\n \\tThe volume of each annotated lesion was calculated and reported in cubic centimeters [cc] in the Annotation Metadata CSV.\\r\\n \\tSeed points were automatically generated, but reviewed by a radiologist.\\r\\n \\tA \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nAt each time point:\\r\\n\\r\\n \\tA seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.\\r\\n \\tSNOMED-CT \\u201cAnatomic Region Sequence\\u201d and \\u201cSegmented Property Category Code Sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201cTracking ID\\u201d and \\u201cTracking UID\\u201d tags were inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\tImaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cClinical Trial Time Point ID\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tContent Item in \\u201cAcquisition Context Sequence\\u201d was added containing \\\"Time Point Type\\\" using Concept Code Sequence (0040,A168) selected from:\\r\\n\\r\\n \\t(255235001, SCT, \\u201cPre-dose\\u201d) (in this trial, both the CT\\\/MRI and PET\\\/CT, while being different timepoints, are pre-treatment)\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing key details about the annotations is available in the\\u00a0Data Access\\u00a0section below.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface\\u00a0application and the\\u00a0REST API\\u00a0to access these data can be found in the\\u00a0Additional Resources\\u00a0section below.\",\"image\":false,\"program\":\"NCI Trials\",\"cancer_types\":[\"Head and Neck Squamous Cell Carcinoma\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"RTSTRUCT\",\"Classification\",\"Measurement\"],\"supporting_data\":[\"Tumor segmentations\",\"Seed points\"],\"species\":[\"Human\"],\"subjects\":258,\"date_updated\":\"2023-11-13\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?ImageModalityCriteria=RTSTRUCT&CollectionCriteria=ACRIN-HNSCC-FDG-PET-CT&DateRange=11\\\/01\\\/2023-11\\\/30\\\/2023&ThirdPartyCriteria=YES \",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ACRIN6685_Tumor-Annotations-manifest_11-07-2023.tcia\",\"blob\":\"annotations for acrin-hnscc-fdg-pet-ct collection acrin 6685-tumor-annotations this dataset contains image annotations derived from the nci clinical trial \\\"acrin-hnscc-fdg-pet-ct (acrin 6685)\\u201d. \\u00a0this dataset was generated as part of an nci project to augment tcia datasets with annotations that will improve their value for cancer researchers and ai developers.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient all available time points were annotated. the following annotation rules were followed:\\r\\n\\r\\n \\tpercist criteria was followed for pet imaging. specifically, the lesions estimated to have the most elevated suvmax were annotated.\\r\\n \\trecist 1.1 was otherwise generally followed for mr and ct imaging. a maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. the same 5 lesions were annotated at each time point. lymph nodes were annotated if >1 cm in short axis. other lesions were annotated if >1 cm. if the primary lesion is < 1 cm, it was still annotated.\\r\\n \\tthree-dimensional segmentations of lesions were created in the axial plane. if no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tmris were annotated using the t1-weighted axial post contrast sequence, fat saturated if available.\\r\\n \\tcts were annotated using the axial post contrast series. if not available, the non contrast series were annotated.\\r\\n \\tpet\\\/cts were annotated on the ct and attenuation corrected pet images.\\r\\n \\tif the post contrast ct was performed the same day as the pet\\\/ct, the non contrast ct portion of the pet\\\/ct was not annotated.\\r\\n \\tlesions were labeled separately.\\r\\n \\tthe volume of each annotated lesion was calculated and reported in cubic centimeters [cc] in the annotation metadata csv.\\r\\n \\tseed points were automatically generated, but reviewed by a radiologist.\\r\\n \\ta \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nat each time point:\\r\\n\\r\\n \\ta seed point (kernel) was created for each segmented structure. the seed points for each segmentation are provided in a separate dicom rtstruct file.\\r\\n \\tsnomed-ct \\u201canatomic region sequence\\u201d and \\u201csegmented property category code sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201ctracking id\\u201d and \\u201ctracking uid\\u201d tags were inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\timaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cclinical trial time point id\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tcontent item in \\u201cacquisition context sequence\\u201d was added containing \\\"time point type\\\" using concept code sequence (0040,a168) selected from:\\r\\n\\r\\n \\t(255235001, sct, \\u201cpre-dose\\u201d) (in this trial, both the ct\\\/mri and pet\\\/ct, while being different timepoints, are pre-treatment)\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing key details about the annotations is available in the\\u00a0data access\\u00a0section below.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface\\u00a0application and the\\u00a0rest api\\u00a0to access these data can be found in the\\u00a0additional resources\\u00a0section below. head and neck squamous cell carcinoma head-neck rtstruct classification measurement tumor segmentations seed points nci trials\"},{\"id\":46397,\"type\":\"analysis\",\"slug\":\"ahep0731-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/ahep0731-tumor-annotations\\\/\",\"title\":\"Annotations for Risk-Based Therapy in Treating Younger Patients With Newly Diagnosed Liver Cancer\",\"short_title\":\"AHEP0731-Tumor-Annotations\",\"doi\":\"10.7937\\\/BDBN-NQ81\",\"summary\":\"This dataset contains image annotations derived from the NCI Clinical Trial \\\"Risk-Based Therapy in Treating Younger Patients With Newly Diagnosed Liver Cancer\\u201d. \\u00a0This dataset was generated as part of an NCI project to augment TCIA datasets with annotations that will improve their value for cancer researchers and AI developers.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient all available time points were annotated. The following annotation rules were followed:\\r\\n\\r\\n \\tRECIST 1.1 was generally followed for MR and CT imaging. A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point. If the primary lesion was >1 cm it was still annotated.\\r\\n \\tThree-dimensional segmentations of lesions were created in the axial plane. If no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tMRIs were annotated using the T1-weighted post contrast sequence, fat saturated if available. Occasionally, if the tumor was significantly better delineated on a T2-weighted sequence, it was annotated on that sequence instead of the post contrast sequence.\\r\\n \\tCTs were annotated using an axial post contrast series, preferably in the arterial phase. If not available, the non contrast series was annotated.\\r\\n \\tLesions were labeled separately.\\r\\n \\tThe volume of each annotated lesion was calculated and reported in cubic centimeters [cc] in the Annotation Metadata CSV.\\r\\n \\tSeed points were automatically generated but reviewed by a radiologist.\\r\\n \\tA \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nAt each time point:\\r\\n\\r\\n \\tA seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.\\r\\n \\tSNOMED-CT \\u201cAnatomic Region Sequence\\u201d and \\u201cSegmented Property Category Code Sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201cTracking ID\\u201d and \\u201cTracking UID\\u201d tags were inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\tImaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cClinical Trial Time Point ID\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tContent Item in \\u201cAcquisition Context Sequence\\u201d was added containing \\\"Time Point Type\\\" using Concept Code Sequence (0040,A168) selected from:\\r\\n\\r\\n \\t(255235001, SCT, \\u201cPre-dose\\u201d)\\r\\n \\t(262502001, SCT, \\\"Post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing key details about the annotations is available in the\\u00a0Data Access\\u00a0section below.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface\\u00a0application and the\\u00a0REST API\\u00a0to access these data can be found in the\\u00a0Additional Resources\\u00a0section below.\",\"image\":false,\"program\":\"NCI Trials\",\"cancer_types\":[\"Hepatoblastoma\"],\"cancer_locations\":[\"Liver\"],\"data_types\":[\"RTSTRUCT\",\"Classification\",\"Measurement\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":80,\"date_updated\":\"2024-01-05\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?ImageModalityCriteria=RTSTRUCT&MinNumberOfStudiesCriteria=1&CollectionCriteria=AHEP0731&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/AHEP0731_Tumor-Annotations-manifest_1-3-2024.tcia\",\"blob\":\"annotations for risk-based therapy in treating younger patients with newly diagnosed liver cancer ahep0731-tumor-annotations this dataset contains image annotations derived from the nci clinical trial \\\"risk-based therapy in treating younger patients with newly diagnosed liver cancer\\u201d. \\u00a0this dataset was generated as part of an nci project to augment tcia datasets with annotations that will improve their value for cancer researchers and ai developers.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient all available time points were annotated. the following annotation rules were followed:\\r\\n\\r\\n \\trecist 1.1 was generally followed for mr and ct imaging. a maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. the same 5 lesions were annotated at each time point. if the primary lesion was >1 cm it was still annotated.\\r\\n \\tthree-dimensional segmentations of lesions were created in the axial plane. if no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tmris were annotated using the t1-weighted post contrast sequence, fat saturated if available. occasionally, if the tumor was significantly better delineated on a t2-weighted sequence, it was annotated on that sequence instead of the post contrast sequence.\\r\\n \\tcts were annotated using an axial post contrast series, preferably in the arterial phase. if not available, the non contrast series was annotated.\\r\\n \\tlesions were labeled separately.\\r\\n \\tthe volume of each annotated lesion was calculated and reported in cubic centimeters [cc] in the annotation metadata csv.\\r\\n \\tseed points were automatically generated but reviewed by a radiologist.\\r\\n \\ta \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nat each time point:\\r\\n\\r\\n \\ta seed point (kernel) was created for each segmented structure. the seed points for each segmentation are provided in a separate dicom rtstruct file.\\r\\n \\tsnomed-ct \\u201canatomic region sequence\\u201d and \\u201csegmented property category code sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201ctracking id\\u201d and \\u201ctracking uid\\u201d tags were inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\timaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cclinical trial time point id\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tcontent item in \\u201cacquisition context sequence\\u201d was added containing \\\"time point type\\\" using concept code sequence (0040,a168) selected from:\\r\\n\\r\\n \\t(255235001, sct, \\u201cpre-dose\\u201d)\\r\\n \\t(262502001, sct, \\\"post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing key details about the annotations is available in the\\u00a0data access\\u00a0section below.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface\\u00a0application and the\\u00a0rest api\\u00a0to access these data can be found in the\\u00a0additional resources\\u00a0section below. hepatoblastoma liver rtstruct classification measurement nci trials\"},{\"id\":46273,\"type\":\"analysis\",\"slug\":\"ahod0831-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/ahod0831-tumor-annotations\\\/\",\"title\":\"Annotations for Combination Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed Hodgkin Lymphoma\",\"short_title\":\"AHOD0831-Tumor-Annotations\",\"doi\":\"10.7937\\\/4QAD-4280\",\"summary\":\"This dataset contains image annotations derived from the NCI Clinical Trial \\\"Combination Chemotherapy and Radiation Therapy in Treating Young Patients With Newly Diagnosed Hodgkin Lymphoma (AHOD0831)\\u201d. \\u00a0This dataset was generated as part of an NCI project to augment TCIA datasets with annotations that will improve their value for cancer researchers and AI developers.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient all available time points were annotated:\\r\\n\\r\\nIn a typical patient the following annotation rules were followed:\\r\\n\\r\\n \\tPERCIST criteria was followed. Specifically, the lesions estimated to have the most elevated SUVmax were annotated.\\r\\n \\tLesions were annotated in the axial plane. If no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tLesions were annotated on the attenuation corrected PET series as well as the diagnostic contrast enhanced CT. If no diagnostic contrast enhanced CT is available for that timepoint, then the non contrast CT portion of the PET\\\/CT was annotated.\\r\\n \\tA maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. For the purposes of this project, the lymph nodes constitute 1 organ, while other lymphatic structures such as the spleen, salivary glands, and Waldeyer\\u2019s ring structures constitute separate organs.\\u00a0 The same 5 lesions were annotated at each time point.\\u00a0 RECIST 1.1 principles will be followed. Specifically, lymph nodes were annotated if > 1.5 cm in short axis. Other lesions were annotated if > 1 cm.\\r\\n \\tLesions were labeled separately.\\r\\n \\tSeed points were automatically generated but reviewed by a radiologist.\\r\\n \\tTo ensure a high standard of accuracy and data quality, each annotation was reviewed by a secondary reader.\\r\\n\\r\\nAt each time point:\\r\\n\\r\\n \\tA seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSS file.\\r\\n \\tSNOMED-CT \\u201cAnatomic Region Sequence\\u201d and \\u201cSegmented Property Category Code Sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201cTracking ID\\u201d and \\u201cTracking UID\\u201d tags were inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\tImaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cClinical Trial Time Point ID\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d\\r\\n \\tContent Item in \\u201cAcquisition Context Sequence\\u201d was added containing \\\"Time Point Type\\\" using Concept Code Sequence (0040,A168) selected from:\\r\\n\\r\\n \\t(255235001, SCT, \\u201cPre-dose\\u201d)\\r\\n \\t(262502001, SCT, \\\"Post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing key details about the annotations is available in the\\u00a0Data Access\\u00a0section below.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface\\u00a0application and the\\u00a0REST API (with authentication)\\u00a0to access these data can be found in the\\u00a0Additional Resources\\u00a0section below.\",\"image\":false,\"program\":\"NCTN\",\"cancer_types\":[\"Hodgkin Lymphoma\"],\"cancer_locations\":[\"Various\"],\"data_types\":[\"RTSTRUCT\",\"Classification\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":165,\"date_updated\":\"2023-02-10\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?ImageModalityCriteria=RTSTRUCT&MinNumberOfStudiesCriteria=1&CollectionCriteria=AHOD0831&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/AHOD0831_Tumor-Annotations-manifest_2-10-2023.tcia\",\"blob\":\"annotations for combination chemotherapy and radiation therapy in treating young patients with newly diagnosed hodgkin lymphoma ahod0831-tumor-annotations this dataset contains image annotations derived from the nci clinical trial \\\"combination chemotherapy and radiation therapy in treating young patients with newly diagnosed hodgkin lymphoma (ahod0831)\\u201d. \\u00a0this dataset was generated as part of an nci project to augment tcia datasets with annotations that will improve their value for cancer researchers and ai developers.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient all available time points were annotated:\\r\\n\\r\\nin a typical patient the following annotation rules were followed:\\r\\n\\r\\n \\tpercist criteria was followed. specifically, the lesions estimated to have the most elevated suvmax were annotated.\\r\\n \\tlesions were annotated in the axial plane. if no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tlesions were annotated on the attenuation corrected pet series as well as the diagnostic contrast enhanced ct. if no diagnostic contrast enhanced ct is available for that timepoint, then the non contrast ct portion of the pet\\\/ct was annotated.\\r\\n \\ta maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. for the purposes of this project, the lymph nodes constitute 1 organ, while other lymphatic structures such as the spleen, salivary glands, and waldeyer\\u2019s ring structures constitute separate organs.\\u00a0 the same 5 lesions were annotated at each time point.\\u00a0 recist 1.1 principles will be followed. specifically, lymph nodes were annotated if > 1.5 cm in short axis. other lesions were annotated if > 1 cm.\\r\\n \\tlesions were labeled separately.\\r\\n \\tseed points were automatically generated but reviewed by a radiologist.\\r\\n \\tto ensure a high standard of accuracy and data quality, each annotation was reviewed by a secondary reader.\\r\\n\\r\\nat each time point:\\r\\n\\r\\n \\ta seed point (kernel) was created for each segmented structure. the seed points for each segmentation are provided in a separate dicom rtss file.\\r\\n \\tsnomed-ct \\u201canatomic region sequence\\u201d and \\u201csegmented property category code sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201ctracking id\\u201d and \\u201ctracking uid\\u201d tags were inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\timaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cclinical trial time point id\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d\\r\\n \\tcontent item in \\u201cacquisition context sequence\\u201d was added containing \\\"time point type\\\" using concept code sequence (0040,a168) selected from:\\r\\n\\r\\n \\t(255235001, sct, \\u201cpre-dose\\u201d)\\r\\n \\t(262502001, sct, \\\"post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing key details about the annotations is available in the\\u00a0data access\\u00a0section below.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface\\u00a0application and the\\u00a0rest api (with authentication)\\u00a0to access these data can be found in the\\u00a0additional resources\\u00a0section below. hodgkin lymphoma various rtstruct classification nctn\"},{\"id\":46357,\"type\":\"analysis\",\"slug\":\"arar0331-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/arar0331-tumor-annotations\\\/\",\"title\":\"Annotations for Radiation Therapy, Amifostine, and Chemotherapy in Treating Young Patients With Newly Diagnosed Nasopharyngeal Cancer Collection\",\"short_title\":\"ARAR0331-Tumor-Annotations\",\"doi\":\"10.25737\\\/H65S-8F58\",\"summary\":\"This dataset contains image annotations derived from the NCI Clinical Trial \\\"Radiation Therapy, Amifostine, and Chemotherapy in Treating Young Patients With Newly Diagnosed Nasopharyngeal Cancer (ARAR0331)\\u201d. \\u00a0This dataset was generated as part of an NCI project to augment TCIA datasets with annotations that will improve their value for cancer researchers and AI developers.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient all available time points were annotated. The following annotation rules were followed:\\r\\n\\r\\n \\tPERCIST criteria was followed for PET imaging. Specifically, the lesions estimated to have the most elevated SUVmax were annotated.\\r\\n \\tRECIST 1.1 was otherwise generally followed for MR and CT imaging. A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point. Lymph nodes were annotated if >1.5 cm in short axis. Other lesions were annotated if >1 cm.\\r\\n \\tThree-dimensional segmentations of lesions were created in the axial plane. If no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tMRIs were annotated using the T1-weighted axial post contrast sequence.\\r\\n \\tSome lesions may cross multiple exams (ie. an MRI of the head and an MRI of the neck). The images portions on each exam were then annotated. If, however, the complete lesion was visualized on either a neck or head exam, then the other exam was not annotated to avoid redundancy.\\r\\n \\tLesions were labeled separately.\\r\\n \\tThe volume of each annotated lesion was calculated and reported in cubic centimeters [cc] in the Annotation Metadata CSV.\\r\\n \\tA \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nAt each time point:\\r\\n\\r\\n \\tA seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.\\r\\n \\tSNOMED-CT \\u201cAnatomic Region Sequence\\u201d and \\u201cSegmented Property Category Code Sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201cTracking ID\\u201d and \\u201cTracking UID\\u201d tags were inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\tImaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cClinical Trial Time Point ID\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tContent Item in \\u201cAcquisition Context Sequence\\u201d was added containing \\\"Time Point Type\\\" using Concept Code Sequence (0040,A168) selected from:\\r\\n\\r\\n \\t(255235001, SCT, \\u201cPre-dose\\u201d)\\r\\n \\t(262502001, SCT, \\\"Post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing key details about the annotations is available in the\\u00a0Data Access\\u00a0section below.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface\\u00a0application and the\\u00a0REST API\\u00a0to access these data can be found in the\\u00a0Additional Resources\\u00a0section below.\",\"image\":false,\"program\":\"NCI Trials\",\"cancer_types\":[\"Nasopharyngeal\"],\"cancer_locations\":[\"Head\"],\"data_types\":[\"RTSTRUCT\",\"Classification\",\"Measurement\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":108,\"date_updated\":\"2023-11-14\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?ImageModalityCriteria=RTSTRUCT&DateRange=11\\\/01\\\/2023-11\\\/30\\\/2023&CollectionCriteria=ARAR0331&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ARAR0331_Tumor-Annotations-manifest_11-13-2023.tcia\",\"blob\":\"annotations for radiation therapy, amifostine, and chemotherapy in treating young patients with newly diagnosed nasopharyngeal cancer collection arar0331-tumor-annotations this dataset contains image annotations derived from the nci clinical trial \\\"radiation therapy, amifostine, and chemotherapy in treating young patients with newly diagnosed nasopharyngeal cancer (arar0331)\\u201d. \\u00a0this dataset was generated as part of an nci project to augment tcia datasets with annotations that will improve their value for cancer researchers and ai developers.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient all available time points were annotated. the following annotation rules were followed:\\r\\n\\r\\n \\tpercist criteria was followed for pet imaging. specifically, the lesions estimated to have the most elevated suvmax were annotated.\\r\\n \\trecist 1.1 was otherwise generally followed for mr and ct imaging. a maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. the same 5 lesions were annotated at each time point. lymph nodes were annotated if >1.5 cm in short axis. other lesions were annotated if >1 cm.\\r\\n \\tthree-dimensional segmentations of lesions were created in the axial plane. if no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tmris were annotated using the t1-weighted axial post contrast sequence.\\r\\n \\tsome lesions may cross multiple exams (ie. an mri of the head and an mri of the neck). the images portions on each exam were then annotated. if, however, the complete lesion was visualized on either a neck or head exam, then the other exam was not annotated to avoid redundancy.\\r\\n \\tlesions were labeled separately.\\r\\n \\tthe volume of each annotated lesion was calculated and reported in cubic centimeters [cc] in the annotation metadata csv.\\r\\n \\ta \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nat each time point:\\r\\n\\r\\n \\ta seed point (kernel) was created for each segmented structure. the seed points for each segmentation are provided in a separate dicom rtstruct file.\\r\\n \\tsnomed-ct \\u201canatomic region sequence\\u201d and \\u201csegmented property category code sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201ctracking id\\u201d and \\u201ctracking uid\\u201d tags were inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\timaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cclinical trial time point id\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tcontent item in \\u201cacquisition context sequence\\u201d was added containing \\\"time point type\\\" using concept code sequence (0040,a168) selected from:\\r\\n\\r\\n \\t(255235001, sct, \\u201cpre-dose\\u201d)\\r\\n \\t(262502001, sct, \\\"post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing key details about the annotations is available in the\\u00a0data access\\u00a0section below.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface\\u00a0application and the\\u00a0rest api\\u00a0to access these data can be found in the\\u00a0additional resources\\u00a0section below. nasopharyngeal head rtstruct classification measurement nci trials\"},{\"id\":46261,\"type\":\"analysis\",\"slug\":\"aren0532-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/aren0532-tumor-annotations\\\/\",\"title\":\"Annotations for Vincristine, Dactinomycin, and Doxorubicin With or Without Radiation Therapy or Observation Only in Treating Younger Patients Who Are Undergoing Surgery for Newly Diagnosed Stage I, II, or III Wilms' Tumor\",\"short_title\":\"AREN0532-Tumor-Annotations\",\"doi\":\"10.7937\\\/KJA4-1Z76\",\"summary\":\"This dataset contains image annotations derived from the NCI Clinical Trial \\\"Vincristine, Dactinomycin, and Doxorubicin With or Without Radiation Therapy or Observation Only in Treating Younger Patients Who Are Undergoing Surgery for Newly Diagnosed Stage I, Stage II, or Stage III Wilms' Tumor (AREN0532)\\u201d.\\u00a0 This dataset was generated as part of an NCI project to augment TCIA datasets with annotations that will improve their value for cancer researchers and AI developers.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient the following annotation rules were followed:\\r\\n\\r\\n \\tThe primary renal tumor(s) were annotated on post-contrast axial series. Normal renal parenchyma were excluded.\\r\\n \\tA maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point.\\u00a0 RECIST 1.1 principles were generally be followed for lesion annotation, however, if <5 lesions measuring >1 cm were present, then smaller lesions were annotated, again up to 2 lesions per organ or 5 lesions per patient scan. Bone lesions were included if other lesions were not present.\\r\\n \\tLesions were labeled separately.\\r\\n \\tSeed points were automatically generated but were reviewed by a radiologist.\\r\\n \\tTo ensure a high standard of accuracy and data quality, each annotation was reviewed by a secondary reader.\\r\\n\\r\\nAt each time point:\\r\\n\\r\\n \\tA seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSS file.\\r\\n \\tSNOMED-CT \\u201cAnatomic Region Sequence\\u201d and \\u201cSegmented Property Category Code Sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\tImaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cClinical Trial Time Point ID\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d\\r\\n \\tContent Item in \\u201cAcquisition Context Sequence\\u201d was added containing \\\"Time Point Type\\\" using Concept Code Sequence (0040,A168) selected from:\\r\\n\\r\\n \\t(255235001, SCT, \\u201cPre-dose\\u201d)\\r\\n \\t(262502001, SCT, \\\"Post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nWe believe that these are the most clinically useful annotations for radiologists as well as for future researchers. The selected sequences inform whether there is residual or recurrent tumor and assess response to therapy.\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing key details about the annotations is available in the\\u00a0Data Access\\u00a0section below.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface\\u00a0application and the\\u00a0REST API (with authentication)\\u00a0to access these data can be found in the\\u00a0Additional Resources\\u00a0section below.\",\"image\":false,\"program\":\"NCTN\",\"cancer_types\":[\"Wilms Tumor\"],\"cancer_locations\":[\"Kidney\"],\"data_types\":[\"RTSTRUCT\",\"Classification\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":543,\"date_updated\":\"2023-08-09\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?ImageModalityCriteria=RTSTRUCT&MinNumberOfStudiesCriteria=1&CollectionCriteria=AREN0532&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/AREN0532_Tumor-Annotations-manifest_2-8-2023.tcia\",\"blob\":\"annotations for vincristine, dactinomycin, and doxorubicin with or without radiation therapy or observation only in treating younger patients who are undergoing surgery for newly diagnosed stage i, ii, or iii wilms' tumor aren0532-tumor-annotations this dataset contains image annotations derived from the nci clinical trial \\\"vincristine, dactinomycin, and doxorubicin with or without radiation therapy or observation only in treating younger patients who are undergoing surgery for newly diagnosed stage i, stage ii, or stage iii wilms' tumor (aren0532)\\u201d.\\u00a0 this dataset was generated as part of an nci project to augment tcia datasets with annotations that will improve their value for cancer researchers and ai developers.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient the following annotation rules were followed:\\r\\n\\r\\n \\tthe primary renal tumor(s) were annotated on post-contrast axial series. normal renal parenchyma were excluded.\\r\\n \\ta maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. the same 5 lesions were annotated at each time point.\\u00a0 recist 1.1 principles were generally be followed for lesion annotation, however, if <5 lesions measuring >1 cm were present, then smaller lesions were annotated, again up to 2 lesions per organ or 5 lesions per patient scan. bone lesions were included if other lesions were not present.\\r\\n \\tlesions were labeled separately.\\r\\n \\tseed points were automatically generated but were reviewed by a radiologist.\\r\\n \\tto ensure a high standard of accuracy and data quality, each annotation was reviewed by a secondary reader.\\r\\n\\r\\nat each time point:\\r\\n\\r\\n \\ta seed point (kernel) was created for each segmented structure. the seed points for each segmentation are provided in a separate dicom rtss file.\\r\\n \\tsnomed-ct \\u201canatomic region sequence\\u201d and \\u201csegmented property category code sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\timaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cclinical trial time point id\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d\\r\\n \\tcontent item in \\u201cacquisition context sequence\\u201d was added containing \\\"time point type\\\" using concept code sequence (0040,a168) selected from:\\r\\n\\r\\n \\t(255235001, sct, \\u201cpre-dose\\u201d)\\r\\n \\t(262502001, sct, \\\"post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nwe believe that these are the most clinically useful annotations for radiologists as well as for future researchers. the selected sequences inform whether there is residual or recurrent tumor and assess response to therapy.\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing key details about the annotations is available in the\\u00a0data access\\u00a0section below.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface\\u00a0application and the\\u00a0rest api (with authentication)\\u00a0to access these data can be found in the\\u00a0additional resources\\u00a0section below. wilms tumor kidney rtstruct classification nctn\"},{\"id\":46237,\"type\":\"analysis\",\"slug\":\"aren0533-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/aren0533-tumor-annotations\\\/\",\"title\":\"Annotations for Combination Chemotherapy With or Without Radiation Therapy in Treating Young Patients With Newly Diagnosed Stage III or Stage IV Wilms Tumor\",\"short_title\":\"AREN0533-Tumor-Annotations\",\"doi\":\"10.7937\\\/WFCC-DA41\",\"summary\":\"This dataset contains image annotations derived from the NCI Clinical Trial \\\"Combination Chemotherapy With or Without Radiation Therapy in Treating Young Patients With Newly Diagnosed Stage III or Stage IV Wilms Tumor (AREN0533)\\u201d. \\u00a0This dataset was generated as part of an NCI project to augment TCIA datasets with annotations that will improve their value for cancer researchers and AI developers.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient the following time points were annotated:\\r\\n\\r\\n \\tPre-treatment CT chest and abdomen\\r\\n \\tCT chest and\\\/or CT abdomen at 6 weeks\\r\\n \\tPossible CT abdomen at 12 weeks.\\r\\n\\r\\nIn a typical patient the following annotation rules were followed:\\r\\n\\r\\n \\tThe primary renal tumor(s) were annotated on post-contrast axial series. Normal renal parenchyma were excluded.\\r\\n \\tA maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point.\\r\\n \\tRECIST 1.1 principles were generally followed for lesion annotation, however if <5 lesions measuring >1 cm were present, then smaller lesions were annotated, again up to 2 lesions per organ or 5 lesions per patient scan.\\r\\n \\tBone lesions were included if other lesions were not present.\\r\\n \\tTo ensure a high standard of accuracy and data quality, each annotation was reviewed by a secondary reader.\\r\\n\\r\\nAt each timepoint:\\r\\n\\r\\n \\tVolumetric segmentations, seed points and negative finding assessments (DICOM RTSTRUCT) were created.\\r\\n \\tSNOMED-CT \\u201cAnatomic Region Sequence\\u201d and \\u201cSegmented Property Category Code Sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\tImaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cClinical Trial Time Point ID\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tThe Content Item in \\u201cAcquisition Context Sequence\\u201d was added containing \\\"Time Point Type\\\" using Concept Code Sequence (0040,A168) selected from:\\r\\n\\r\\n \\t(255235001, SCT, \\u201cPre-dose\\u201d)\\r\\n \\t(262502001, SCT, \\\"Post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing key details about the annotations is available in the Data Access\\u00a0section below.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface application and the REST API (with authentication)\\u00a0to access these data can be found in the\\u00a0Additional Resources\\u00a0section below.\",\"image\":false,\"program\":\"NCTN\",\"cancer_types\":[\"Wilms Tumor\"],\"cancer_locations\":[\"Kidney\"],\"data_types\":[\"RTSTRUCT\",\"Classification\",\"Measurement\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":294,\"date_updated\":\"2023-01-31\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?ImageModalityCriteria=RTSTRUCT&DateRange=01\\\/10\\\/2023-02\\\/01\\\/2023&CollectionCriteria=AREN0533&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/AREN0533-Tumor-Annotations-manifest-01-31-2023.tcia\",\"blob\":\"annotations for combination chemotherapy with or without radiation therapy in treating young patients with newly diagnosed stage iii or stage iv wilms tumor aren0533-tumor-annotations this dataset contains image annotations derived from the nci clinical trial \\\"combination chemotherapy with or without radiation therapy in treating young patients with newly diagnosed stage iii or stage iv wilms tumor (aren0533)\\u201d. \\u00a0this dataset was generated as part of an nci project to augment tcia datasets with annotations that will improve their value for cancer researchers and ai developers.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient the following time points were annotated:\\r\\n\\r\\n \\tpre-treatment ct chest and abdomen\\r\\n \\tct chest and\\\/or ct abdomen at 6 weeks\\r\\n \\tpossible ct abdomen at 12 weeks.\\r\\n\\r\\nin a typical patient the following annotation rules were followed:\\r\\n\\r\\n \\tthe primary renal tumor(s) were annotated on post-contrast axial series. normal renal parenchyma were excluded.\\r\\n \\ta maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. the same 5 lesions were annotated at each time point.\\r\\n \\trecist 1.1 principles were generally followed for lesion annotation, however if <5 lesions measuring >1 cm were present, then smaller lesions were annotated, again up to 2 lesions per organ or 5 lesions per patient scan.\\r\\n \\tbone lesions were included if other lesions were not present.\\r\\n \\tto ensure a high standard of accuracy and data quality, each annotation was reviewed by a secondary reader.\\r\\n\\r\\nat each timepoint:\\r\\n\\r\\n \\tvolumetric segmentations, seed points and negative finding assessments (dicom rtstruct) were created.\\r\\n \\tsnomed-ct \\u201canatomic region sequence\\u201d and \\u201csegmented property category code sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\timaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cclinical trial time point id\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tthe content item in \\u201cacquisition context sequence\\u201d was added containing \\\"time point type\\\" using concept code sequence (0040,a168) selected from:\\r\\n\\r\\n \\t(255235001, sct, \\u201cpre-dose\\u201d)\\r\\n \\t(262502001, sct, \\\"post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing key details about the annotations is available in the data access\\u00a0section below.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface application and the rest api (with authentication)\\u00a0to access these data can be found in the\\u00a0additional resources\\u00a0section below. wilms tumor kidney rtstruct classification measurement nctn\"},{\"id\":46249,\"type\":\"analysis\",\"slug\":\"aren0534-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/aren0534-tumor-annotations\\\/\",\"title\":\"Annotations for Combination Chemotherapy and Surgery in Treating Young Patients With Wilms Tumor\",\"short_title\":\"AREN0534-Tumor-Annotations\",\"doi\":\"10.7937\\\/N930-BM78\",\"summary\":\"This dataset contains image annotations derived from the NCI Clinical Trial \\\"Combination Chemotherapy and Surgery in Treating Young Patients With Wilms Tumor (AREN0534)\\u201d.\\u00a0 This dataset was generated as part of an NCI project to augment TCIA datasets with annotations that will improve their value for cancer researchers and AI developers.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient the following time points were annotated:\\r\\n\\r\\n \\tPre-surgical CT chest and CT\\\/MRI abdomen\\r\\n \\tCT chest and\\\/or CT\\\/MRI abdomen at 6 weeks\\r\\n \\tPossible CT\\\/MRI abdomen at 12 weeks.\\r\\n \\tAny negative imaging included past 12 weeks was annotated as negative. If any included imaging past 12 weeks is positive for tumor, the last positive exam was annotated.\\r\\n\\r\\nIn a typical patient the following annotation rules were followed:\\r\\n\\r\\n \\tThe primary renal tumor(s) were annotated on post-contrast axial series. Normal renal parenchyma were excluded.\\r\\n \\tA maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point.\\u00a0 RECIST 1.1 principles were followed for lesion annotation, however, if <5 lesions measuring >1 cm were present, then smaller lesions were annotated, again up to 2 lesions per organ or 5 lesions per patient scan. Bone lesions were included if other lesions were not present.\\r\\n \\tLesions were labeled separately.\\r\\n \\tSeed points were automatically generated but reviewed by a radiologist.\\r\\n \\tTo ensure a high standard of accuracy and data quality, each annotation was reviewed by a secondary reader.\\r\\n\\r\\nAt each time point:\\r\\n\\r\\n \\tA seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSS file.\\r\\n \\tSNOMED-CT \\u201cAnatomic Region Sequence\\u201d and \\u201cSegmented Property Category Code Sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\tImaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cClinical Trial Time Point ID\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d, \\u201cpost-chemotherapy\\u201d, or \\u201cpost-operative\\u201d.\\r\\n \\tContent Item in \\u201cAcquisition Context Sequence\\u201d will be added containing \\\"Time Point Type\\\" using Concept Code Sequence (0040,A168) selected from:\\r\\n\\r\\n \\t(255235001, SCT, \\u201cPre-dose\\u201d)\\r\\n \\t(262502001, SCT, \\\"Post-chemotherapy\\\")\\r\\n \\t(262061000, SCT, \\\"Post-operative\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing key details about the annotations is available in the Data Access\\u00a0section below.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface application and the REST API (with authentication)\\u00a0to access these data can be found in the\\u00a0Additional Resources\\u00a0section below.\",\"image\":false,\"program\":\"NCTN\",\"cancer_types\":[\"Wilms Tumor\"],\"cancer_locations\":[\"Kidney\"],\"data_types\":[\"RTSTRUCT\",\"Classification\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":239,\"date_updated\":\"2023-02-08\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?ImageModalityCriteria=RTSTRUCT&MinNumberOfStudiesCriteria=1&CollectionCriteria=AREN0534&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/AREN0534-Tumor-Annotations-manifest_2-8-2023.tcia\",\"blob\":\"annotations for combination chemotherapy and surgery in treating young patients with wilms tumor aren0534-tumor-annotations this dataset contains image annotations derived from the nci clinical trial \\\"combination chemotherapy and surgery in treating young patients with wilms tumor (aren0534)\\u201d.\\u00a0 this dataset was generated as part of an nci project to augment tcia datasets with annotations that will improve their value for cancer researchers and ai developers.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient the following time points were annotated:\\r\\n\\r\\n \\tpre-surgical ct chest and ct\\\/mri abdomen\\r\\n \\tct chest and\\\/or ct\\\/mri abdomen at 6 weeks\\r\\n \\tpossible ct\\\/mri abdomen at 12 weeks.\\r\\n \\tany negative imaging included past 12 weeks was annotated as negative. if any included imaging past 12 weeks is positive for tumor, the last positive exam was annotated.\\r\\n\\r\\nin a typical patient the following annotation rules were followed:\\r\\n\\r\\n \\tthe primary renal tumor(s) were annotated on post-contrast axial series. normal renal parenchyma were excluded.\\r\\n \\ta maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. the same 5 lesions were annotated at each time point.\\u00a0 recist 1.1 principles were followed for lesion annotation, however, if <5 lesions measuring >1 cm were present, then smaller lesions were annotated, again up to 2 lesions per organ or 5 lesions per patient scan. bone lesions were included if other lesions were not present.\\r\\n \\tlesions were labeled separately.\\r\\n \\tseed points were automatically generated but reviewed by a radiologist.\\r\\n \\tto ensure a high standard of accuracy and data quality, each annotation was reviewed by a secondary reader.\\r\\n\\r\\nat each time point:\\r\\n\\r\\n \\ta seed point (kernel) was created for each segmented structure. the seed points for each segmentation are provided in a separate dicom rtss file.\\r\\n \\tsnomed-ct \\u201canatomic region sequence\\u201d and \\u201csegmented property category code sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\timaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cclinical trial time point id\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d, \\u201cpost-chemotherapy\\u201d, or \\u201cpost-operative\\u201d.\\r\\n \\tcontent item in \\u201cacquisition context sequence\\u201d will be added containing \\\"time point type\\\" using concept code sequence (0040,a168) selected from:\\r\\n\\r\\n \\t(255235001, sct, \\u201cpre-dose\\u201d)\\r\\n \\t(262502001, sct, \\\"post-chemotherapy\\\")\\r\\n \\t(262061000, sct, \\\"post-operative\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing key details about the annotations is available in the data access\\u00a0section below.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface application and the rest api (with authentication)\\u00a0to access these data can be found in the\\u00a0additional resources\\u00a0section below. wilms tumor kidney rtstruct classification nctn\"},{\"id\":45725,\"type\":\"analysis\",\"slug\":\"brats-tcga-gbm\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/brats-tcga-gbm\\\/\",\"title\":\"Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection\",\"short_title\":\"BraTS-TCGA-GBM\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2017.KLXWJJ1Q\",\"summary\":\"This data container describes both computer-aided and manually-corrected segmentation labels for the pre-operative multi-institutional scans of The Cancer Genome Atlas (TCGA) Glioblastoma Multiforme (GBM) collection, publicly available in The Cancer Imaging Archive (TCIA), coupled with a rich panel of radiomic features along with their corresponding skull-stripped and co-registered multimodal (i.e. T1, T1-Gd, T2, T2-FLAIR) magnetic resonance imaging (MRI) volumes in NIfTI format. Pre-operative multimodal MRI scans were identified in the TCGA-GBM collection via radiological assessment. These scans were initially skull-stripped and co-registered, before their tumor segmentation labels were produced by an automated hybrid generative-discriminative method, ranked first during the International multimodal BRAin Tumor Segmentation challenge (BRATS 2015). These segmentation labels were revised and any label misclassifications were manually corrected by an expert board-certified neuroradiologist. The final labels were used to extract a rich panel of imaging features, including intensity, volumetric, morphologic, histogram-based and textural parameters, as well as spatial information and diffusion properties extracted from glioma growth models. The generated computer-aided and manually-revised labels enable quantitative computational and clinical studies without the need to repeat manual annotations whilst allowing for comparison across studies. They can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational challenges. The provided panel of radiomic features may facilitate research integrative of the molecular characterization offered by TCGA, and hence allow associations with molecular markers, clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/BRATS_banner_noCaption.png\",\"program\":\"Community\",\"cancer_types\":[\"Glioblastoma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"Radiomic Feature\",\"Segmentation\"],\"supporting_data\":[\"Tumor segmentations\",\"radiomic image features\"],\"species\":[],\"subjects\":135,\"date_updated\":\"2017-07-17\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjU3NiIsInBhc3Njb2RlIjoiZTkwNWU1NTVmNTdlNTM3NDhiYmU5MTM3YTE0YTk3OTc0MTc3NmI3ZCIsInBhY2thZ2VfaWQiOiI1NzYiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"segmentation labels and radiomic features for the pre-operative scans of the tcga-gbm collection brats-tcga-gbm this data container describes both computer-aided and manually-corrected segmentation labels for the pre-operative multi-institutional scans of the cancer genome atlas (tcga) glioblastoma multiforme (gbm) collection, publicly available in the cancer imaging archive (tcia), coupled with a rich panel of radiomic features along with their corresponding skull-stripped and co-registered multimodal (i.e. t1, t1-gd, t2, t2-flair) magnetic resonance imaging (mri) volumes in nifti format. pre-operative multimodal mri scans were identified in the tcga-gbm collection via radiological assessment. these scans were initially skull-stripped and co-registered, before their tumor segmentation labels were produced by an automated hybrid generative-discriminative method, ranked first during the international multimodal brain tumor segmentation challenge (brats 2015). these segmentation labels were revised and any label misclassifications were manually corrected by an expert board-certified neuroradiologist. the final labels were used to extract a rich panel of imaging features, including intensity, volumetric, morphologic, histogram-based and textural parameters, as well as spatial information and diffusion properties extracted from glioma growth models. the generated computer-aided and manually-revised labels enable quantitative computational and clinical studies without the need to repeat manual annotations whilst allowing for comparison across studies. they can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational challenges. the provided panel of radiomic features may facilitate research integrative of the molecular characterization offered by tcga, and hence allow associations with molecular markers, clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features. glioblastoma brain mr radiomic feature segmentation tumor segmentations radiomic image features community\"},{\"id\":45739,\"type\":\"analysis\",\"slug\":\"brats-tcga-lgg\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/brats-tcga-lgg\\\/\",\"title\":\"Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection\",\"short_title\":\"BraTS-TCGA-LGG\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2017.GJQ7R0EF\",\"summary\":\"This data container describes both computer-aided and manually-corrected segmentation labels for the pre-operative multi-institutional scans of The Cancer Genome Atlas (TCGA) Low Grade Glioma (LGG) collection, publicly available in The Cancer Imaging Archive (TCIA), coupled with a rich panel of radiomic features along with their corresponding skull-stripped and co-registered multimodal (i.e. T1, T1-Gd, T2, T2-FLAIR) magnetic resonance imaging (MRI) volumes in NIfTI format. Pre-operative multimodal MRI scans were identified in the TCGA-LGG collection via radiological assessment. These scans were initially skull-stripped and co-registered, before their tumor segmentation labels were produced by an automated hybrid generative-discriminative method, ranked first during the International multimodal BRAin Tumor Segmentation challenge (BRATS 2015). These segmentation labels were revised and any label misclassifications were manually corrected by an expert board-certified neuroradiologist. The final labels were used to extract a rich panel of imaging features, including intensity, volumetric, morphologic, histogram-based and textural parameters, as well as spatial information and diffusion properties extracted from glioma growth models. The generated computer-aided and manually-revised labels enable quantitative computational and clinical studies without the need to repeat manual annotations whilst allowing for comparison across studies. They can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational challenges. The provided panel of radiomic features may facilitate research integrative of the molecular characterization offered by TCGA, and hence allow associations with molecular markers, clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/BRATS_banner_noCaption.png\",\"program\":\"Community\",\"cancer_types\":[\"Low Grade Glioma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"Segmentation\"],\"supporting_data\":[\"Tumor segmentations\",\"radiomic image features\"],\"species\":[],\"subjects\":108,\"date_updated\":\"2017-07-17\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjYwMCIsInBhc3Njb2RlIjoiNmRkMWUzNTlhMWUwMDY0ZTZmNGI3NmE3YTczZDljZTZlMzJkMjA4OSIsInBhY2thZ2VfaWQiOiI2MDAiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"segmentation labels and radiomic features for the pre-operative scans of the tcga-lgg collection brats-tcga-lgg this data container describes both computer-aided and manually-corrected segmentation labels for the pre-operative multi-institutional scans of the cancer genome atlas (tcga) low grade glioma (lgg) collection, publicly available in the cancer imaging archive (tcia), coupled with a rich panel of radiomic features along with their corresponding skull-stripped and co-registered multimodal (i.e. t1, t1-gd, t2, t2-flair) magnetic resonance imaging (mri) volumes in nifti format. pre-operative multimodal mri scans were identified in the tcga-lgg collection via radiological assessment. these scans were initially skull-stripped and co-registered, before their tumor segmentation labels were produced by an automated hybrid generative-discriminative method, ranked first during the international multimodal brain tumor segmentation challenge (brats 2015). these segmentation labels were revised and any label misclassifications were manually corrected by an expert board-certified neuroradiologist. the final labels were used to extract a rich panel of imaging features, including intensity, volumetric, morphologic, histogram-based and textural parameters, as well as spatial information and diffusion properties extracted from glioma growth models. the generated computer-aided and manually-revised labels enable quantitative computational and clinical studies without the need to repeat manual annotations whilst allowing for comparison across studies. they can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational challenges. the provided panel of radiomic features may facilitate research integrative of the molecular characterization offered by tcga, and hence allow associations with molecular markers, clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features. low grade glioma brain mr segmentation tumor segmentations radiomic image features community\"},{\"id\":46285,\"type\":\"analysis\",\"slug\":\"calgb50303-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/calgb50303-tumor-annotations\\\/\",\"title\":\"Annotations for Rituximab and Combination Chemotherapy in Treating Patients With Diffuse Large B-Cell Non-Hodgkin's Lymphoma\",\"short_title\":\"CALGB50303-Tumor-Annotations\",\"doi\":\"10.7937\\\/9JER-G980\",\"summary\":\"This dataset contains image annotations derived from the NCI Clinical Trial \\\"Rituximab and Combination Chemotherapy in Treating Patients With Diffuse Large B-Cell Non-Hodgkin's Lymphoma (CALGB50303)\\u201d. \\u00a0This dataset was generated as part of an NCI project to augment TCIA datasets with annotations that will improve their value for cancer researchers and AI developers.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient all available time points were annotated.\\r\\n\\r\\nIn a typical patient the following annotation rules were followed:\\r\\n\\r\\na. \\u00a0 \\u00a0PERCIST criteria was followed. Specifically, the lesions estimated to have the most elevated SUVmax were annotated.\\r\\nb. \\u00a0 \\u00a0Lesions were annotated in the axial plane. If no axial plane were available, lesions were annotated in the coronal plane.\\r\\nc. \\u00a0 \\u00a0Lesions were annotated on the attenuation corrected PET series as well as the diagnostic contrast-enhanced CT. If no diagnostic contrast-enhanced CT was available for that timepoint, then the non-contrast CT portion of the PET\\\/CT was annotated.\\r\\nd. \\u00a0 \\u00a0A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. For the purposes of this project, the lymph nodes constitute 1 organ, while other lymphatic structures such as the spleen, salivary glands, and Waldeyer\\u2019s ring structures constitute separate organs. \\u00a0The same 5 lesions were annotated at each time point. \\u00a0RECIST 1.1 principles were followed. Specifically, lymph nodes were annotated if > 1.5 cm in short axis. Other lesions were annotated if > 1 cm.\\r\\ne. \\u00a0 \\u00a0Lesions were labeled separately.\\r\\nf. \\u00a0 \\u00a0Seed points were automatically generated and reviewed by a radiologist.\\r\\n\\r\\nAt each time point:\\r\\n\\r\\n \\tA seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSS file.\\r\\n \\tSNOMED-CT \\u201cAnatomic Region Sequence\\u201d and \\u201cSegmented Property Category Code Sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201cTracking ID\\u201d and \\u201cTracking UID\\u201d tags were inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\tImaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cClinical Trial Time Point ID\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d\\r\\n \\tContent Item in \\u201cAcquisition Context Sequence\\u201d was added containing \\\"Time Point Type\\\" using Concept Code Sequence (0040,A168) selected from:\\r\\n\\r\\n \\t(255235001, SCT, \\u201cPre-dose\\u201d)\\r\\n \\t(262502001, SCT, \\\"Post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing key details about the annotations is available in the\\u00a0Data Access\\u00a0section below.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface\\u00a0application and the\\u00a0REST API (with authentication)\\u00a0to access these data can be found in the\\u00a0Additional Resources\\u00a0section below.\",\"image\":false,\"program\":\"NCTN\",\"cancer_types\":[\"Lymphoma\"],\"cancer_locations\":[\"Various\"],\"data_types\":[\"RTSTRUCT\",\"Classification\",\"Measurement\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":155,\"date_updated\":\"2023-03-30\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?ImageModalityCriteria=RTSTRUCT&DateRange=03\\\/14\\\/2023-04\\\/01\\\/2023&CollectionCriteria=CALGB50303&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CALGB50303_Tumor-Annotations-manifest_03-30-2023.tcia\",\"blob\":\"annotations for rituximab and combination chemotherapy in treating patients with diffuse large b-cell non-hodgkin's lymphoma calgb50303-tumor-annotations this dataset contains image annotations derived from the nci clinical trial \\\"rituximab and combination chemotherapy in treating patients with diffuse large b-cell non-hodgkin's lymphoma (calgb50303)\\u201d. \\u00a0this dataset was generated as part of an nci project to augment tcia datasets with annotations that will improve their value for cancer researchers and ai developers.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient all available time points were annotated.\\r\\n\\r\\nin a typical patient the following annotation rules were followed:\\r\\n\\r\\na. \\u00a0 \\u00a0percist criteria was followed. specifically, the lesions estimated to have the most elevated suvmax were annotated.\\r\\nb. \\u00a0 \\u00a0lesions were annotated in the axial plane. if no axial plane were available, lesions were annotated in the coronal plane.\\r\\nc. \\u00a0 \\u00a0lesions were annotated on the attenuation corrected pet series as well as the diagnostic contrast-enhanced ct. if no diagnostic contrast-enhanced ct was available for that timepoint, then the non-contrast ct portion of the pet\\\/ct was annotated.\\r\\nd. \\u00a0 \\u00a0a maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. for the purposes of this project, the lymph nodes constitute 1 organ, while other lymphatic structures such as the spleen, salivary glands, and waldeyer\\u2019s ring structures constitute separate organs. \\u00a0the same 5 lesions were annotated at each time point. \\u00a0recist 1.1 principles were followed. specifically, lymph nodes were annotated if > 1.5 cm in short axis. other lesions were annotated if > 1 cm.\\r\\ne. \\u00a0 \\u00a0lesions were labeled separately.\\r\\nf. \\u00a0 \\u00a0seed points were automatically generated and reviewed by a radiologist.\\r\\n\\r\\nat each time point:\\r\\n\\r\\n \\ta seed point (kernel) was created for each segmented structure. the seed points for each segmentation are provided in a separate dicom rtss file.\\r\\n \\tsnomed-ct \\u201canatomic region sequence\\u201d and \\u201csegmented property category code sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201ctracking id\\u201d and \\u201ctracking uid\\u201d tags were inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\timaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cclinical trial time point id\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d\\r\\n \\tcontent item in \\u201cacquisition context sequence\\u201d was added containing \\\"time point type\\\" using concept code sequence (0040,a168) selected from:\\r\\n\\r\\n \\t(255235001, sct, \\u201cpre-dose\\u201d)\\r\\n \\t(262502001, sct, \\\"post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing key details about the annotations is available in the\\u00a0data access\\u00a0section below.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface\\u00a0application and the\\u00a0rest api (with authentication)\\u00a0to access these data can be found in the\\u00a0additional resources\\u00a0section below. lymphoma various rtstruct classification measurement nctn\"},{\"id\":46313,\"type\":\"analysis\",\"slug\":\"cptac-ccrcc-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/cptac-ccrcc-tumor-annotations\\\/\",\"title\":\"Annotations for The Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma Collection\",\"short_title\":\"CPTAC-CCRCC-Tumor-Annotations\",\"doi\":\"10.7937\\\/SKQ4-QX48\",\"summary\":\"This dataset contains image annotations derived from \\\"The Clinical Proteomic Tumor Analysis Consortium Clear Cell Renal Cell Carcinoma Collection (CPTAC-CCRCC)\\u201d.\\u00a0 This dataset was generated as part of a National Cancer Institute project to augment images from The Cancer Imaging Archive with annotations that will improve their value for cancer researchers and artificial intelligence experts.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient all available time points were annotated. The following annotation rules were followed:\\r\\n\\r\\n \\tRECIST 1.1 was generally followed for MR and CT imaging. A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point. Lymph nodes were annotated if > 1 cm in short axis. Other lesions were annotated if > 1 cm. If the primary lesion measures < 1 cm, it was still annotated.\\r\\n \\tThree-dimensional segmentations of lesions were created in the axial plane. If no axial plane was available, lesions were annotated in the available plane.\\r\\n \\tMRIs were annotated using all axial T1-weighted post contrast sequences.\\r\\n \\tCTs were annotated using all axial post contrast series.\\r\\n \\tLesions were labeled separately.\\r\\n \\tSeed points were automatically generated, but reviewed by a radiologist.\\r\\n \\tA \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nAt each time point:\\r\\n\\r\\n \\tVolume calculations were performed for each segmented structure. \\u00a0These calculations are provided in the Annotation Metadata CSV.\\r\\n \\tA seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.\\r\\n \\tSNOMED-CT \\u201cAnatomic Region Sequence\\u201d and \\u201cSegmented Property Category Code Sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201cTracking ID\\u201d and \\u201cTracking UID\\u201d tags were inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\tImaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cClinical Trial Time Point ID\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tContent Item in \\u201cAcquisition Context Sequence\\u201d was added containing \\\"Time Point Type\\\" using Concept Code Sequence (0040,A168) selected from:\\r\\n\\r\\n \\t(255235001, SCT, \\u201cPre-dose\\u201d)\\r\\n \\t(719864002, SCT, \\\"Post-cancer treatment monitoring\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing key details about the annotations is available in the\\u00a0Data Access\\u00a0section below.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface\\u00a0application and the\\u00a0REST API\\u00a0to access these data can be found in the\\u00a0Additional Resources\\u00a0section below.\",\"image\":false,\"program\":\"CPTAC\",\"cancer_types\":[\"Clear Cell Carcinoma\"],\"cancer_locations\":[\"Kidney\"],\"data_types\":[\"RTSTRUCT\",\"Classification\",\"Measurement\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":60,\"date_updated\":\"2023-07-24\",\"status\":\"\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=CPTAC-CCRCC&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CPTAC-CCRCC_Tumor-Annotations-manifest_07-03-2023.tcia\",\"blob\":\"annotations for the clinical proteomic tumor analysis consortium clear cell renal cell carcinoma collection cptac-ccrcc-tumor-annotations this dataset contains image annotations derived from \\\"the clinical proteomic tumor analysis consortium clear cell renal cell carcinoma collection (cptac-ccrcc)\\u201d.\\u00a0 this dataset was generated as part of a national cancer institute project to augment images from the cancer imaging archive with annotations that will improve their value for cancer researchers and artificial intelligence experts.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient all available time points were annotated. the following annotation rules were followed:\\r\\n\\r\\n \\trecist 1.1 was generally followed for mr and ct imaging. a maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. the same 5 lesions were annotated at each time point. lymph nodes were annotated if > 1 cm in short axis. other lesions were annotated if > 1 cm. if the primary lesion measures < 1 cm, it was still annotated.\\r\\n \\tthree-dimensional segmentations of lesions were created in the axial plane. if no axial plane was available, lesions were annotated in the available plane.\\r\\n \\tmris were annotated using all axial t1-weighted post contrast sequences.\\r\\n \\tcts were annotated using all axial post contrast series.\\r\\n \\tlesions were labeled separately.\\r\\n \\tseed points were automatically generated, but reviewed by a radiologist.\\r\\n \\ta \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nat each time point:\\r\\n\\r\\n \\tvolume calculations were performed for each segmented structure. \\u00a0these calculations are provided in the annotation metadata csv.\\r\\n \\ta seed point (kernel) was created for each segmented structure. the seed points for each segmentation are provided in a separate dicom rtstruct file.\\r\\n \\tsnomed-ct \\u201canatomic region sequence\\u201d and \\u201csegmented property category code sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201ctracking id\\u201d and \\u201ctracking uid\\u201d tags were inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\timaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cclinical trial time point id\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tcontent item in \\u201cacquisition context sequence\\u201d was added containing \\\"time point type\\\" using concept code sequence (0040,a168) selected from:\\r\\n\\r\\n \\t(255235001, sct, \\u201cpre-dose\\u201d)\\r\\n \\t(719864002, sct, \\\"post-cancer treatment monitoring\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing key details about the annotations is available in the\\u00a0data access\\u00a0section below.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface\\u00a0application and the\\u00a0rest api\\u00a0to access these data can be found in the\\u00a0additional resources\\u00a0section below. clear cell carcinoma kidney rtstruct classification measurement cptac\"},{\"id\":48963,\"type\":\"analysis\",\"slug\":\"cptac-glioblastoma-codex\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/cptac-glioblastoma-codex\\\/\",\"title\":\"Multi-scale signaling and tumor evolution in high-grade gliomas\",\"short_title\":\"CPTAC-Glioblastoma-CODEX\",\"doi\":\"10.7937\\\/ce1t-ea12\",\"summary\":\"Co-detection by indexing (CODEX) based multiplexed imaging was performed on 18 samples from 12 patients, including 8 treatment-naive IDH-wt glioblastoma (GBM), 6 post-treatment recurrent IDH-wt GBM from matching patients, 2 IDH-mut grade 4 astrocytoma and 2 normal adjacent brain. Hematoxylin and Eosin staining on adjacent sections was also included for pathological annotation.\\r\\n\\r\\nPatient inclusion criteria: Only histopathologically defined adult GBMs, grade 4 IDH-mutant astrocytomas and normal adjacent brain tissue were considered. Clinical data were obtained from the tissue source sites and reviewed for correctness and completeness of data.\\r\\n\\r\\nImaging protocol: A panel of 25 CODEX antibodies were designed for the human brain. Carrier-free antibodies were verified for their specificity by using immunofluorescence (IF) staining in multiple channels. Once verified, antibodies were conjugated using Akoya Antibody Conjugation Kit (Akoya Biosciences, SKU 7000009) with a barcode (Akoya Biosciences) assigned based on the IF staining results. Several common markers were directly purchased through Akoya Biosciences. CODEX staining and imaging were performed according to the manufacturer\\u2019s instruction (CODEX User Manual - Rev C). Briefly, 5 \\u03bcm FFPE sections were placed on APTES (Sigma, #440140) coated coverslips and baked at 60\\u00b0C overnight before deparaffinization. The next day, tissues were incubated in xylene, rehydrated in ethanol, and washed in ddH2O before antigen retrieval with TE buffer, pH 9 (Genemed, #10-0046) in boiling water for 10 min in a pressure cooker. Sections were then blocked using the blocking buffer (CODEX staining kit, SKU 7000008) and stained with the 25-marker antibody panel to a volume of 200 \\u00b5l for 3 hours at room temperature in a humidified chamber. Imaging of the CODEX multicycle experiment was performed using Keyence fluorescence microscope (model BZ-X810) equipped with a Nikon CFI Plan Apo \\u03bb 20x\\\/0.75 objective, the CODEX instrument (Akoya Biosciences, USA), and CODEX Instrument Manager (CIM) (Akoya Biosciences, USA). The raw images were then stitched and processed using the CODEX processor (Akoya Biosciences, USA). After multiplex imaging was completed, hematoxylin and eosin (H&E) staining was performed on the adjacent section from the same tissue.\\r\\n\\r\\nImaging analysis description: Multiplex images were segmented using the Mesmer pretrained nuclei + membrane segmentation model in the Deepcell cell segmentation library. The DAPI channel was used as the nuclei segmentation image, and CD45, CD8, CD31, CD4, HLA-DR, GFAP, CD68 channels were merged and used as the membrane segmentation image. Following segmentation, cells were classified with gating strategy (Endothelial cells: CD31+, macrophages: IBA1+ and\\\/or CD163+, malignant cells: OLIG2+ and\\\/or GFAP+, CD4 T cell: CD4+ and IBA1-, CD8 T cell: CD8+ and IBA-, monocyte\\\/granulocyte: CD11b+). To eliminate batch effects, marker thresholds were set manually for each image.\\r\\n\\r\\nBenefit to researchers: This dataset can serve as a resource to further understand the spatial cellular organization in IDH-mut and IDH-with high-grade gliomas, as well as primary and recurrent (post-treatment) glioblastoma.\",\"image\":false,\"program\":\"CPTAC\",\"cancer_types\":[\"Glioblastoma\",\"Astrocytoma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"CODEX images\",\"Whole Slide Image\",\"Pathology Detail\",\"Follow-Up\"],\"supporting_data\":[\"Clinical\"],\"species\":[\"Human\"],\"subjects\":12,\"date_updated\":\"2024-07-23\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/pathdb.cancerimagingarchive.net\\\/eaglescope\\\/dist\\\/?configurl=\\\/system\\\/files\\\/collectionmetadata\\\/202407\\\/CPTAC-Glioblastoma-CODEX_07-09-2024.json\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjkyNyIsInBhc3Njb2RlIjoiYjg2YjIxOWNiNGI5OTY5NmQ4YTJkN2VkYzIzZjY5NGI3NDk3MmUyMCIsInBhY2thZ2VfaWQiOiI5MjciLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"multi-scale signaling and tumor evolution in high-grade gliomas cptac-glioblastoma-codex co-detection by indexing (codex) based multiplexed imaging was performed on 18 samples from 12 patients, including 8 treatment-naive idh-wt glioblastoma (gbm), 6 post-treatment recurrent idh-wt gbm from matching patients, 2 idh-mut grade 4 astrocytoma and 2 normal adjacent brain. hematoxylin and eosin staining on adjacent sections was also included for pathological annotation.\\r\\n\\r\\npatient inclusion criteria: only histopathologically defined adult gbms, grade 4 idh-mutant astrocytomas and normal adjacent brain tissue were considered. clinical data were obtained from the tissue source sites and reviewed for correctness and completeness of data.\\r\\n\\r\\nimaging protocol: a panel of 25 codex antibodies were designed for the human brain. carrier-free antibodies were verified for their specificity by using immunofluorescence (if) staining in multiple channels. once verified, antibodies were conjugated using akoya antibody conjugation kit (akoya biosciences, sku 7000009) with a barcode (akoya biosciences) assigned based on the if staining results. several common markers were directly purchased through akoya biosciences. codex staining and imaging were performed according to the manufacturer\\u2019s instruction (codex user manual - rev c). briefly, 5 \\u03bcm ffpe sections were placed on aptes (sigma, #440140) coated coverslips and baked at 60\\u00b0c overnight before deparaffinization. the next day, tissues were incubated in xylene, rehydrated in ethanol, and washed in ddh2o before antigen retrieval with te buffer, ph 9 (genemed, #10-0046) in boiling water for 10 min in a pressure cooker. sections were then blocked using the blocking buffer (codex staining kit, sku 7000008) and stained with the 25-marker antibody panel to a volume of 200 \\u00b5l for 3 hours at room temperature in a humidified chamber. imaging of the codex multicycle experiment was performed using keyence fluorescence microscope (model bz-x810) equipped with a nikon cfi plan apo \\u03bb 20x\\\/0.75 objective, the codex instrument (akoya biosciences, usa), and codex instrument manager (cim) (akoya biosciences, usa). the raw images were then stitched and processed using the codex processor (akoya biosciences, usa). after multiplex imaging was completed, hematoxylin and eosin (h&e) staining was performed on the adjacent section from the same tissue.\\r\\n\\r\\nimaging analysis description: multiplex images were segmented using the mesmer pretrained nuclei + membrane segmentation model in the deepcell cell segmentation library. the dapi channel was used as the nuclei segmentation image, and cd45, cd8, cd31, cd4, hla-dr, gfap, cd68 channels were merged and used as the membrane segmentation image. following segmentation, cells were classified with gating strategy (endothelial cells: cd31+, macrophages: iba1+ and\\\/or cd163+, malignant cells: olig2+ and\\\/or gfap+, cd4 t cell: cd4+ and iba1-, cd8 t cell: cd8+ and iba-, monocyte\\\/granulocyte: cd11b+). to eliminate batch effects, marker thresholds were set manually for each image.\\r\\n\\r\\nbenefit to researchers: this dataset can serve as a resource to further understand the spatial cellular organization in idh-mut and idh-with high-grade gliomas, as well as primary and recurrent (post-treatment) glioblastoma. glioblastoma astrocytoma brain codex images whole slide image pathology detail follow-up clinical cptac\"},{\"id\":46349,\"type\":\"analysis\",\"slug\":\"cptac-hnscc-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/cptac-hnscc-tumor-annotations\\\/\",\"title\":\"Annotations for The Clinical Proteomic Tumor Analysis Consortium Head and Neck Squamous Cell Carcinoma Collection\",\"short_title\":\"CPTAC-HNSCC-Tumor-Annotations\",\"doi\":\"10.7937\\\/PFEC-T641\",\"summary\":\"This dataset contains image annotations derived from \\\"The Clinical Proteomic Tumor Analysis Consortium Head and Neck Squamous Cell Carcinoma Collection (CPTAC-HNSCC)\\u201d.\\u00a0 This dataset was generated as part of a National Cancer Institute project to augment images from The Cancer Imaging Archive with tumor annotations that will improve their value for cancer researchers and artificial intelligence experts.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient all available time points were annotated. The following annotation rules were followed:\\r\\n\\r\\n \\tPERCIST criteria was followed for PET imaging. Specifically, the lesions estimated to have the most elevated SUVmax were annotated.\\r\\n \\tRECIST 1.1 was otherwise generally followed for MR and CT imaging. A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point. Lymph nodes were annotated if >1 cm in short axis. Other lesions were annotated if >1 cm. The primary tumor was still annotated if < 1 cm.\\r\\n \\tThree-dimensional segmentations of lesions were created in the axial plane. If no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tMRIs were annotated using the T1-weighted axial post contrast sequence.\\r\\n \\tCTs were annotated using all axial post contrast series.\\r\\n \\tPET\\\/CTs were annotated on the CT and attenuation corrected PET images, unless there was a diagnostic CT from the same time point, in which case the CT portion of the PET\\\/CT was not annotated.\\r\\n \\tLesions were labeled separately.\\r\\n \\tSeed points were automatically generated, but reviewed by a radiologist.\\r\\n \\tA \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nAt each time point:\\r\\n\\r\\n \\tVolume calculations were performed for each segmented structure. \\u00a0These calculations are provided in the Annotation Metadata CSV.\\r\\n \\tA seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.\\r\\n \\tSNOMED-CT \\u201cAnatomic Region Sequence\\u201d and \\u201cSegmented Property Category Code Sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\tImaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cClinical Trial Time Point ID\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tContent Item in \\u201cAcquisition Context Sequence\\u201d was added containing \\\"Time Point Type\\\" using Concept Code Sequence (0040,A168) selected from:\\r\\n\\r\\n \\t(255235001, SCT, \\u201cPre-dose\\u201d)\\r\\n \\t(719864002, SCT, \\\"Post-cancer treatment monitoring\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing key details about the annotations is available in the\\u00a0Data Access\\u00a0section below.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface\\u00a0application and the REST API\\u00a0to access these data can be found in the\\u00a0Additional Resources\\u00a0section below.\",\"image\":false,\"program\":\"CPTAC\",\"cancer_types\":[\"Head and Neck Squamous Cell Carcinoma\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"RTSTRUCT\",\"Classification\",\"Measurement\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":101,\"date_updated\":\"2023-07-24\",\"status\":\"\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?DateRange=07%2F10%2F2023-07%2F24%2F2023&CollectionCriteria=CPTAC-HNSCC&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CPTAC-HNSCC_Tumor-Annotations-manifest_07-14-2023.tcia\",\"blob\":\"annotations for the clinical proteomic tumor analysis consortium head and neck squamous cell carcinoma collection cptac-hnscc-tumor-annotations this dataset contains image annotations derived from \\\"the clinical proteomic tumor analysis consortium head and neck squamous cell carcinoma collection (cptac-hnscc)\\u201d.\\u00a0 this dataset was generated as part of a national cancer institute project to augment images from the cancer imaging archive with tumor annotations that will improve their value for cancer researchers and artificial intelligence experts.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient all available time points were annotated. the following annotation rules were followed:\\r\\n\\r\\n \\tpercist criteria was followed for pet imaging. specifically, the lesions estimated to have the most elevated suvmax were annotated.\\r\\n \\trecist 1.1 was otherwise generally followed for mr and ct imaging. a maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. the same 5 lesions were annotated at each time point. lymph nodes were annotated if >1 cm in short axis. other lesions were annotated if >1 cm. the primary tumor was still annotated if < 1 cm.\\r\\n \\tthree-dimensional segmentations of lesions were created in the axial plane. if no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tmris were annotated using the t1-weighted axial post contrast sequence.\\r\\n \\tcts were annotated using all axial post contrast series.\\r\\n \\tpet\\\/cts were annotated on the ct and attenuation corrected pet images, unless there was a diagnostic ct from the same time point, in which case the ct portion of the pet\\\/ct was not annotated.\\r\\n \\tlesions were labeled separately.\\r\\n \\tseed points were automatically generated, but reviewed by a radiologist.\\r\\n \\ta \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nat each time point:\\r\\n\\r\\n \\tvolume calculations were performed for each segmented structure. \\u00a0these calculations are provided in the annotation metadata csv.\\r\\n \\ta seed point (kernel) was created for each segmented structure. the seed points for each segmentation are provided in a separate dicom rtstruct file.\\r\\n \\tsnomed-ct \\u201canatomic region sequence\\u201d and \\u201csegmented property category code sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\timaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cclinical trial time point id\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tcontent item in \\u201cacquisition context sequence\\u201d was added containing \\\"time point type\\\" using concept code sequence (0040,a168) selected from:\\r\\n\\r\\n \\t(255235001, sct, \\u201cpre-dose\\u201d)\\r\\n \\t(719864002, sct, \\\"post-cancer treatment monitoring\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing key details about the annotations is available in the\\u00a0data access\\u00a0section below.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface\\u00a0application and the rest api\\u00a0to access these data can be found in the\\u00a0additional resources\\u00a0section below. head and neck squamous cell carcinoma head-neck rtstruct classification measurement cptac\"},{\"id\":46325,\"type\":\"analysis\",\"slug\":\"cptac-pda-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/cptac-pda-tumor-annotations\\\/\",\"title\":\"Annotations for The Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma Collection\",\"short_title\":\"CPTAC-PDA-Tumor-Annotations\",\"doi\":\"10.7937\\\/BW9V-BX61\",\"summary\":\"This dataset contains image annotations derived from \\\"The Clinical Proteomic Tumor Analysis Consortium Pancreatic Ductal Adenocarcinoma Collection (CPTAC-PDA)\\u201d.\\u00a0 This dataset was generated as part of a National Cancer Institute project to augment images from The Cancer Imaging Archive with tumor annotations that will improve their value for cancer researchers and artificial intelligence experts.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient all available time points were annotated. The following annotation rules were followed:\\r\\n\\r\\n \\tPERCIST criteria was followed for PET imaging. Specifically, the lesions estimated to have the most elevated SUVmax were annotated.\\r\\n \\tRECIST 1.1 was otherwise generally followed for MR and CT imaging. A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point. Lymph nodes were annotated if >1 cm in short axis. Other lesions were annotated if >1 cm. If the primary lesion measures < 1 cm, it was still annotated.\\r\\n \\tThree-dimensional segmentations of lesions were created in the axial plane. If no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tMRIs were annotated using axial T1-weighted post contrast sequences that best demonstrated the tumor.\\r\\n \\tCTs were annotated using all axial post contrast series. If not available, the axial non-contrast series were annotated.\\r\\n \\tPET\\\/CTs were annotated on the CT and attenuation corrected PET images, unless there was a diagnostic CT from the same time point, in which case the CT portion of the PET\\\/CT was not annotated.\\r\\n \\tLesions were labeled separately.\\r\\n \\tSeed points were automatically generated, but reviewed by a radiologist.\\r\\n \\tA \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nAt each time point:\\r\\n\\r\\n \\tVolume calculations were performed for each segmented structure. \\u00a0These calculations are provided in the Annotation Metadata CSV.\\r\\n \\tA seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.\\r\\n \\tSNOMED-CT \\u201cAnatomic Region Sequence\\u201d and \\u201cSegmented Property Category Code Sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\tImaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cClinical Trial Time Point ID\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tContent Item in \\u201cAcquisition Context Sequence\\u201d was added containing \\\"Time Point Type\\\" using Concept Code Sequence (0040,A168) selected from:\\r\\n\\r\\n \\t(255235001, SCT, \\u201cPre-dose\\u201d)\\r\\n \\t(719864002, SCT, \\\"Post-cancer treatment monitoring\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing key details about the annotations is available in the\\u00a0Data Access\\u00a0section below.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface\\u00a0application and the\\u00a0REST API\\u00a0to access these data can be found in the\\u00a0Additional Resources\\u00a0section below.\",\"image\":false,\"program\":\"CPTAC\",\"cancer_types\":[\"Ductal Adenocarcinoma\"],\"cancer_locations\":[\"Pancreas\"],\"data_types\":[\"RTSTRUCT\",\"Classification\",\"Measurement\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":103,\"date_updated\":\"2023-07-24\",\"status\":\"\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=CPTAC-PDA&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CPTAC-PDA_Tumor-Annotations-manifest_07-05-2023.tcia\",\"blob\":\"annotations for the clinical proteomic tumor analysis consortium pancreatic ductal adenocarcinoma collection cptac-pda-tumor-annotations this dataset contains image annotations derived from \\\"the clinical proteomic tumor analysis consortium pancreatic ductal adenocarcinoma collection (cptac-pda)\\u201d.\\u00a0 this dataset was generated as part of a national cancer institute project to augment images from the cancer imaging archive with tumor annotations that will improve their value for cancer researchers and artificial intelligence experts.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient all available time points were annotated. the following annotation rules were followed:\\r\\n\\r\\n \\tpercist criteria was followed for pet imaging. specifically, the lesions estimated to have the most elevated suvmax were annotated.\\r\\n \\trecist 1.1 was otherwise generally followed for mr and ct imaging. a maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. the same 5 lesions were annotated at each time point. lymph nodes were annotated if >1 cm in short axis. other lesions were annotated if >1 cm. if the primary lesion measures < 1 cm, it was still annotated.\\r\\n \\tthree-dimensional segmentations of lesions were created in the axial plane. if no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tmris were annotated using axial t1-weighted post contrast sequences that best demonstrated the tumor.\\r\\n \\tcts were annotated using all axial post contrast series. if not available, the axial non-contrast series were annotated.\\r\\n \\tpet\\\/cts were annotated on the ct and attenuation corrected pet images, unless there was a diagnostic ct from the same time point, in which case the ct portion of the pet\\\/ct was not annotated.\\r\\n \\tlesions were labeled separately.\\r\\n \\tseed points were automatically generated, but reviewed by a radiologist.\\r\\n \\ta \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nat each time point:\\r\\n\\r\\n \\tvolume calculations were performed for each segmented structure. \\u00a0these calculations are provided in the annotation metadata csv.\\r\\n \\ta seed point (kernel) was created for each segmented structure. the seed points for each segmentation are provided in a separate dicom rtstruct file.\\r\\n \\tsnomed-ct \\u201canatomic region sequence\\u201d and \\u201csegmented property category code sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\timaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cclinical trial time point id\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tcontent item in \\u201cacquisition context sequence\\u201d was added containing \\\"time point type\\\" using concept code sequence (0040,a168) selected from:\\r\\n\\r\\n \\t(255235001, sct, \\u201cpre-dose\\u201d)\\r\\n \\t(719864002, sct, \\\"post-cancer treatment monitoring\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing key details about the annotations is available in the\\u00a0data access\\u00a0section below.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface\\u00a0application and the\\u00a0rest api\\u00a0to access these data can be found in the\\u00a0additional resources\\u00a0section below. ductal adenocarcinoma pancreas rtstruct classification measurement cptac\"},{\"id\":46337,\"type\":\"analysis\",\"slug\":\"cptac-ucec-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/cptac-ucec-tumor-annotations\\\/\",\"title\":\"Annotations for The Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma Collection\",\"short_title\":\"CPTAC-UCEC-Tumor-Annotations\",\"doi\":\"10.7937\\\/89M3-KQ43\",\"summary\":\"This dataset contains image annotations derived from \\\"The Clinical Proteomic Tumor Analysis Consortium Uterine Corpus Endometrial Carcinoma Collection (CPTAC-UCEC)\\u201d.\\u00a0 This dataset was generated as part of a National Cancer Institute project to augment images from The Cancer Imaging Archive with annotations that will improve their value for cancer researchers and artificial intelligence experts.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient all available time points were annotated. The following annotation rules were followed:\\r\\n\\r\\n \\tPERCIST criteria was followed for PET imaging. Specifically, the lesions estimated to have the most elevated SUVmax were annotated.\\r\\n \\tRECIST 1.1 was otherwise generally followed for MR and CT imaging. A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point. Lymph nodes were annotated if >1 cm in short axis. Other lesions were annotated if >1 cm. If the primary lesion measures < 1 cm, it was still annotated.\\r\\n \\tThree-dimensional segmentations of lesions were created in the axial plane. If no axial plane was available, lesions were annotated in the available plane.\\r\\n \\tMRIs were annotated using all available axial T1-weighted post contrast sequences.\\r\\n \\tCTs were annotated using the axial post contrast series if available. If not available, the axial non-contrast series were annotated as accurately as possible.\\r\\n \\tPET\\\/CTs were annotated on the CT and attenuation corrected PET images, unless there was a diagnostic CT from the same time point, in which case the CT portion of the PET\\\/CT was not annotated.\\r\\n \\tLesions were labeled separately.\\r\\n \\tA \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nAt each time point:\\r\\n\\r\\n \\tVolume calculations were performed for each segmented structure. \\u00a0These calculations are provided in the Annotation Metadata CSV.\\r\\n \\tA seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSTRUCT file.\\r\\n \\tSNOMED-CT \\u201cAnatomic Region Sequence\\u201d and \\u201cSegmented Property Category Code Sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\tImaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cClinical Trial Time Point ID\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tContent Item in \\u201cAcquisition Context Sequence\\u201d was added containing \\\"Time Point Type\\\" using Concept Code Sequence (0040,A168) selected from:\\r\\n\\r\\n \\t(255235001, SCT, \\u201cPre-dose\\u201d)\\r\\n \\t(719864002, SCT, \\\"Post-cancer treatment monitoring\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing key details about the annotations is available in the\\u00a0Data Access\\u00a0section below.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface\\u00a0application and the\\u00a0REST API\\u00a0to access these data can be found in the\\u00a0Additional Resources\\u00a0section below.\",\"image\":false,\"program\":\"CPTAC\",\"cancer_types\":[\"Corpus Endometrial Carcinoma\"],\"cancer_locations\":[\"Uterus\"],\"data_types\":[\"RTSTRUCT\",\"Classification\",\"Measurement\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":72,\"date_updated\":\"2023-07-24\",\"status\":\"\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?DateRange=07%2F01%2F2023-07%2F24%2F2023&CollectionCriteria=CPTAC-UCEC&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CPTAC-UCEC_Tumor-Annotations-manifest_07-14-2023.tcia\",\"blob\":\"annotations for the clinical proteomic tumor analysis consortium uterine corpus endometrial carcinoma collection cptac-ucec-tumor-annotations this dataset contains image annotations derived from \\\"the clinical proteomic tumor analysis consortium uterine corpus endometrial carcinoma collection (cptac-ucec)\\u201d.\\u00a0 this dataset was generated as part of a national cancer institute project to augment images from the cancer imaging archive with annotations that will improve their value for cancer researchers and artificial intelligence experts.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient all available time points were annotated. the following annotation rules were followed:\\r\\n\\r\\n \\tpercist criteria was followed for pet imaging. specifically, the lesions estimated to have the most elevated suvmax were annotated.\\r\\n \\trecist 1.1 was otherwise generally followed for mr and ct imaging. a maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. the same 5 lesions were annotated at each time point. lymph nodes were annotated if >1 cm in short axis. other lesions were annotated if >1 cm. if the primary lesion measures < 1 cm, it was still annotated.\\r\\n \\tthree-dimensional segmentations of lesions were created in the axial plane. if no axial plane was available, lesions were annotated in the available plane.\\r\\n \\tmris were annotated using all available axial t1-weighted post contrast sequences.\\r\\n \\tcts were annotated using the axial post contrast series if available. if not available, the axial non-contrast series were annotated as accurately as possible.\\r\\n \\tpet\\\/cts were annotated on the ct and attenuation corrected pet images, unless there was a diagnostic ct from the same time point, in which case the ct portion of the pet\\\/ct was not annotated.\\r\\n \\tlesions were labeled separately.\\r\\n \\ta \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nat each time point:\\r\\n\\r\\n \\tvolume calculations were performed for each segmented structure. \\u00a0these calculations are provided in the annotation metadata csv.\\r\\n \\ta seed point (kernel) was created for each segmented structure. the seed points for each segmentation are provided in a separate dicom rtstruct file.\\r\\n \\tsnomed-ct \\u201canatomic region sequence\\u201d and \\u201csegmented property category code sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\timaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cclinical trial time point id\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d.\\r\\n \\tcontent item in \\u201cacquisition context sequence\\u201d was added containing \\\"time point type\\\" using concept code sequence (0040,a168) selected from:\\r\\n\\r\\n \\t(255235001, sct, \\u201cpre-dose\\u201d)\\r\\n \\t(719864002, sct, \\\"post-cancer treatment monitoring\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing key details about the annotations is available in the\\u00a0data access\\u00a0section below.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface\\u00a0application and the\\u00a0rest api\\u00a0to access these data can be found in the\\u00a0additional resources\\u00a0section below. corpus endometrial carcinoma uterus rtstruct classification measurement cptac\"},{\"id\":45815,\"type\":\"analysis\",\"slug\":\"crowds-cure-2017\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/crowds-cure-2017\\\/\",\"title\":\"Crowds Cure Cancer: Data collected at the RSNA 2017 annual meeting\",\"short_title\":\"Crowds-Cure-2017\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2018.OW73VLO2\",\"summary\":\"Many Cancers routinely identified by imaging haven\\u2019t yet benefited from recent advances in computer science. Approaches such as machine learning and deep learning can generate quantitative tumor 3D volumes, complex features and therapy-tracking temporal dynamics. However, cross-disciplinary researchers striving to develop new approaches often lack disease understanding or sufficient contacts within the medical community. Their research can greatly benefit from labeling and annotating basic information in the images such as tumor locations, which are obvious to radiologists.\\r\\n\\r\\nCrowd-sourcing the creation of publicly-accessible reference data sets could address this challenge. In 2011 the National Cancer Institute funded development of The Cancer Imaging Archive (TCIA), a free and open-access database of medical images. However, most of these collections lack the labeling and annotations needed by image processing researchers for progress in deep learning and radiomics. As a result, TCIA has partnered with the Radiological Society of North America (RSNA) and numerous academic centers to harness the vast knowledge of RSNA meeting attendees to generate these tumor markups.\\u00a0 Data sets annotated included CT scans from 352 subjects from the\\u00a0The Cancer Genome Atlas Lung Adenocarcinoma Collection (TCGA-LUAD),\\u00a0The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma Collection (TCGA-KIRC),\\u00a0The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC), and\\u00a0The Cancer Genome Atlas Ovarian Cancer Collection (TCGA-OV)\\u00a0collections on TCIA.\\r\\n\\r\\nA full explanation of the project can be seen in the Detailed Description.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Lung Adenocarcinoma\",\"Renal Clear Cell\",\"Liver\",\"Ovarian\"],\"cancer_locations\":[\"Chest\",\"Kidney\",\"Liver\",\"Ovary\"],\"data_types\":[\"Measurement\",\"SR\",\"Demographic\",\"Follow-Up\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":352,\"date_updated\":\"2018-05-17\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CrowdsCureCancer2017Annotations.csv\",\"blob\":\"crowds cure cancer: data collected at the rsna 2017 annual meeting crowds-cure-2017 many cancers routinely identified by imaging haven\\u2019t yet benefited from recent advances in computer science. approaches such as machine learning and deep learning can generate quantitative tumor 3d volumes, complex features and therapy-tracking temporal dynamics. however, cross-disciplinary researchers striving to develop new approaches often lack disease understanding or sufficient contacts within the medical community. their research can greatly benefit from labeling and annotating basic information in the images such as tumor locations, which are obvious to radiologists.\\r\\n\\r\\ncrowd-sourcing the creation of publicly-accessible reference data sets could address this challenge. in 2011 the national cancer institute funded development of the cancer imaging archive (tcia), a free and open-access database of medical images. however, most of these collections lack the labeling and annotations needed by image processing researchers for progress in deep learning and radiomics. as a result, tcia has partnered with the radiological society of north america (rsna) and numerous academic centers to harness the vast knowledge of rsna meeting attendees to generate these tumor markups.\\u00a0 data sets annotated included ct scans from 352 subjects from the\\u00a0the cancer genome atlas lung adenocarcinoma collection (tcga-luad),\\u00a0the cancer genome atlas kidney renal clear cell carcinoma collection (tcga-kirc),\\u00a0the cancer genome atlas liver hepatocellular carcinoma collection (tcga-lihc), and\\u00a0the cancer genome atlas ovarian cancer collection (tcga-ov)\\u00a0collections on tcia.\\r\\n\\r\\na full explanation of the project can be seen in the detailed description. lung adenocarcinoma renal clear cell liver ovarian chest kidney liver ovary measurement sr demographic follow-up community\"},{\"id\":45927,\"type\":\"analysis\",\"slug\":\"crowds-cure-2018\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/crowds-cure-2018\\\/\",\"title\":\"Crowds Cure Cancer: Data collected at the RSNA 2018 annual meeting\",\"short_title\":\"Crowds-Cure-2018\",\"doi\":\"10.7937\\\/TCIA.2019.yk0gm1eb\",\"summary\":\"Description\\r\\nThe Cancer Imaging Archive (TCIA) has procured substantial troves of image data, which could serve as valuable training sets for improving machine learning algorithms. However, these datasets lack consistent lesion annotations. To address this issue, the\\u00a0Cancer Imaging Informatics Lab\\u00a0at the Frederick National Laboratory for Cancer Research (FNLCR) formed a partnership with five groups funded by the\\u00a0National Cancer Institute's Informatics Technology for Cancer Research program\\u00a0to develop a web-based crowdsourcing application for gathering lesion annotations, featured at the annual meeting of the Radiological Society of North American (RSNA).\\r\\n\\r\\nCrowds Cure Cancer (https:\\\/\\\/www.crowds-cure.org) first exhibited at RSNA 2017 utilizing CT scans from 4 different TCIA collections. Participants were asked to make a uni-dimensional measurement of the largest lesion. There were no options to provide details regarding imaging quality (e.g., no IV contrast, motion artifact, etc.), lesion location (e.g., lung, liver, etc.) or lesion characteristics (e.g., ill-defined, ground glass, etc.), requiring additional post-collection image review.\\u00a0 The Crowds-Cure-2017\\u00a0dataset can be found at DOI: 10.7937\\\/K9\\\/TCIA.2018.OW73VLO2.\\r\\n\\r\\nFor RSNA 2018, the application was re-designed to promote more comprehensive data collection and increased community participation. Participants were instructed to identify all metastatic disease and provide details regarding image quality, lesion location and characteristics. To provide additional incentives for participation, we improved the system by adding gamification features (e.g., reward badges), and created a leaderboard to display participant standings.\\u00a0 The amount of data being annotated was also significantly increased to include CT scans from 324 patients spanning 13 TCIA collections:\\u00a0\\u00a0Anti-PD-1_Lung,\\u00a0\\u00a0Anti-PD-1_MELANOMA, CPTAC-CCRCC, CPTAC-GBM, CPTAC-HNSCC, CPTAC-PDA, CPTAC-UCEC,\\u00a0\\u00a0NSCLC Radiogenomics,\\u00a0 TCGA-BLCA,\\u00a0 TCGA-COAD,\\u00a0 TCGA-HNSC,\\u00a0 TCGA-LUSC,\\u00a0 TCGA-UCEC.\\u00a0\\u00a0During RSNA 2018, 4756 bi-directional measurements were obtained compared to 2345 uni-dimensional measurements in 2017. Of the 4756 measurements, 65% of the lesions were annotated with location information. The data is being released in DICOM Structured Report and CSV formats for analysis by the community. The application is available on GitHub\\u00a0https:\\\/\\\/github.com\\\/crowds-cure\\\/cancer\\u00a0.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/crowds-cure-booth-scaled.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Bladder Endothelial Carcinoma\",\"Colon adenocarcinoma\",\"Glioblastoma Multiforme\",\"Head and Neck Squamous Cell Carcinoma\",\"Lung Squamous Cell Carcinoma\",\"Melanoma\",\"Non-small Cell Lung Cancer\",\"Pancreatic Ductal Adenocarcinoma\",\"Renal Clear Cell Carcinoma\",\"Uterine Corpus Endometrial Carcinoma\",\"Metastatic disease\"],\"cancer_locations\":[\"Bladder\",\"Brain\",\"Colon\",\"Head-Neck\",\"Kidney\",\"Lung\",\"Pancreas\",\"Skin\",\"Uterine corpus\"],\"data_types\":[\"Measurement\",\"SR\"],\"supporting_data\":[\"Lesion measurements\"],\"species\":[],\"subjects\":324,\"date_updated\":\"2019-05-30\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CrowdsCureCancer2018-Results.csv\",\"blob\":\"crowds cure cancer: data collected at the rsna 2018 annual meeting crowds-cure-2018 description\\r\\nthe cancer imaging archive (tcia) has procured substantial troves of image data, which could serve as valuable training sets for improving machine learning algorithms. however, these datasets lack consistent lesion annotations. to address this issue, the\\u00a0cancer imaging informatics lab\\u00a0at the frederick national laboratory for cancer research (fnlcr) formed a partnership with five groups funded by the\\u00a0national cancer institute's informatics technology for cancer research program\\u00a0to develop a web-based crowdsourcing application for gathering lesion annotations, featured at the annual meeting of the radiological society of north american (rsna).\\r\\n\\r\\ncrowds cure cancer (https:\\\/\\\/www.crowds-cure.org) first exhibited at rsna 2017 utilizing ct scans from 4 different tcia collections. participants were asked to make a uni-dimensional measurement of the largest lesion. there were no options to provide details regarding imaging quality (e.g., no iv contrast, motion artifact, etc.), lesion location (e.g., lung, liver, etc.) or lesion characteristics (e.g., ill-defined, ground glass, etc.), requiring additional post-collection image review.\\u00a0 the crowds-cure-2017\\u00a0dataset can be found at doi: 10.7937\\\/k9\\\/tcia.2018.ow73vlo2.\\r\\n\\r\\nfor rsna 2018, the application was re-designed to promote more comprehensive data collection and increased community participation. participants were instructed to identify all metastatic disease and provide details regarding image quality, lesion location and characteristics. to provide additional incentives for participation, we improved the system by adding gamification features (e.g., reward badges), and created a leaderboard to display participant standings.\\u00a0 the amount of data being annotated was also significantly increased to include ct scans from 324 patients spanning 13 tcia collections:\\u00a0\\u00a0anti-pd-1_lung,\\u00a0\\u00a0anti-pd-1_melanoma, cptac-ccrcc, cptac-gbm, cptac-hnscc, cptac-pda, cptac-ucec,\\u00a0\\u00a0nsclc radiogenomics,\\u00a0 tcga-blca,\\u00a0 tcga-coad,\\u00a0 tcga-hnsc,\\u00a0 tcga-lusc,\\u00a0 tcga-ucec.\\u00a0\\u00a0during rsna 2018, 4756 bi-directional measurements were obtained compared to 2345 uni-dimensional measurements in 2017. of the 4756 measurements, 65% of the lesions were annotated with location information. the data is being released in dicom structured report and csv formats for analysis by the community. the application is available on github\\u00a0https:\\\/\\\/github.com\\\/crowds-cure\\\/cancer\\u00a0. bladder endothelial carcinoma colon adenocarcinoma glioblastoma multiforme head and neck squamous cell carcinoma lung squamous cell carcinoma melanoma non-small cell lung cancer pancreatic ductal adenocarcinoma renal clear cell carcinoma uterine corpus endometrial carcinoma metastatic disease bladder brain colon head-neck kidney lung pancreas skin uterine corpus measurement sr lesion measurements community\"},{\"id\":45937,\"type\":\"analysis\",\"slug\":\"di-cubed-reports\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/di-cubed-reports\\\/\",\"title\":\"SDTM datasets of clinical data and measurements for selected cancer collections to TCIA\",\"short_title\":\"DI-Cubed-Reports\",\"doi\":\"10.7937\\\/TCIA.2019.zfv154m9\",\"summary\":\"The Data Integration & Imaging Informatics (DI-Cubed) project explored the issue of lack of standardized data capture at the point of data creation, as reflected in the non-image data accompanying 4 TCIA breast cancer collections (Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1\\\/ACRIN 6657 trials (ISPY1),\\u00a0BREAST-DIAGNOSIS,\\u00a0Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy (Breast-MRI-NACT-Pilot), The Cancer Genome Atlas Breast Invasive Carcinoma Collection (TCGA-BRCA))\\u00a0and the\\u00a0Ivy Glioblastoma Atlas Project (IvyGAP) brain cancer collection. The work addressed the desire for semantic interoperability between various NCI initiatives by aligning on common clinical metadata elements and supporting use cases that connect clinical, imaging, and genomics data. Accordingly, clinical and measurement data imported into I2B2 were cross-mapped to industry standard concepts for names and values including those derived from BRIDG, CDISC SDTM, DICOM Structured Reporting models and using NCI Thesaurus, SNOMED CT and LOINC controlled terminology.\\r\\n\\r\\nA subset of the standardized data was then exported from I2B2 in SDTM compliant SAS transport files. \\u00a0The SDTM data was derived from data taken from both the curated TCIA spreadsheets as well as tumor measurements and dates from the TCIA Restful API. \\u00a0Due to the nature of the available data\\u00a0not all SDTM conformance rules were applicable or adhered to.\\r\\n\\r\\nThese Study Data Tabulation Model format (SDTM) datasets were validated using Pinnacle 21 CDISC validation software. The validation software reviews datasets according to their degree of conformance to rules developed for the purposes of FDA submissions of electronic data. \\u00a0Iterative refinements were made to the datasets based upon group discussions and feedback from the validation tool.\\r\\n\\r\\nExport datasets for the following SDTM domains were generated:\\r\\n\\r\\n \\tDM (Demographics)\\r\\n \\tDS (Disposition)\\r\\n \\tMI (Microscopic Findings)\\r\\n \\tPR (Procedures)\\r\\n \\tSS (Subject Status)\\r\\n \\tTU (Tumor\\\/Lesion Identification)\\r\\n \\tTR (Tumor\\\/Lesion Results)\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Breast\",\"Glioblastoma\"],\"cancer_locations\":[\"Breast\",\"Brain\"],\"data_types\":[\"Other\"],\"supporting_data\":[\"Standardized (SDTM format) conversions of clinical and image analysis data\"],\"species\":[],\"subjects\":516,\"date_updated\":\"2019-06-21\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/dicubed_sdtm_xpt_2019-05-30.zip\",\"blob\":\"sdtm datasets of clinical data and measurements for selected cancer collections to tcia di-cubed-reports the data integration & imaging informatics (di-cubed) project explored the issue of lack of standardized data capture at the point of data creation, as reflected in the non-image data accompanying 4 tcia breast cancer collections (multi-center breast dce-mri data and segmentations from patients in the i-spy 1\\\/acrin 6657 trials (ispy1),\\u00a0breast-diagnosis,\\u00a0single site breast dce-mri data and segmentations from patients undergoing neoadjuvant chemotherapy (breast-mri-nact-pilot), the cancer genome atlas breast invasive carcinoma collection (tcga-brca))\\u00a0and the\\u00a0ivy glioblastoma atlas project (ivygap) brain cancer collection. the work addressed the desire for semantic interoperability between various nci initiatives by aligning on common clinical metadata elements and supporting use cases that connect clinical, imaging, and genomics data. accordingly, clinical and measurement data imported into i2b2 were cross-mapped to industry standard concepts for names and values including those derived from bridg, cdisc sdtm, dicom structured reporting models and using nci thesaurus, snomed ct and loinc controlled terminology.\\r\\n\\r\\na subset of the standardized data was then exported from i2b2 in sdtm compliant sas transport files. \\u00a0the sdtm data was derived from data taken from both the curated tcia spreadsheets as well as tumor measurements and dates from the tcia restful api. \\u00a0due to the nature of the available data\\u00a0not all sdtm conformance rules were applicable or adhered to.\\r\\n\\r\\nthese study data tabulation model format (sdtm) datasets were validated using pinnacle 21 cdisc validation software. the validation software reviews datasets according to their degree of conformance to rules developed for the purposes of fda submissions of electronic data. \\u00a0iterative refinements were made to the datasets based upon group discussions and feedback from the validation tool.\\r\\n\\r\\nexport datasets for the following sdtm domains were generated:\\r\\n\\r\\n \\tdm (demographics)\\r\\n \\tds (disposition)\\r\\n \\tmi (microscopic findings)\\r\\n \\tpr (procedures)\\r\\n \\tss (subject status)\\r\\n \\ttu (tumor\\\/lesion identification)\\r\\n \\ttr (tumor\\\/lesion results) breast glioblastoma breast brain other standardized (sdtm format) conversions of clinical and image analysis data community\"},{\"id\":45847,\"type\":\"analysis\",\"slug\":\"dicom-glioma-seg\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/dicom-glioma-seg\\\/\",\"title\":\"DICOM-SEG Conversions for TCGA-LGG and TCGA-GBM Segmentation Datasets\",\"short_title\":\"DICOM-Glioma-SEG\",\"doi\":\"10.7937\\\/TCIA.2018.ow6ce3ml\",\"summary\":\"This dataset contains DICOM-SEG (DSO) conversions of the\\u00a0  Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection \\u00a0and\\u00a0  Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-GBM collection\\u00a0analysis datasets.\\r\\n\\r\\nThe MR volumes and segmentations provided in the original segmentation datasets (T1 pre-contrast, T1 post-contrast, T2, FLAIR) are in NIfTI format, co-registered to an atlas space, and re-sampled to 1mm isotropic resolution. This dataset contains DICOM-SEG versions of the same segmentations, transformed back into the original patient resolutions and orientations found in the TCIA\\u2019s\\u00a0TCGA-GBM and\\u00a0TCGA-LGG datasets. This allows users to extract features from MR sequences without introducing interpolation artifacts from isotropic resampling.\\r\\n\\r\\nThe process for creating these DSO objects is as follows. Patient data from the original NIfTI datasets were registered and resampled from isotropic space to patient space and resolution using\\u00a0  3DSlicer\\u2019s BRAINSFit module  . The affine transformation files from these registrations are used to register and resample both the semi-automatic and automatic NIfTI segmentations into the spaces of each original MR DICOM dataset. These transformed NIfTI segmentations are then converted into DICOM-SEG datasets using the software package\\u00a0  dcmqi  . Because each MR sequence has a unique patient space and resolution, the resulting dataset contains four DSO segmentations for each original NIfTI segmentation.\\r\\n\\r\\nIncluded in this dataset are the converted DSO volumes, DSO metadata values used in the DSO conversion program\\u00a0dcmqi, and affine transformation files from isotropic space to the original patient space saved in ITK format. Original patient DICOM volumes are also available for download below. A key is provided that maps individual DSO objects to their corresponding DICOM Series UID, to facilitate easier data analysis.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Glioblastoma\",\"Low Grade Glioma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"SEG\",\"Other\"],\"supporting_data\":[\"Tumor segmentations\"],\"species\":[],\"subjects\":167,\"date_updated\":\"2020-04-30\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&DateRange=04\\\/10\\\/2020-04\\\/25\\\/2020&CollectionCriteria=TCGA-LGG,TCGA-GBM&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/DICOM-GLIOMA-SEG_20240410.tcia\",\"blob\":\"dicom-seg conversions for tcga-lgg and tcga-gbm segmentation datasets dicom-glioma-seg this dataset contains dicom-seg (dso) conversions of the\\u00a0  segmentation labels and radiomic features for the pre-operative scans of the tcga-lgg collection \\u00a0and\\u00a0  segmentation labels and radiomic features for the pre-operative scans of the tcga-gbm collection\\u00a0analysis datasets.\\r\\n\\r\\nthe mr volumes and segmentations provided in the original segmentation datasets (t1 pre-contrast, t1 post-contrast, t2, flair) are in nifti format, co-registered to an atlas space, and re-sampled to 1mm isotropic resolution. this dataset contains dicom-seg versions of the same segmentations, transformed back into the original patient resolutions and orientations found in the tcia\\u2019s\\u00a0tcga-gbm and\\u00a0tcga-lgg datasets. this allows users to extract features from mr sequences without introducing interpolation artifacts from isotropic resampling.\\r\\n\\r\\nthe process for creating these dso objects is as follows. patient data from the original nifti datasets were registered and resampled from isotropic space to patient space and resolution using\\u00a0  3dslicer\\u2019s brainsfit module  . the affine transformation files from these registrations are used to register and resample both the semi-automatic and automatic nifti segmentations into the spaces of each original mr dicom dataset. these transformed nifti segmentations are then converted into dicom-seg datasets using the software package\\u00a0  dcmqi  . because each mr sequence has a unique patient space and resolution, the resulting dataset contains four dso segmentations for each original nifti segmentation.\\r\\n\\r\\nincluded in this dataset are the converted dso volumes, dso metadata values used in the dso conversion program\\u00a0dcmqi, and affine transformation files from isotropic space to the original patient space saved in itk format. original patient dicom volumes are also available for download below. a key is provided that maps individual dso objects to their corresponding dicom series uid, to facilitate easier data analysis. glioblastoma low grade glioma brain seg other tumor segmentations community\"},{\"id\":45865,\"type\":\"analysis\",\"slug\":\"dicom-lidc-idri-nodules\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/dicom-lidc-idri-nodules\\\/\",\"title\":\"Standardized representation of the TCIA LIDC-IDRI annotations using DICOM\",\"short_title\":\"DICOM-LIDC-IDRI-Nodules\",\"doi\":\"10.7937\\\/TCIA.2018.h7umfurq\",\"summary\":\"This dataset contains standardized DICOM representation of the annotations and characterizations collected by the LIDC\\\/IDRI initiative, originally stored in XML and available in the TCIA LIDC-IDRI\\u00a0collection . Only the nodules that were deemed to be greater or equal to 3 mm in the largest planar dimensions have been annotated and characterized by the expert radiologists performing the annotations. Only those nodules are included in the present dataset.\\r\\n\\r\\nConversion was enabled by the\\u00a0pylidc\\u00a0library (https:\\\/\\\/pylidc.github.io\\\/) (parsing of XML, volumetric reconstruction of the nodule annotations, clustering of the annotations belonging to the same nodule, calculation of the volume, surface area and largest diameter of the nodules) and the\\u00a0dcmqi\\u00a0library (https:\\\/\\\/github.com\\\/qiicr\\\/dcmqi) (storing of the annotations into DICOM Segmentation objects, and storing of the characterizations and measurements into DICOM Structured Reporting objects). The script used for the conversion is available at\\u00a0https:\\\/\\\/github.com\\\/qiicr\\\/lidc2dicom. The details on the process of the conversion and the usage of the resulting objects are available in the citation (see Citations & Data Usage Policy section).\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Lung\"],\"cancer_locations\":[\"Chest\"],\"data_types\":[\"SEG\",\"SR\",\"Other\"],\"supporting_data\":[\"Tumor segmentations\",\"image features\",\"Software\\\/Source Code\"],\"species\":[],\"subjects\":875,\"date_updated\":\"2020-03-26\",\"status\":\"\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/LIDC-IDRI-StandardizedRepresentation-March2020-manifest.tcia\",\"blob\":\"standardized representation of the tcia lidc-idri annotations using dicom dicom-lidc-idri-nodules this dataset contains standardized dicom representation of the annotations and characterizations collected by the lidc\\\/idri initiative, originally stored in xml and available in the tcia lidc-idri\\u00a0collection . only the nodules that were deemed to be greater or equal to 3 mm in the largest planar dimensions have been annotated and characterized by the expert radiologists performing the annotations. only those nodules are included in the present dataset.\\r\\n\\r\\nconversion was enabled by the\\u00a0pylidc\\u00a0library (https:\\\/\\\/pylidc.github.io\\\/) (parsing of xml, volumetric reconstruction of the nodule annotations, clustering of the annotations belonging to the same nodule, calculation of the volume, surface area and largest diameter of the nodules) and the\\u00a0dcmqi\\u00a0library (https:\\\/\\\/github.com\\\/qiicr\\\/dcmqi) (storing of the annotations into dicom segmentation objects, and storing of the characterizations and measurements into dicom structured reporting objects). the script used for the conversion is available at\\u00a0https:\\\/\\\/github.com\\\/qiicr\\\/lidc2dicom. the details on the process of the conversion and the usage of the resulting objects are available in the citation (see citations & data usage policy section). lung chest seg sr other tumor segmentations image features software\\\/source code community\"},{\"id\":45915,\"type\":\"analysis\",\"slug\":\"dicom-sr-breast-clinical\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/dicom-sr-breast-clinical\\\/\",\"title\":\"DICOM SR of clinical data and measurement for breast cancer collections to TCIA\",\"short_title\":\"DICOM-SR-Breast-Clinical\",\"doi\":\"10.7937\\\/TCIA.2019.wgllssg1\",\"summary\":\"The Data Integration & Imaging Informatics (DI-Cubed) project explored the issue of lack of standardized data capture at the point of data creation, as reflected in the non-image data accompanying various TCIA breast cancer collections. The work addressed the desire for semantic interoperability between various NCI initiatives by aligning on common clinical metadata elements and supporting use cases that connect clinical, imaging, and genomics data. Accordingly, clinical and measurement data was imported into I2B2 and cross-mapped to industry standard concepts for names and values including those derived from BRIDG, CDISC SDTM, DICOM Structured Reporting models and using NCI Thesaurus, SNOMED CT and LOINC controlled terminology.\\r\\n\\r\\nA subset of the standardized data was then exported from I2B2 to CSV and thence converted to DICOM SR according to the the DICOM Breast Imaging Report template\\u00a0  [1] , which supports description of patient characteristics, histopathology, receptor status and clinical findings including measurements. The purpose was not to advocate DICOM SR as an appropriate format for interchange or storage of such information for query purposes, but rather to demonstrate that use of standard concepts harmonized across multiple collections could be transformed into an existing standard report representation. The DICOM SR can be stored and used together with the images in repositories such as TCIA and in image viewers that support rendering of DICOM SR content.\\r\\n\\r\\nDuring the project, various deficiencies in the DICOM Breast Imaging Report template were identified with respect to describing breast MR studies, laterality of findings versus procedures, more recently developed receptor types, and patient characteristics and status. These were addressed via DICOM CP 1838, finalized in Jan 2019, and this subset reflects those changes.\\r\\n\\r\\nDICOM Breast Imaging Report Templates available from: http:\\\/\\\/dicom.nema.org\\\/medical\\\/dicom\\\/current\\\/output\\\/chtml\\\/part16\\\/sect_BreastImagingReportTemplates.html\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"SR\"],\"supporting_data\":[\"DICOM SR descriptions of patient characteristics\",\"Histopathology\",\"receptor status and clinical findings including measurements.\"],\"species\":[],\"subjects\":474,\"date_updated\":\"2020-05-26\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?ImageModalityCriteria=SR&MinNumberOfStudiesCriteria=1&CollectionCriteria=BREAST-DIAGNOSIS,Breast-MRI-NACT-Pilot,ISPY1,TCGA-BRCA&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/SR_wgllssg1.tcia\",\"blob\":\"dicom sr of clinical data and measurement for breast cancer collections to tcia dicom-sr-breast-clinical the data integration & imaging informatics (di-cubed) project explored the issue of lack of standardized data capture at the point of data creation, as reflected in the non-image data accompanying various tcia breast cancer collections. the work addressed the desire for semantic interoperability between various nci initiatives by aligning on common clinical metadata elements and supporting use cases that connect clinical, imaging, and genomics data. accordingly, clinical and measurement data was imported into i2b2 and cross-mapped to industry standard concepts for names and values including those derived from bridg, cdisc sdtm, dicom structured reporting models and using nci thesaurus, snomed ct and loinc controlled terminology.\\r\\n\\r\\na subset of the standardized data was then exported from i2b2 to csv and thence converted to dicom sr according to the the dicom breast imaging report template\\u00a0  [1] , which supports description of patient characteristics, histopathology, receptor status and clinical findings including measurements. the purpose was not to advocate dicom sr as an appropriate format for interchange or storage of such information for query purposes, but rather to demonstrate that use of standard concepts harmonized across multiple collections could be transformed into an existing standard report representation. the dicom sr can be stored and used together with the images in repositories such as tcia and in image viewers that support rendering of dicom sr content.\\r\\n\\r\\nduring the project, various deficiencies in the dicom breast imaging report template were identified with respect to describing breast mr studies, laterality of findings versus procedures, more recently developed receptor types, and patient characteristics and status. these were addressed via dicom cp 1838, finalized in jan 2019, and this subset reflects those changes.\\r\\n\\r\\ndicom breast imaging report templates available from: http:\\\/\\\/dicom.nema.org\\\/medical\\\/dicom\\\/current\\\/output\\\/chtml\\\/part16\\\/sect_breastimagingreporttemplates.html breast cancer breast sr dicom sr descriptions of patient characteristics histopathology receptor status and clinical findings including measurements. community\"},{\"id\":45543,\"type\":\"analysis\",\"slug\":\"gbm-mr-ner-outcomes\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/gbm-mr-ner-outcomes\\\/\",\"title\":\"Outcome Prediction in Patients with Glioblastoma by Using Imaging, Clinical, and Genomic Biomarkers: Focus on the Nonenhancing Component of the Tumor\",\"short_title\":\"GBM-MR-NER-Outcomes\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2014.FAB7YRPZ\",\"summary\":\"This manuscript correlates patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (NER) of glioblastoma (GBM), along with clinical and genomic markers. Forty-five patients with GBM underwent baseline imaging with contrast material-enhanced magnetic resonance (MR) imaging and dynamic susceptibility contrast-enhanced T2*-weighted perfusion MR imaging.\\u00a0 See DSC T2* MR Perfusion Analysis for more information about the authors' perfusion analysis.\\u00a0 Molecular and clinical predictors of survival were obtained. Single and multivariable models of overall survival (OS) and progression-free survival (PFS) were explored with Kaplan-Meier estimates, Cox regression, and random survival forests.\\r\\n\\r\\nWorsening OS (log-rank test, P = .0103) and PFS (log-rank test, P = .0223) were associated with increasing relative cerebral blood volume of NER (rCBV NER ), which was higher with deep white matter involvement (t test, P = .0482) and poor NER margin definition (t test, P = .0147). NER crossing the midline was the only morphologic feature of NER associated with poor survival (log-rank test, P = .0125). Preoperative Karnofsky performance score (KPS) and resection extent (n = 30) were clinically significant OS predictors (log-rank test, P = .0176 and P = .0038, respectively). No genomic alterations were associated with survival, except patients with high rCBV NER \\u00a0and wild-type epidermal growth factor receptor (EGFR) mutation had significantly poor survival (log-rank test, P = .0306; area under the receiver operating characteristic curve = 0.62). Combining resection extent with rCBV NER \\u00a0marginally improved prognostic ability (permutation, P = .084). Random forest models of presurgical predictors indicated rCBV NER \\u00a0as the top predictor; also important were KPS, age at diagnosis, and NER crossing the midline. A multivariable model containing rCBV NER , age at diagnosis, and KPS can be used to group patients with more than 1 year of difference in observed median survival (0.49-1.79 years). Conclusion Patients with high rCBV NER \\u00a0and NER crossing the midline and those with high rCBV NER \\u00a0and wild-type EGFR mutation showed poor survival. In multivariable survival models, however, rCBV NER \\u00a0provided unique prognostic information that went above and beyond the assessment of all NER imaging features, as well as clinical and genomic features.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Glioblastoma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"Measurement\",\"Demographic\",\"Follow-Up\",\"Molecular Test\"],\"supporting_data\":[],\"species\":[],\"subjects\":45,\"date_updated\":\"2014-07-24\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=TCGA-GBM\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/manifest-nordicICE.tcia\",\"blob\":\"outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor gbm-mr-ner-outcomes this manuscript correlates patient survival with morphologic imaging features and hemodynamic parameters obtained from the nonenhancing region (ner) of glioblastoma (gbm), along with clinical and genomic markers. forty-five patients with gbm underwent baseline imaging with contrast material-enhanced magnetic resonance (mr) imaging and dynamic susceptibility contrast-enhanced t2*-weighted perfusion mr imaging.\\u00a0 see dsc t2* mr perfusion analysis for more information about the authors' perfusion analysis.\\u00a0 molecular and clinical predictors of survival were obtained. single and multivariable models of overall survival (os) and progression-free survival (pfs) were explored with kaplan-meier estimates, cox regression, and random survival forests.\\r\\n\\r\\nworsening os (log-rank test, p = .0103) and pfs (log-rank test, p = .0223) were associated with increasing relative cerebral blood volume of ner (rcbv ner ), which was higher with deep white matter involvement (t test, p = .0482) and poor ner margin definition (t test, p = .0147). ner crossing the midline was the only morphologic feature of ner associated with poor survival (log-rank test, p = .0125). preoperative karnofsky performance score (kps) and resection extent (n = 30) were clinically significant os predictors (log-rank test, p = .0176 and p = .0038, respectively). no genomic alterations were associated with survival, except patients with high rcbv ner \\u00a0and wild-type epidermal growth factor receptor (egfr) mutation had significantly poor survival (log-rank test, p = .0306; area under the receiver operating characteristic curve = 0.62). combining resection extent with rcbv ner \\u00a0marginally improved prognostic ability (permutation, p = .084). random forest models of presurgical predictors indicated rcbv ner \\u00a0as the top predictor; also important were kps, age at diagnosis, and ner crossing the midline. a multivariable model containing rcbv ner , age at diagnosis, and kps can be used to group patients with more than 1 year of difference in observed median survival (0.49-1.79 years). conclusion patients with high rcbv ner \\u00a0and ner crossing the midline and those with high rcbv ner \\u00a0and wild-type egfr mutation showed poor survival. in multivariable survival models, however, rcbv ner \\u00a0provided unique prognostic information that went above and beyond the assessment of all ner imaging features, as well as clinical and genomic features. glioblastoma brain mr measurement demographic follow-up molecular test tcga\"},{\"id\":45675,\"type\":\"analysis\",\"slug\":\"isbi-mr-prostate-2013\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/isbi-mr-prostate-2013\\\/\",\"title\":\"NCI-ISBI 2013 Challenge: Automated Segmentation of Prostate Structures\",\"short_title\":\"ISBI-MR-Prostate-2013\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2015.zF0vlOPv\",\"summary\":\"This data set was created for use in the\\u00a0NCI-ISBI 2013 Challenge - Automated Segmentation of Prostate Structures. \\u00a0The challenge data set was divided into 3 parts including training, leaderboard and test data sets. \\u00a0This allowed participants to prepare their algorithms and test their results prior to submitting to a final test phase for selecting the winner.\\r\\n\\r\\nImage data were selected from\\u00a0PROSTATE-DIAGNOSIS\\u00a0and\\u00a0Prostate-3T\\u00a0collections on TCIA. \\u00a0Cases consist of axial scans with half\\u00a0obtained at 1.5 T (Philips Achieva) with an endo-rectal receiver coil (fromBostonMedicalCenter) and the other half at 3T (Siemens TIM) with a surface coil (from Radboud University Nijmegen Medical Centre [RUNMC],Netherlands). They were acquired as T2-weighted MR axial pulse sequences with\\u00a0either 4 mm thick slices at 3T or 3 mm thick at 1.5T. Each file contains nearly all DICOM acquisition parameters except tags that specifically identify Private Health Information.\\r\\n\\r\\nEach case has had central gland (CG) and peripheral zone (PZ) outlines marked by Drs. Nicolas Bloch (Boston University School of Medicine) and Mirabela Rusu (Case Western University) or Drs. Henkjan Huisman, Geert Litjens, or Jurgen Futterer at RUNMC Netherlands.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Prostate\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"Segmentation\",\"MR\"],\"supporting_data\":[\"Prostate structure segmentations\"],\"species\":[\"Human\"],\"subjects\":80,\"date_updated\":\"2015-08-20\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NCI-ISBI-2013-Prostate-Challenge-Training.zip\",\"blob\":\"nci-isbi 2013 challenge: automated segmentation of prostate structures isbi-mr-prostate-2013 this data set was created for use in the\\u00a0nci-isbi 2013 challenge - automated segmentation of prostate structures. \\u00a0the challenge data set was divided into 3 parts including training, leaderboard and test data sets. \\u00a0this allowed participants to prepare their algorithms and test their results prior to submitting to a final test phase for selecting the winner.\\r\\n\\r\\nimage data were selected from\\u00a0prostate-diagnosis\\u00a0and\\u00a0prostate-3t\\u00a0collections on tcia. \\u00a0cases consist of axial scans with half\\u00a0obtained at 1.5 t (philips achieva) with an endo-rectal receiver coil (frombostonmedicalcenter) and the other half at 3t (siemens tim) with a surface coil (from radboud university nijmegen medical centre [runmc],netherlands). they were acquired as t2-weighted mr axial pulse sequences with\\u00a0either 4 mm thick slices at 3t or 3 mm thick at 1.5t. each file contains nearly all dicom acquisition parameters except tags that specifically identify private health information.\\r\\n\\r\\neach case has had central gland (cg) and peripheral zone (pz) outlines marked by drs. nicolas bloch (boston university school of medicine) and mirabela rusu (case western university) or drs. henkjan huisman, geert litjens, or jurgen futterer at runmc netherlands. prostate prostate segmentation mr prostate structure segmentations community\"},{\"id\":46167,\"type\":\"analysis\",\"slug\":\"ispy1-tumor-seg-radiomics\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/ispy1-tumor-seg-radiomics\\\/\",\"title\":\"Expert tumor annotations and radiomic features for the ISPY1\\\/ACRIN 6657 trial data collection\",\"short_title\":\"ISPY1-Tumor-SEG-Radiomics\",\"doi\":\"10.7937\\\/TCIA.XC7A-QT20\",\"summary\":\"This dataset enhances the ISPY1 data collection, with uniformly curated data, tumor annotations, and quantitative imaging features. This dataset includes a) uniformly processed scans that are harmonized to match the intensity and spatial characteristics, facilitating immediate use in computational studies, b) computationally-generated and manually-revised expert annotations of tumor regions, as well as c) a comprehensive set of quantitative imaging (also known as radiomic) features corresponding to the tumor regions.\\r\\n\\r\\nThe segmentations for the ISPY1\\\/ACRIN 6657 dataset currently hosted on TCIA\\u2019s website describe a) the tumor volume of interest (VOI) and b) functional tumor volume (FTV).\\r\\n\\r\\n \\tThe provided tumor VOI is a 3D rectangular box enclosing the enhancing tumor region, while including peritumoral tissue. The VOI provides a general guideline of where the tumor is located within patient anatomy, but it does not delineate tumor boundaries or shape.\\r\\n \\tThe FTV segmentations describe only enhancing voxels in the tumor, i.e., defined by peak enhancement or signal enhancement ratio criteria.\\r\\n\\r\\nThese currently provided segmentations do not include non-enhancing portions of the tumor volume, which represent a significant portion of the disease burden that needs to be studied to better understand and quantify the disease.\\r\\n\\r\\nThe\\u00a0segmentations in these new analysis results are for\\u00a0the entire 3D primary lesion, including both the enhancing and the non-enhancing tumor regions, therefore defining the structural tumor volume (STV). These STV annotations were generated by manually delineating the primary lesion volume, after confirming the location of the primary lesion from the provided VOI and FTV. The STV annotations were reviewed and approved by a board-certified, fellowship-trained breast radiologist, and are statistically significantly different from FTV.\\r\\n\\r\\nWe believe these STV annotations will allow analyses of the entire disease burden and analyses of tumor heterogeneity regarding contrast uptake, contributing to further expanding our mechanistic understanding of the disease potentially leading to improved patient management.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image2021-11-18_14-53-19.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Breast\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"MR\",\"Segmentation\",\"Radiomic Feature\",\"Other\"],\"supporting_data\":[\"Tumor segmentations\",\"radiomic features\"],\"species\":[\"Human\"],\"subjects\":163,\"date_updated\":\"2022-06-01\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?ImageModalityCriteria=MR&MinNumberOfStudiesCriteria=1&PatientCriteria=ISPY1_1002,ISPY1_1003,ISPY1_1004,ISPY1_1005,ISPY1_1007,ISPY1_1008,ISPY1_1009,ISPY1_1010,ISPY1_1011,ISPY1_1012,ISPY1_1013,ISPY1_1015,ISPY1_1016,ISPY1_1017,ISPY1_1021,ISPY1_1025,ISPY1_1029,ISPY1_1030,ISPY1_1031,ISPY1_1032,ISPY1_1034,ISPY1_1035,ISPY1_1036,ISPY1_1037,ISPY1_1038,ISPY1_1041,ISPY1_1042,ISPY1_1049,ISPY1_1051,ISPY1_1053,ISPY1_1056,ISPY1_1058,ISPY1_1060,ISPY1_1061,ISPY1_1062,ISPY1_1064,ISPY1_1065,ISPY1_1066,ISPY1_1069,ISPY1_1070,ISPY1_1072,ISPY1_1073,ISPY1_1074,ISPY1_1075,ISPY1_1077,ISPY1_1078,ISPY1_1082,ISPY1_1083,ISPY1_1084,ISPY1_1087,ISPY1_1088,ISPY1_1089,ISPY1_1090,ISPY1_1094,ISPY1_1096,ISPY1_1097,ISPY1_1098,ISPY1_1099,ISPY1_1100,ISPY1_1101,ISPY1_1102,ISPY1_1104,ISPY1_1106,ISPY1_1107,ISPY1_1112,ISPY1_1114,ISPY1_1117,ISPY1_1120,ISPY1_1122,ISPY1_1123,ISPY1_1124,ISPY1_1125,ISPY1_1127,ISPY1_1128,ISPY1_1129,ISPY1_1130,ISPY1_1132,ISPY1_1134,ISPY1_1135,ISPY1_1136,ISPY1_1137,ISPY1_1140,ISPY1_1141,ISPY1_1142,ISPY1_1144,ISPY1_1145,ISPY1_1146,ISPY1_1147,ISPY1_1148,ISPY1_1149,ISPY1_1150,ISPY1_1151,ISPY1_1152,ISPY1_1155,ISPY1_1156,ISPY1_1157,ISPY1_1158,ISPY1_1159,ISPY1_1160,ISPY1_1161,ISPY1_1163,ISPY1_1164,ISPY1_1165,ISPY1_1166,ISPY1_1167,ISPY1_1168,ISPY1_1171,ISPY1_1172,ISPY1_1173,ISPY1_1174,ISPY1_1175,ISPY1_1176,ISPY1_1177,ISPY1_1179,ISPY1_1180,ISPY1_1181,ISPY1_1182,ISPY1_1183,ISPY1_1184,ISPY1_1185,ISPY1_1187,ISPY1_1188,ISPY1_1189,ISPY1_1191,ISPY1_1192,ISPY1_1193,ISPY1_1196,ISPY1_1197,ISPY1_1198,ISPY1_1199,ISPY1_1201,ISPY1_1202,ISPY1_1203,ISPY1_1204,ISPY1_1205,ISPY1_1207,ISPY1_1208,ISPY1_1209,ISPY1_1210,ISPY1_1212,ISPY1_1216,ISPY1_1217,ISPY1_1218,ISPY1_1219,ISPY1_1220,ISPY1_1221,ISPY1_1222,ISPY1_1223,ISPY1_1224,ISPY1_1225,ISPY1_1226,ISPY1_1227,ISPY1_1228,ISPY1_1229,ISPY1_1230,ISPY1_1231,ISPY1_1233,ISPY1_1234,ISPY1_1235,ISPY1_1236,ISPY1_1237,ISPY1_1238,ISPY1_1239&CollectionCriteria=ISPY1&ThirdPartyCriteria=NO\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjY5OSIsInBhc3Njb2RlIjoiZGQ3ZTk1OWYwNzhmZGQ4NzVjMTFiNDEwNjJlMjA0NDcwNmU2NmQyMSIsInBhY2thZ2VfaWQiOiI2OTkiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"expert tumor annotations and radiomic features for the ispy1\\\/acrin 6657 trial data collection ispy1-tumor-seg-radiomics this dataset enhances the ispy1 data collection, with uniformly curated data, tumor annotations, and quantitative imaging features. this dataset includes a) uniformly processed scans that are harmonized to match the intensity and spatial characteristics, facilitating immediate use in computational studies, b) computationally-generated and manually-revised expert annotations of tumor regions, as well as c) a comprehensive set of quantitative imaging (also known as radiomic) features corresponding to the tumor regions.\\r\\n\\r\\nthe segmentations for the ispy1\\\/acrin 6657 dataset currently hosted on tcia\\u2019s website describe a) the tumor volume of interest (voi) and b) functional tumor volume (ftv).\\r\\n\\r\\n \\tthe provided tumor voi is a 3d rectangular box enclosing the enhancing tumor region, while including peritumoral tissue. the voi provides a general guideline of where the tumor is located within patient anatomy, but it does not delineate tumor boundaries or shape.\\r\\n \\tthe ftv segmentations describe only enhancing voxels in the tumor, i.e., defined by peak enhancement or signal enhancement ratio criteria.\\r\\n\\r\\nthese currently provided segmentations do not include non-enhancing portions of the tumor volume, which represent a significant portion of the disease burden that needs to be studied to better understand and quantify the disease.\\r\\n\\r\\nthe\\u00a0segmentations in these new analysis results are for\\u00a0the entire 3d primary lesion, including both the enhancing and the non-enhancing tumor regions, therefore defining the structural tumor volume (stv). these stv annotations were generated by manually delineating the primary lesion volume, after confirming the location of the primary lesion from the provided voi and ftv. the stv annotations were reviewed and approved by a board-certified, fellowship-trained breast radiologist, and are statistically significantly different from ftv.\\r\\n\\r\\nwe believe these stv annotations will allow analyses of the entire disease burden and analyses of tumor heterogeneity regarding contrast uptake, contributing to further expanding our mechanistic understanding of the disease potentially leading to improved patient management. breast breast mr segmentation radiomic feature other tumor segmentations radiomic features community\"},{\"id\":46107,\"type\":\"analysis\",\"slug\":\"ivygap-radiomics\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/ivygap-radiomics\\\/\",\"title\":\"Multi-Institutional Paired Expert Segmentations and Radiomic Features of the Ivy GAP Dataset\",\"short_title\":\"IvyGAP-Radiomics\",\"doi\":\"10.7937\\\/9j41-7d44\",\"summary\":\"This dataset comprises two paired sets of expert segmentation labels for tumor sub-compartments of the pre-operative multi-institutional scans of the\\u00a0Ivy Glioblastoma Atlas Project (Ivy GAP) collection of The Cancer Imaging Archive (TCIA). These labels have been approved by independent expert board-certified neuroradiologists at the Hospital of the University of Pennsylvania and at Case Western Reserve University. Furthermore, for each of the paired sets of approved labels, a diverse comprehensive panel of radiomic features is provided, along with their corresponding skull-stripped and co-registered multi-parametric magnetic resonance imaging (mpMRI) volumes (i.e. native (T1) and post-contrast T1-weighted (T1-Gd), T2, T2-FLAIR), in NIfTI format. The pre-operative mpMRI scans were identified in the Ivy GAP collection via radiological assessment. These scans were initially skull-stripped and co-registered to a common anatomical atlas (provided within this dataset), before their tumor segmentation labels were produced following a consistent annotation protocol across the two institutions. The final labels were used to extract a rich panel of radiomic features through\\u00a0the Cancer Imaging Phenomics Toolkit (CaPTk), comprising intensity, volumetric, morphologic, histogram-based, and textural parameters compliant with\\u00a0the Image Biomarker Standardisation Initiative (IBSI), as well as through a\\u00a03D Slicer extension for the novel CoLlAGe feature family. Radiomic features robust to variability in segmentations were then identified following a statistical robustness analysis.\\r\\n\\r\\nThe approved expert segmentation labels should enable quantitative computational and clinical studies without the need to repeat manual annotations, whilst allowing for comparison across studies. They can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational competitions, such as the\\u00a0International Brain Tumor Segmentation (BraTS) challenge. The provided panel of robust radiomic features may facilitate research integrative of the molecular characterization offered by the Allen Institute, and hence allow associations with molecular markers (radiogenomics), clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features. The complete reproducibility analysis can be found in the associated publication citation found in the \\u201cCitations & Data Usage Policy\\u201d.\\r\\n\\r\\nSpecifically, the released data comprises of 1) the available expert segmentation labels of the various tumor sub-compartments performed at each institution (i.e. 34 subjects segmented at UPenn, 34 subjects segmented at CWRU), with a total of 37 subjects (including 31 paired segmentations performed at both UPenn and CWRU), in the original space they were created (i.e., SRI for UPenn and MNI for CWRU), with 2) their corresponding co-registered and skull-stripped structural mpMRI scans (i.e., in SRI for UPenn and in MNI for CWRU), 3) the paired expert segmentation labels that were available for the 31 subjects, all being co-registered in the SRI atlas, 4) the corresponding SRI and MNI anatomical atlas files that we employed, 5) the complete set of 11,700 extracted radiomic features per subject, for each of the 31 included subjects, 6) the metadata relating to the metrics we utilized for the evaluation of the inter-rater agreement, as well as 7) the parameters used for the radiomic feature extraction and the correlation analysis results for identifying robust radiomic features, for the 28 subjects, and finally 8) the specific identified robust\\\/reproducible radiomic features.\\r\\n\\r\\nAll image related files are provided in NIfTI format, while the metadata files are provided in tabular formats (.xlsx and .csv).\\r\\n\\r\\nMNI atlas: see (Montreal Neurological Institute,\\u00a0https:\\\/\\\/mcin.ca\\\/research\\\/neuroimaging-methods\\\/atlases\\\/\\u00a0)\\r\\n\\r\\nSRI atlas: see (T. Rohlfing, et al. (2010) DOI:\\u00a010.1002\\\/hbm.20906\\u00a0,\\u00a0PMC2915788)\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Glioblastoma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"MR\",\"Segmentation\",\"Demographic\",\"Molecular Test\",\"Diagnosis\",\"Follow-Up\",\"Radiomic Feature\"],\"supporting_data\":[\"Tumor segmentations\",\"radiomic features\"],\"species\":[\"Human\"],\"subjects\":37,\"date_updated\":\"2023-04-23\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?CollectionCriteria=IvyGAP\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc2MCIsInBhc3Njb2RlIjoiOWJhOGIxODRkZDE5ZDNlNjliOWUwYjIwMDI1NGZjMGYyZDcyNjNkYiIsInBhY2thZ2VfaWQiOiI3NjAiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"multi-institutional paired expert segmentations and radiomic features of the ivy gap dataset ivygap-radiomics this dataset comprises two paired sets of expert segmentation labels for tumor sub-compartments of the pre-operative multi-institutional scans of the\\u00a0ivy glioblastoma atlas project (ivy gap) collection of the cancer imaging archive (tcia). these labels have been approved by independent expert board-certified neuroradiologists at the hospital of the university of pennsylvania and at case western reserve university. furthermore, for each of the paired sets of approved labels, a diverse comprehensive panel of radiomic features is provided, along with their corresponding skull-stripped and co-registered multi-parametric magnetic resonance imaging (mpmri) volumes (i.e. native (t1) and post-contrast t1-weighted (t1-gd), t2, t2-flair), in nifti format. the pre-operative mpmri scans were identified in the ivy gap collection via radiological assessment. these scans were initially skull-stripped and co-registered to a common anatomical atlas (provided within this dataset), before their tumor segmentation labels were produced following a consistent annotation protocol across the two institutions. the final labels were used to extract a rich panel of radiomic features through\\u00a0the cancer imaging phenomics toolkit (captk), comprising intensity, volumetric, morphologic, histogram-based, and textural parameters compliant with\\u00a0the image biomarker standardisation initiative (ibsi), as well as through a\\u00a03d slicer extension for the novel collage feature family. radiomic features robust to variability in segmentations were then identified following a statistical robustness analysis.\\r\\n\\r\\nthe approved expert segmentation labels should enable quantitative computational and clinical studies without the need to repeat manual annotations, whilst allowing for comparison across studies. they can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational competitions, such as the\\u00a0international brain tumor segmentation (brats) challenge. the provided panel of robust radiomic features may facilitate research integrative of the molecular characterization offered by the allen institute, and hence allow associations with molecular markers (radiogenomics), clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features. the complete reproducibility analysis can be found in the associated publication citation found in the \\u201ccitations & data usage policy\\u201d.\\r\\n\\r\\nspecifically, the released data comprises of 1) the available expert segmentation labels of the various tumor sub-compartments performed at each institution (i.e. 34 subjects segmented at upenn, 34 subjects segmented at cwru), with a total of 37 subjects (including 31 paired segmentations performed at both upenn and cwru), in the original space they were created (i.e., sri for upenn and mni for cwru), with 2) their corresponding co-registered and skull-stripped structural mpmri scans (i.e., in sri for upenn and in mni for cwru), 3) the paired expert segmentation labels that were available for the 31 subjects, all being co-registered in the sri atlas, 4) the corresponding sri and mni anatomical atlas files that we employed, 5) the complete set of 11,700 extracted radiomic features per subject, for each of the 31 included subjects, 6) the metadata relating to the metrics we utilized for the evaluation of the inter-rater agreement, as well as 7) the parameters used for the radiomic feature extraction and the correlation analysis results for identifying robust radiomic features, for the 28 subjects, and finally 8) the specific identified robust\\\/reproducible radiomic features.\\r\\n\\r\\nall image related files are provided in nifti format, while the metadata files are provided in tabular formats (.xlsx and .csv).\\r\\n\\r\\nmni atlas: see (montreal neurological institute,\\u00a0https:\\\/\\\/mcin.ca\\\/research\\\/neuroimaging-methods\\\/atlases\\\/\\u00a0)\\r\\n\\r\\nsri atlas: see (t. rohlfing, et al. (2010) doi:\\u00a010.1002\\\/hbm.20906\\u00a0,\\u00a0pmc2915788) glioblastoma brain mr segmentation demographic molecular test diagnosis follow-up radiomic feature tumor segmentations radiomic features community\"},{\"id\":45769,\"type\":\"analysis\",\"slug\":\"luad-ct-survival\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/luad-ct-survival\\\/\",\"title\":\"Long and Short Survival in Adenocarcinoma Lung CTs\",\"short_title\":\"LUAD-CT-Survival\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2017.0tv7b9x1\",\"summary\":\"The dataset consists of pre-surgical chest CT images of 40 subjects from the H. Lee Moffitt Cancer Center & Research Institute, Tampa, Florida. The CT images were acquired by standard-of-care, contrast-enhanced CT scans among patients who had non-small cell cancer with biopsy-verified adenocarcinoma with 2 years of follow-up. A region-growing algorithm segmented the tumor with seed points that were chosen by radiologists.\\r\\n\\r\\nThe adenocarcinoma cases are divided into the upper and lower quartiles of survival. Both the lower and upper quartiles have 20 cases. The lower quartile survival timeline is 103 to 498 days while the upper quartile timeline is 1351 to 2163 days. The average survival of the lower and upper quartiles is 288 days and 1569 days respectively. The median survival for the lower and upper quartiles is 289 and 1551 days respectively. The overall mean survival time is 879 days and median survival time is 925 days. Three of these cases, QIN-LSC-0009, QIN-LSC-0014, and QIN-LSC-0064\\u00a0appear in\\u00a0 LungCT-Diagnosis \\u00a0collection, while the remaining 37 cases are from the\\u00a0 QIN Lung CT \\u00a0collection.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Lung Adenocarcinoma\"],\"cancer_locations\":[\"Chest\"],\"data_types\":[\"Segmentation\",\"Radiomic Feature\",\"Follow-Up\"],\"supporting_data\":[\"Tumor segmentations\",\"radiomic image features\"],\"species\":[],\"subjects\":40,\"date_updated\":\"2017-08-11\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/LUAD-CT-SURVIVAL-segmentations.zip\",\"blob\":\"long and short survival in adenocarcinoma lung cts luad-ct-survival the dataset consists of pre-surgical chest ct images of 40 subjects from the h. lee moffitt cancer center & research institute, tampa, florida. the ct images were acquired by standard-of-care, contrast-enhanced ct scans among patients who had non-small cell cancer with biopsy-verified adenocarcinoma with 2 years of follow-up. a region-growing algorithm segmented the tumor with seed points that were chosen by radiologists.\\r\\n\\r\\nthe adenocarcinoma cases are divided into the upper and lower quartiles of survival. both the lower and upper quartiles have 20 cases. the lower quartile survival timeline is 103 to 498 days while the upper quartile timeline is 1351 to 2163 days. the average survival of the lower and upper quartiles is 288 days and 1569 days respectively. the median survival for the lower and upper quartiles is 289 and 1551 days respectively. the overall mean survival time is 879 days and median survival time is 925 days. three of these cases, qin-lsc-0009, qin-lsc-0014, and qin-lsc-0064\\u00a0appear in\\u00a0 lungct-diagnosis \\u00a0collection, while the remaining 37 cases are from the\\u00a0 qin lung ct \\u00a0collection. lung adenocarcinoma chest segmentation radiomic feature follow-up tumor segmentations radiomic image features community\"},{\"id\":46121,\"type\":\"analysis\",\"slug\":\"mrqy-quality-measures\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/mrqy-quality-measures\\\/\",\"title\":\"MRQy quality measures for TCIA MRI datasets\",\"short_title\":\"MRQy-Quality-Measures\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2020.JHZ2-T694\",\"summary\":\"Magnetic Resonance Imaging (MRI)\\u00a0quality assessment measures were generated for\\r\\n\\r\\n \\tT1-weighted post-contrast axial MRI sequences from 133 TCGA-GBM\\u00a0subjects\\r\\n \\tT1-weighted post-contrast axial MRI sequences from 46 CPTAC-GBM\\u00a0subjects\\r\\n \\tBoth T1- and T2-weighted axial MRI sequences from 54 TCGA-CESC\\u00a0subjects\\r\\n\\r\\nAll MRI scans for each cohort were downloaded as DICOM files from TCIA and then processed via MRQy to compute quality measures for (a) interrogating the presence of site- or equipment-specific variations within a cohort, and (b) quantifying the impact of MRI artifacts to determine what pre-analytical corrections are needed. The MRQy output can be easily interrogated via the associated HTML5 based front-end, allowing for real-time filtering and visualization. MRQy is available for download at:\\u00a0http:\\\/\\\/github.com\\\/ccipd\\\/MRQy.\\u00a0 Manifest files to download the DICOM images these results were derived from are available in the \\\"Collections Used In This Analysis Result\\\" table.\\r\\n\\r\\nIn the figure, see (a) MRQy front-end interface for interrogating TCGA-GBM cohort. (b) Outlier dataset identified on the parallel coordinate chart for the CJV quality measure found to exhibit shading artifacts on (c) representative images, especially when compared to (d) a different dataset without this artifact. (e) t-SNE scatter plot of quality measures revealing presence of site-specific batch effects (colors correspond to different sites, note presence of site-specific clusters).\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/mrqy_gbm_results_v1.png\",\"program\":\"Community\",\"cancer_types\":[\"Glioblastoma\",\"Cervical Squamous Cell Carcinoma\",\"Endocervical Adenocarcinoma\"],\"cancer_locations\":[\"Brain\",\"Cervix\"],\"data_types\":[\"Radiomic Feature\"],\"supporting_data\":[\"Image quality assessment measures\",\"Software\\\/Source Code\"],\"species\":[],\"subjects\":233,\"date_updated\":\"2020-07-16\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TCGA_GBM_MRQy_Results.xlsx\",\"blob\":\"mrqy quality measures for tcia mri datasets mrqy-quality-measures magnetic resonance imaging (mri)\\u00a0quality assessment measures were generated for\\r\\n\\r\\n \\tt1-weighted post-contrast axial mri sequences from 133 tcga-gbm\\u00a0subjects\\r\\n \\tt1-weighted post-contrast axial mri sequences from 46 cptac-gbm\\u00a0subjects\\r\\n \\tboth t1- and t2-weighted axial mri sequences from 54 tcga-cesc\\u00a0subjects\\r\\n\\r\\nall mri scans for each cohort were downloaded as dicom files from tcia and then processed via mrqy to compute quality measures for (a) interrogating the presence of site- or equipment-specific variations within a cohort, and (b) quantifying the impact of mri artifacts to determine what pre-analytical corrections are needed. the mrqy output can be easily interrogated via the associated html5 based front-end, allowing for real-time filtering and visualization. mrqy is available for download at:\\u00a0http:\\\/\\\/github.com\\\/ccipd\\\/mrqy.\\u00a0 manifest files to download the dicom images these results were derived from are available in the \\\"collections used in this analysis result\\\" table.\\r\\n\\r\\nin the figure, see (a) mrqy front-end interface for interrogating tcga-gbm cohort. (b) outlier dataset identified on the parallel coordinate chart for the cjv quality measure found to exhibit shading artifacts on (c) representative images, especially when compared to (d) a different dataset without this artifact. (e) t-sne scatter plot of quality measures revealing presence of site-specific batch effects (colors correspond to different sites, note presence of site-specific clusters). glioblastoma cervical squamous cell carcinoma endocervical adenocarcinoma brain cervix radiomic feature image quality assessment measures software\\\/source code community\"},{\"id\":45795,\"type\":\"analysis\",\"slug\":\"oropharyngeal-radiomics-outcomes\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/oropharyngeal-radiomics-outcomes\\\/\",\"title\":\"Radiomics outcome prediction in Oropharyngeal cancer\",\"short_title\":\"Oropharyngeal-Radiomics-Outcomes\",\"doi\":\"10.7937\\\/TCIA.2020.2vx6-fy46\",\"summary\":\"This study describes a subset of the HNSCC\\u00a0collection on TCIA.\\r\\n\\r\\nThere is an unmet need for integrating quantitative imaging biomarkers into current risk stratification tools and\\u00a0to explore the correlation between radiomics features \\u2013alone or in combination with clinical prognosticators- and tumor outcome.\\u00a0 Clinical meta-data and matched baseline contrast-enhanced computed tomography (CECT) scans were used to build a\\u00a0cohort of 495 oropharyngeal cancer (OPC) patients\\u00a0treated\\u00a0between 2005 and 2012.\\u00a0\\u00a0Expert radiation oncologists manually segmented primary and nodal disease gross volumes (GTVp & GTVn). Structures were named per the American Association of Physicists in Medicine (AAPM) TG-263 recommendations, then retrieved in RT-STRUCT format. Matched patient, disease, treatment and outcomes data were obtained. Radiomics analysis was performed using an open-source institutionally-developed software that runs on Matlab platform.\\r\\n\\r\\nA related dataset describing the other component of the HNSCC collection is here: Data from Head and Neck Cancer CT Atlas. DOI: 10.7937\\\/K9\\\/TCIA.2017.umz8dv6s\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Oropharyngeal Cancer\"],\"cancer_locations\":[\"Head-Neck\"],\"data_types\":[\"CT\",\"RTSTRUCT\",\"Treatment\",\"Follow-Up\",\"Diagnosis\",\"Demographic\",\"Exposure\"],\"supporting_data\":[\"Clinical\",\"radiomic features\"],\"species\":[\"Human\"],\"subjects\":412,\"date_updated\":\"2022-08-02\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/manifest-1585330520084.tcia\",\"blob\":\"radiomics outcome prediction in oropharyngeal cancer oropharyngeal-radiomics-outcomes this study describes a subset of the hnscc\\u00a0collection on tcia.\\r\\n\\r\\nthere is an unmet need for integrating quantitative imaging biomarkers into current risk stratification tools and\\u00a0to explore the correlation between radiomics features \\u2013alone or in combination with clinical prognosticators- and tumor outcome.\\u00a0 clinical meta-data and matched baseline contrast-enhanced computed tomography (cect) scans were used to build a\\u00a0cohort of 495 oropharyngeal cancer (opc) patients\\u00a0treated\\u00a0between 2005 and 2012.\\u00a0\\u00a0expert radiation oncologists manually segmented primary and nodal disease gross volumes (gtvp & gtvn). structures were named per the american association of physicists in medicine (aapm) tg-263 recommendations, then retrieved in rt-struct format. matched patient, disease, treatment and outcomes data were obtained. radiomics analysis was performed using an open-source institutionally-developed software that runs on matlab platform.\\r\\n\\r\\na related dataset describing the other component of the hnscc collection is here: data from head and neck cancer ct atlas. doi: 10.7937\\\/k9\\\/tcia.2017.umz8dv6s oropharyngeal cancer head-neck ct rtstruct treatment follow-up diagnosis demographic exposure clinical radiomic features community\"},{\"id\":45965,\"type\":\"analysis\",\"slug\":\"pan-cancer-nuclei-seg\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/pan-cancer-nuclei-seg\\\/\",\"title\":\"Dataset of Segmented Nuclei in Hematoxylin and Eosin Stained Histopathology Images\",\"short_title\":\"Pan-Cancer-Nuclei-Seg\",\"doi\":\"10.7937\\\/TCIA.2019.4A4DKP9U\",\"summary\":\"Detection, segmentation and classification of nuclei are fundamental analysis operations in digital pathology. Existing state-of-the-art approaches demand extensive amounts of supervised training data from pathologists and may still perform poorly in images from unseen tissue types. We propose an unsupervised approach for histopathology image segmentation that synthesizes heterogeneous sets of training image patches, of every tissue type. Although our synthetic\\u00a0patches are not always of high quality, we harness the motley crew of generated samples through a generally applicable importance sampling method.\\r\\n\\r\\nThis proposed approach,\\u00a0for the first time, re-weighs the training loss over synthetic data so that the ideal (unbiased) generalization loss over\\u00a0the true data distribution is minimized. This enables us\\u00a0to use a random polygon generator to synthesize approximate cellular structures (i.e., nuclear masks) for which no real examples are given in many tissue types, and hence,\\u00a0GAN-based methods are not suited. In addition, we propose a hybrid synthesis pipeline that utilizes textures in real histopathology patches and GAN models, to tackle heterogeneity in tissue textures.\\u00a0 Compared with existing state-of-the-art supervised models, our approach generalizes significantly better on cancer types without training data. Even\\u00a0in cancer types with training data, our approach achieves the same performance without supervision cost.\\r\\n\\r\\nIn this dataset we release code and nucleus segmentations in whole slide tissue images with quality control results for Whole Slide Images (WSI) in The Cancer Genome Atlas (TCGA) repository from 5,204 subjects (6,142 slide images).\\u00a0 Within this total, there are two subsets of data: (1) automatic nucleus segmentation data of 5,060 whole slide tissue images of 10 cancer types, with quality control results, and (2) manual nucleus segmentation data of 1,356 image patches from the same 10 cancer types plus additional 4 cancer types.\\r\\n5,060 Whole Slide Images (WSIs) are from the following 10 cancer types:\\r\\n BLCA  Bladder urothelial carcinoma\\r\\n BRCA  Breast invasive carcinoma\\r\\n CESC  Cervical squamous cell carcinoma and endocervical adenocarcinoma\\r\\n GBM  Glioblastoma Multiforme\\r\\n LUAD  Lung adenocarcinoma\\r\\n LUSC  Lung squamous cell carcinoma\\r\\n PAAD  Pancreatic adenocarcinoma\\r\\n PRAD  Prostate adenocarcinoma\\r\\n SKCM  Skin Cutaneous Melanoma\\r\\n UCEC  Uterine Corpus Endometrial Carcinoma\\r\\n\\r\\nNote that you can also download segmentation data of following 4 cancer types, although they are not officially verified.\\r\\n\\r\\nCOAD Colon adenocarcinoma\\r\\nREAD Rectal adenocarcinoma\\r\\nSTAD Stomach adenocarcinoma\\r\\nUVM Uveal Melanoma\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/image002.png\",\"program\":\"Community\",\"cancer_types\":[\"Bladder Urothelial Carcinoma\",\"Breast Invasive Carcinoma\",\"Cervical Squamous Cell Carcinoma\",\"Endocervical Adenocarcinoma\",\"Glioblastoma Multiforme\",\"Lung Adenocarcinoma\",\"Lung Squamous Cell Carcinoma\",\"Pancreatic adenocarcinoma\",\"Prostate Adenocarcinoma\",\"Skin Cutaneous Melanoma\",\"Uterine Corpus Endometrial Carcinoma\",\"Colon adenocarcinoma\",\"Rectal Adenocarcinoma\",\"Stomach Adenocarcinoma\",\"Uveal Melanoma\"],\"cancer_locations\":[\"Bladder\",\"Brain\",\"Breast\",\"Colon\",\"Eye\",\"Lung\",\"Pancreas\",\"Prostate\",\"Rectum\",\"Stomach\",\"Uterus\",\"Endometrium\"],\"data_types\":[\"Pathology Detail\",\"Other\",\"Measurement\"],\"supporting_data\":[\"Nuclei segmentations\"],\"species\":[],\"subjects\":5204,\"date_updated\":\"2020-02-08\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6Ijc2NiIsInBhc3Njb2RlIjoiMDgwNjQ5ZDRmOWRjZjMwMzllMDMyN2Y5Njk2MTU5NTkxNWY4MjNmMiIsInBhY2thZ2VfaWQiOiI3NjYiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"dataset of segmented nuclei in hematoxylin and eosin stained histopathology images pan-cancer-nuclei-seg detection, segmentation and classification of nuclei are fundamental analysis operations in digital pathology. existing state-of-the-art approaches demand extensive amounts of supervised training data from pathologists and may still perform poorly in images from unseen tissue types. we propose an unsupervised approach for histopathology image segmentation that synthesizes heterogeneous sets of training image patches, of every tissue type. although our synthetic\\u00a0patches are not always of high quality, we harness the motley crew of generated samples through a generally applicable importance sampling method.\\r\\n\\r\\nthis proposed approach,\\u00a0for the first time, re-weighs the training loss over synthetic data so that the ideal (unbiased) generalization loss over\\u00a0the true data distribution is minimized. this enables us\\u00a0to use a random polygon generator to synthesize approximate cellular structures (i.e., nuclear masks) for which no real examples are given in many tissue types, and hence,\\u00a0gan-based methods are not suited. in addition, we propose a hybrid synthesis pipeline that utilizes textures in real histopathology patches and gan models, to tackle heterogeneity in tissue textures.\\u00a0 compared with existing state-of-the-art supervised models, our approach generalizes significantly better on cancer types without training data. even\\u00a0in cancer types with training data, our approach achieves the same performance without supervision cost.\\r\\n\\r\\nin this dataset we release code and nucleus segmentations in whole slide tissue images with quality control results for whole slide images (wsi) in the cancer genome atlas (tcga) repository from 5,204 subjects (6,142 slide images).\\u00a0 within this total, there are two subsets of data: (1) automatic nucleus segmentation data of 5,060 whole slide tissue images of 10 cancer types, with quality control results, and (2) manual nucleus segmentation data of 1,356 image patches from the same 10 cancer types plus additional 4 cancer types.\\r\\n5,060 whole slide images (wsis) are from the following 10 cancer types:\\r\\n blca  bladder urothelial carcinoma\\r\\n brca  breast invasive carcinoma\\r\\n cesc  cervical squamous cell carcinoma and endocervical adenocarcinoma\\r\\n gbm  glioblastoma multiforme\\r\\n luad  lung adenocarcinoma\\r\\n lusc  lung squamous cell carcinoma\\r\\n paad  pancreatic adenocarcinoma\\r\\n prad  prostate adenocarcinoma\\r\\n skcm  skin cutaneous melanoma\\r\\n ucec  uterine corpus endometrial carcinoma\\r\\n\\r\\nnote that you can also download segmentation data of following 4 cancer types, although they are not officially verified.\\r\\n\\r\\ncoad colon adenocarcinoma\\r\\nread rectal adenocarcinoma\\r\\nstad stomach adenocarcinoma\\r\\nuvm uveal melanoma bladder urothelial carcinoma breast invasive carcinoma cervical squamous cell carcinoma endocervical adenocarcinoma glioblastoma multiforme lung adenocarcinoma lung squamous cell carcinoma pancreatic adenocarcinoma prostate adenocarcinoma skin cutaneous melanoma uterine corpus endometrial carcinoma colon adenocarcinoma rectal adenocarcinoma stomach adenocarcinoma uveal melanoma bladder brain breast colon eye lung pancreas prostate rectum stomach uterus endometrium pathology detail other measurement nuclei segmentations community\"},{\"id\":46039,\"type\":\"analysis\",\"slug\":\"plethora\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/plethora\\\/\",\"title\":\"Thoracic Volume and Pleural Effusion Segmentations in Diseased Lungs for Benchmarking Chest CT Processing Pipelines\",\"short_title\":\"PleThora\",\"doi\":\"10.7937\\\/tcia.2020.6c7y-gq39\",\"summary\":\"Automated or semi-automated algorithms intended for chest CT analyses typically require the creation of a 3D map of the thoracic volume as their initial step. Identifying this anatomic region precedes fundamental tasks such as lung structure segmentation, lesion detection, and radiomics feature extraction in analysis pipelines. However, automatic approaches to segment the thoracic volume maps struggle to perform consistently in subjects with diseased lungs \\u2013 yet this is exactly the circumstance for which pipeline analyses would be most useful.\\r\\n\\r\\nTo address this need, we have created PleThora, a dataset of pleural effusion and thoracic cavity segmentations in subjects with diseased lungs. PleThora consists of left and right thoracic cavity segmentations delineated on 402 CT scans from The Cancer Imaging Archive\\u00a0NSCLC-Radiomics\\u00a0collection as well as separate segmentations labeling pleural effusions alone. Thoracic cavity segmentations include lung parenchyma, tumor, atelectasis, adhesions, and effusion. PleThora is a tool for medical image preprocessing and segmentation researchers to build and compare approaches for region-of-interest identification and analysis.\\r\\n\\r\\nThe thoracic cavity segmentations were generated automatically by a U-Net based algorithm trained on chest CTs without cancer, manually corrected by a medical student, and revised by a radiation oncologist or a radiologist.\\u00a0 Pleural effusion segmentations were manually delineated by a medical student and revised by a radiologist. Expert GTV segmentations already provided by the\\u00a0NSCLC-Radiomics collection helped inform our segmentations, and areas of the effusion that overlap with GTVs are not included. Researchers interested in discriminating between GTV and effusion using imaging biomarker inputs may find our pleural effusion segmentations useful, especially when paired with the GTV segmentations provided in the\\u00a0NSCLC-Radiomics collection.\\r\\n\\r\\nTabular data are also provided, including GTV, thorax, and effusion volumes (in cm3), tumor location, and image metadata. Additionally, we standardized a train\\\/test split for training deep learning algorithms with the thoracic cavity segmentations.\\r\\n\\r\\nNote: These segmentations use the RPI orientation, but the original DICOM files are oriented using the RAI convention.\\u00a0 As a result, some tools such as ITK-SNAP will not render the segmentations in the correct orientation when visualized.\\u00a0 The authors of these data suggest using software like Mango (http:\\\/\\\/ric.uthscsa.edu\\\/mango\\\/) or to convert to DICOM files to NIfTI with software like dcm2niix (https:\\\/\\\/github.com\\\/rordenlab\\\/dcm2niix) to address this issue.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Screen-Shot-2020-03-31-at-4.00.06-PM.png\",\"program\":\"Community\",\"cancer_types\":[\"Lung\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"Segmentation\",\"Radiomic Feature\",\"Measurement\",\"Other\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":402,\"date_updated\":\"2020-07-28\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/PleThora-Thoracic_Cavities-June-2020.zip\",\"blob\":\"thoracic volume and pleural effusion segmentations in diseased lungs for benchmarking chest ct processing pipelines plethora automated or semi-automated algorithms intended for chest ct analyses typically require the creation of a 3d map of the thoracic volume as their initial step. identifying this anatomic region precedes fundamental tasks such as lung structure segmentation, lesion detection, and radiomics feature extraction in analysis pipelines. however, automatic approaches to segment the thoracic volume maps struggle to perform consistently in subjects with diseased lungs \\u2013 yet this is exactly the circumstance for which pipeline analyses would be most useful.\\r\\n\\r\\nto address this need, we have created plethora, a dataset of pleural effusion and thoracic cavity segmentations in subjects with diseased lungs. plethora consists of left and right thoracic cavity segmentations delineated on 402 ct scans from the cancer imaging archive\\u00a0nsclc-radiomics\\u00a0collection as well as separate segmentations labeling pleural effusions alone. thoracic cavity segmentations include lung parenchyma, tumor, atelectasis, adhesions, and effusion. plethora is a tool for medical image preprocessing and segmentation researchers to build and compare approaches for region-of-interest identification and analysis.\\r\\n\\r\\nthe thoracic cavity segmentations were generated automatically by a u-net based algorithm trained on chest cts without cancer, manually corrected by a medical student, and revised by a radiation oncologist or a radiologist.\\u00a0 pleural effusion segmentations were manually delineated by a medical student and revised by a radiologist. expert gtv segmentations already provided by the\\u00a0nsclc-radiomics collection helped inform our segmentations, and areas of the effusion that overlap with gtvs are not included. researchers interested in discriminating between gtv and effusion using imaging biomarker inputs may find our pleural effusion segmentations useful, especially when paired with the gtv segmentations provided in the\\u00a0nsclc-radiomics collection.\\r\\n\\r\\ntabular data are also provided, including gtv, thorax, and effusion volumes (in cm3), tumor location, and image metadata. additionally, we standardized a train\\\/test split for training deep learning algorithms with the thoracic cavity segmentations.\\r\\n\\r\\nnote: these segmentations use the rpi orientation, but the original dicom files are oriented using the rai convention.\\u00a0 as a result, some tools such as itk-snap will not render the segmentations in the correct orientation when visualized.\\u00a0 the authors of these data suggest using software like mango (http:\\\/\\\/ric.uthscsa.edu\\\/mango\\\/) or to convert to dicom files to nifti with software like dcm2niix (https:\\\/\\\/github.com\\\/rordenlab\\\/dcm2niix) to address this issue. lung lung segmentation radiomic feature measurement other community\"},{\"id\":45947,\"type\":\"analysis\",\"slug\":\"prostatex-seg-hires\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/prostatex-seg-hires\\\/\",\"title\":\"High Resolution Prostate Segmentations for the ProstateX-Challenge\",\"short_title\":\"PROSTATEx-Seg-HiRes\",\"doi\":\"10.7937\\\/tcia.2019.deg7zg1u\",\"summary\":\"We created 66 high resolution segmentations for randomly chosen T2-weighted volumes of the\\u00a0SPIE-AAPM-NCI PROSTATEx Challenges (PROSTATEx). The high\\u00a0resolution segmentations were obtained\\u00a0by\\u00a0considering the three scan directions: for each scan direction (axial, sagittal, coronal),\\u00a0the gland was manually delineated by a medical student, followed\\u00a0by a review and corrections of an expert urologist.\\u00a0These three anisotropic segmentations were fused to one isotropic segmentation by means of shape-based interpolation\\u00a0in the following manner: (1) The signed distance transformation of the three segmentations is computed.\\u00a0(2) The anisotropic distance volumes are transformed into an isotropic high-resolution representation with linear interpolation. (3) By averaging the distances, smoothing and thresholding them at zero, we obtained the fused segmentation. The resulting segmentations were manually verified and corrected further by the expert urologist if necessary.\\u00a0Serving as ground truth for training CNNs, these segmentations have the potential to improve the segmentation accuracy of automated algorithms. By considering not only the axial scans but also sagittal and coronal scan directions, we aimed to have higher fidelity of the segmentations especially at the apex and base regions of the prostate.\\r\\n\\r\\nThe segmentations to standard DICOM representation were created with  dcmqi\\u00a0\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/High-Resolution.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Prostate\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"SEG\"],\"supporting_data\":[\"Organ segmentations\"],\"species\":[\"Human\"],\"subjects\":66,\"date_updated\":\"2020-09-18\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&DateRange=09\\\/10\\\/2020-09\\\/11\\\/2020&CollectionCriteria=PROSTATEx&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/High-Resolution-Prostate-Segmentations-for-the-ProstateX-Challenge-NBIA-manifest_20200917.tcia\",\"blob\":\"high resolution prostate segmentations for the prostatex-challenge prostatex-seg-hires we created 66 high resolution segmentations for randomly chosen t2-weighted volumes of the\\u00a0spie-aapm-nci prostatex challenges (prostatex). the high\\u00a0resolution segmentations were obtained\\u00a0by\\u00a0considering the three scan directions: for each scan direction (axial, sagittal, coronal),\\u00a0the gland was manually delineated by a medical student, followed\\u00a0by a review and corrections of an expert urologist.\\u00a0these three anisotropic segmentations were fused to one isotropic segmentation by means of shape-based interpolation\\u00a0in the following manner: (1) the signed distance transformation of the three segmentations is computed.\\u00a0(2) the anisotropic distance volumes are transformed into an isotropic high-resolution representation with linear interpolation. (3) by averaging the distances, smoothing and thresholding them at zero, we obtained the fused segmentation. the resulting segmentations were manually verified and corrected further by the expert urologist if necessary.\\u00a0serving as ground truth for training cnns, these segmentations have the potential to improve the segmentation accuracy of automated algorithms. by considering not only the axial scans but also sagittal and coronal scan directions, we aimed to have higher fidelity of the segmentations especially at the apex and base regions of the prostate.\\r\\n\\r\\nthe segmentations to standard dicom representation were created with  dcmqi\\u00a0 prostate prostate seg organ segmentations community\"},{\"id\":46151,\"type\":\"analysis\",\"slug\":\"prostatex-seg-zones\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/prostatex-seg-zones\\\/\",\"title\":\"PROSTATEx Zone Segmentations\",\"short_title\":\"PROSTATEx-Seg-Zones\",\"doi\":\"10.7937\\\/TCIA.NBB4-4655\",\"summary\":\"This collection contains prostate\\u2019s zonal segmentation for 98 cases randomly selected from the SPIE-AAPM-NCI PROSTATEx Challenges (PROSTATEx). The four-class segmentation encompasses the peripheral zone, transition zone, fibromuscular stroma and the distal prostatic urethra. As underlying images, we used transversal T2w scans. Segmentations were created by a medical student with experience in prostate segmentation and an expert urologist who instructed the student and double-checked the segmentations in the end. The DICOM representation of these segmentations were generated with dcmqi.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/example_segmentation_20201120.png\",\"program\":\"Community\",\"cancer_types\":[\"Prostate\"],\"cancer_locations\":[\"Prostate\"],\"data_types\":[\"SEG\"],\"supporting_data\":[\"Organ segmentations\"],\"species\":[\"Human\"],\"subjects\":98,\"date_updated\":\"2020-11-25\",\"status\":\"\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&DateRange=11\\\/01\\\/2020-11\\\/06\\\/2020&CollectionCriteria=PROSTATEx&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ProstateX-Zone-Segmentations-manifest_20201125.tcia\",\"blob\":\"prostatex zone segmentations prostatex-seg-zones this collection contains prostate\\u2019s zonal segmentation for 98 cases randomly selected from the spie-aapm-nci prostatex challenges (prostatex). the four-class segmentation encompasses the peripheral zone, transition zone, fibromuscular stroma and the distal prostatic urethra. as underlying images, we used transversal t2w scans. segmentations were created by a medical student with experience in prostate segmentation and an expert urologist who instructed the student and double-checked the segmentations in the end. the dicom representation of these segmentations were generated with dcmqi. prostate prostate seg organ segmentations community\"},{\"id\":46141,\"type\":\"analysis\",\"slug\":\"qiba-volct-1b\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/qiba-volct-1b\\\/\",\"title\":\"QIBA VolCT Group 1B Round 2 No Change Size Measurements\",\"short_title\":\"QIBA-VolCT-1B\",\"doi\":\"10.7937\\\/tcia.2020.1c3h-vp70\",\"summary\":\"PURPOSE: To determine the variability of lesion size measurements in\\u00a0computed tomography\\u00a0data sets of patients imaged under a \\u201cno change\\u201d (\\u201ccoffee break\\u201d) condition and to determine the impact of two reading paradigms on measurement variability.\\r\\n\\r\\nMETHOD AND MATERIALS: Using data sets from 32\\u00a0RIDER Lung CT patients and 8 RIDER Pilot patients scanned twice within 15 minutes (\\u201cno change\\u201d), measurements were performed by five radiologists in two phases: (1) independent reading of each computed tomography dataset (timepoint): (2) a locked, sequential reading of datasets. Readers performed measurements using several sizing methods, including one-dimensional (1D) longest in-slice dimension and 3D semi-automated segmented volume. Change in size was estimated by comparing measurements performed on both timepoints for the same lesion, for each reader and each measurement method. For each reading paradigm, results were pooled across lesions, across readers, and across both readers and lesions, for each measurement method.\\r\\n\\r\\nFor additional information please see\\u00a0https:\\\/\\\/qibawiki.rsna.org\\\/index.php\\\/VolCT_-_Group_1B and the Release Notes\\u00a0from which the following may be specially useful: \\\"Results are described in DICOM SR files, which in turn reference DICOM segmentation files that encode the region as a 3D raster, and presentation states that record the zoom, pan and window levels at the time of measurement\\\" and \\\"Readers are identified by number (from 1 through 5) ... and their actual identity recorded in the SR tree in observer context and worklist descriptions has been removed.\\\"\",\"image\":false,\"program\":\"QIBA\",\"cancer_types\":[\"Lung\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"PR\",\"SR\",\"SEG\"],\"supporting_data\":[\"Tumor segmentations\",\"image features\"],\"species\":[],\"subjects\":40,\"date_updated\":\"2023-05-24\",\"status\":\"\",\"license\":\"CC BY 4.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?MinNumberOfStudiesCriteria=1&CollectionCriteria=RIDER Lung CT&&CollectionCriteria=RIDER Pilot&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NBIA-manifest-QIBA-VolCT-Group-1B-Round-2.tcia\",\"blob\":\"qiba volct group 1b round 2 no change size measurements qiba-volct-1b purpose: to determine the variability of lesion size measurements in\\u00a0computed tomography\\u00a0data sets of patients imaged under a \\u201cno change\\u201d (\\u201ccoffee break\\u201d) condition and to determine the impact of two reading paradigms on measurement variability.\\r\\n\\r\\nmethod and materials: using data sets from 32\\u00a0rider lung ct patients and 8 rider pilot patients scanned twice within 15 minutes (\\u201cno change\\u201d), measurements were performed by five radiologists in two phases: (1) independent reading of each computed tomography dataset (timepoint): (2) a locked, sequential reading of datasets. readers performed measurements using several sizing methods, including one-dimensional (1d) longest in-slice dimension and 3d semi-automated segmented volume. change in size was estimated by comparing measurements performed on both timepoints for the same lesion, for each reader and each measurement method. for each reading paradigm, results were pooled across lesions, across readers, and across both readers and lesions, for each measurement method.\\r\\n\\r\\nfor additional information please see\\u00a0https:\\\/\\\/qibawiki.rsna.org\\\/index.php\\\/volct_-_group_1b and the release notes\\u00a0from which the following may be specially useful: \\\"results are described in dicom sr files, which in turn reference dicom segmentation files that encode the region as a 3d raster, and presentation states that record the zoom, pan and window levels at the time of measurement\\\" and \\\"readers are identified by number (from 1 through 5) ... and their actual identity recorded in the sr tree in observer context and worklist descriptions has been removed.\\\" lung lung pr sr seg tumor segmentations image features qiba\"},{\"id\":45659,\"type\":\"analysis\",\"slug\":\"qin-lungct-seg\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/qin-lungct-seg\\\/\",\"title\":\"QIN multi-site collection of Lung CT data with Nodule Segmentations\",\"short_title\":\"QIN-LungCT-Seg\",\"doi\":\"10.7937\\\/k9\\\/tcia.2015.1buvfjr7\",\"summary\":\"This dataset (also known as the \\u201cmoist run\\u201d among QIN sites) contains CT images (41 total scans) of non-small cell lung cancer from: the Reference Image Database to Evaluate Therapy Response (RIDER), the Lung Image Database Consortium (LIDC), patients from Stanford University Medical Center and the Moffitt Cancer Center, and the Columbia University\\\/FDA Phantom. In addition, 3 academic institutions (Columbia, Stanford, Moffitt-USF) each ran their own segmentation algorithm on a total of 52 tumor volumes.\\u00a0 Segmentations were performed 3 different times with different initial conditions, resulting in 9 segmentations formatted as DICOM Segmentation Objects (DSOs) for each tumor volume, for a total of 468 segmentations. This collection may be useful for designing and comparing competing segmentation algorithms, for establishing acceptable ranges of variability in volume and segmentation borders, and for developing algorithms for creating cancer biomarkers from features computed from the segmented tumors and their environments.\\r\\n\\r\\n Note:  In December 2018 it was discovered that an update to\\u00a0NSCLC Radiogenomics mistakenly resulted in the deletion of the segmentation data from this analysis set.\\u00a0 As a result, the 10 affected patients and related segmentations are no longer included in the download section below.\",\"image\":false,\"program\":\"QIN\",\"cancer_types\":[\"Lung\"],\"cancer_locations\":[\"Chest\"],\"data_types\":[\"SEG\",\"CT\",\"Other\"],\"supporting_data\":[\"Tumor segmentations\"],\"species\":[],\"subjects\":31,\"date_updated\":\"2018-12-18\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/QIN-Multi-site-Lung-SEG-Only-minus-Stanford.tcia\",\"blob\":\"qin multi-site collection of lung ct data with nodule segmentations qin-lungct-seg this dataset (also known as the \\u201cmoist run\\u201d among qin sites) contains ct images (41 total scans) of non-small cell lung cancer from: the reference image database to evaluate therapy response (rider), the lung image database consortium (lidc), patients from stanford university medical center and the moffitt cancer center, and the columbia university\\\/fda phantom. in addition, 3 academic institutions (columbia, stanford, moffitt-usf) each ran their own segmentation algorithm on a total of 52 tumor volumes.\\u00a0 segmentations were performed 3 different times with different initial conditions, resulting in 9 segmentations formatted as dicom segmentation objects (dsos) for each tumor volume, for a total of 468 segmentations. this collection may be useful for designing and comparing competing segmentation algorithms, for establishing acceptable ranges of variability in volume and segmentation borders, and for developing algorithms for creating cancer biomarkers from features computed from the segmented tumors and their environments.\\r\\n\\r\\n note:  in december 2018 it was discovered that an update to\\u00a0nsclc radiogenomics mistakenly resulted in the deletion of the segmentation data from this analysis set.\\u00a0 as a result, the 10 affected patients and related segmentations are no longer included in the download section below. lung chest seg ct other tumor segmentations qin\"},{\"id\":46081,\"type\":\"analysis\",\"slug\":\"radiomic-feature-standards\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/radiomic-feature-standards\\\/\",\"title\":\"Standardization in Quantitative Imaging: A Multi-center Comparison of Radiomic Feature Values\",\"short_title\":\"Radiomic-Feature-Standards\",\"doi\":\"10.7937\\\/tcia.2020.9era-gg29\",\"summary\":\"This dataset was used by the NCI's Quantitative Imaging Network (QIN) PET-CT Subgroup for their project titled: Multi-center Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Datasets.\\u00a0 The purpose of this project was to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included common image data sets and standardized feature definitions.\\r\\n\\r\\n\\r\\n\\r\\nThe image datasets (and Volumes of Interest \\u2013 VOIs) provided here are the same ones used in that project and reported in the publication listed below (ISSN 2379-1381\\u00a0https:\\\/\\\/doi.org\\\/10.18383\\\/j.tom.2019.00031).\\u00a0 In addition, we have provided detailed information about the software packages used (Table 1 in that publication) as well as the individual feature value results for each image dataset and each software package that was used to create the summary tables (Tables 2, 3 and 4) in that publication.\\r\\n\\r\\nFor that project, nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture and that are described in detail in the International Biomarker Standardisation Initiative (IBSI,\\u00a0https:\\\/\\\/arxiv.org\\\/abs\\\/1612.07003\\u00a0\\u00a0and\\u00a0 publication (Zwanenburg A. Valli\\u00e8res M, et al, The Image Biomarker\\u00a0Standardization\\u00a0Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping. Radiology. 2020 May;295(2):328-338. doi: https:\\\/\\\/doi.org\\\/10.1148\\\/radiol.2020191145).\\r\\n\\r\\nThere are three datasets provided \\u2013 two image datasets and one dataset consisting of four excel spreadsheets containing feature values.\\r\\n\\r\\n \\tThe first image dataset is a set of three Digital Reference Objects (DROs) used in the project, which are: (a) a sphere with uniform intensity, (b) a sphere with intensity variation (c) a nonspherical (but mathematically defined) object with uniform intensity. These DROs were created by the team at Stanford University and are described in (Jaggi A, Mattonen SA, McNitt-Gray M, Napel S. Stanford DRO Toolkit: digital reference objects for standardization of radiomic features. Tomography. 2019;6:\\u2013.) and are a subset of the DROs described in DRO Toolkit. Each DRO is represented in both DICOM and NIfTI format and the VOI was provided in each format as well (DICOM Segmentation Object (DSO) as well as NIfTI segmentation boundary).\\r\\n \\tThe second image dataset is the set of 10 patient CT scans, originating from the LIDC-IDRI dataset, that were used in the QIN multi-site collection of Lung CT data with Nodule Segmentations project ( https:\\\/\\\/doi.org\\\/10.7937\\\/K9\\\/TCIA.2015.1BUVFJR7 ). In that QIN study, a single lesion from each case was identified for analysis and then nine VOIs were generated using three repeat runs of three segmentation algorithms (one from each of three academic institutions) on each lesion.\\u00a0 To eliminate one source of variability in our project, only one of the VOIs previously created for each lesion was identified and all sites used that same VOI definition. The specific VOI chosen for each lesion was the first run of the first algorithm (algorithm 1, run 1). DICOM images were provided for each dataset and the VOI was provided in both DICOM Segmentation Object (DSO) and NIfTI segmentation formats.\\r\\n \\tThe third dataset is a collection of four excel spreadsheets, each of which contains detailed information corresponding to each of the four tables in the publication. For example, the raw feature values and the summary tables for Tables 2,3 and 4 reported in the publication cited (https:\\\/\\\/doi.org\\\/10.18383\\\/j.tom.2019.00031). These tables are:\\r\\n\\r\\n Software Package details : This table contains detailed information about the software packages used in the study (and listed in Table 1 in the publication) including version number and any parameters specified in the calculation of the features reported.\\r\\n\\r\\n DRO results : This contains the original feature values obtained for each software package for each DRO as well as the table summarizing results across software packages (Table 2 in the publication) .\\r\\n\\r\\nPatient Dataset results: This contains the original feature values for each software package for each patient dataset (1 lesion per case) as well as the table summarizing results across software packages and patient datasets (Table 3 in the publication).\\r\\n\\r\\n Harmonized GLCM Entropy Results : This contains the values for the \\u201cHarmonized\\u201d GLCM Entropy feature for each patient dataset and each software package as well as the summary across software packages (Table 4 in the publication).\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Screen-Shot-2020-06-19-at-7.51.57-PM.png\",\"program\":\"QIN\",\"cancer_types\":[\"Lung\"],\"cancer_locations\":[\"Chest\"],\"data_types\":[\"Segmentation\",\"Other\",\"Radiomic Feature\"],\"supporting_data\":[\"Multi-center comparison of radiomic features.\"],\"species\":[],\"subjects\":13,\"date_updated\":\"2020-06-09\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?ImageModalityCriteria=CT&MinNumberOfStudiesCriteria=1&PatientCriteria=Phantom-100.0-1.0-1.0-1.0-9.0-0.0-100.0-10.0-0.0-0.0,Phantom-100.0-1.0-1.0-1.0-9.0-0.0-100.0-10.0-50.0-0.0,Phantom-100.0-1.0-1.0-1.0-9.0-0.2-100.0-10.0-0.0-0.0,LIDC-IDRI-0314,LIDC-IDRI-0325,LIDC-IDRI-0580,LIDC-IDRI-0766,LIDC-IDRI-0771,LIDC-IDRI-0811,LIDC-IDRI-0905,LIDC-IDRI-0963,LIDC-IDRI-0965,LIDC-IDRI-1012\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/DRO-Toolkit-3-Subjects-SEG-and-QIN-multi-site-10-Subjects-SEG-NIfTI.zip\",\"blob\":\"standardization in quantitative imaging: a multi-center comparison of radiomic feature values radiomic-feature-standards this dataset was used by the nci's quantitative imaging network (qin) pet-ct subgroup for their project titled: multi-center comparison of radiomic features from different software packages on digital reference objects and patient datasets.\\u00a0 the purpose of this project was to assess the agreement among radiomic features when computed by several groups by using different software packages under very tightly controlled conditions, which included common image data sets and standardized feature definitions.\\r\\n\\r\\n\\r\\n\\r\\nthe image datasets (and volumes of interest \\u2013 vois) provided here are the same ones used in that project and reported in the publication listed below (issn 2379-1381\\u00a0https:\\\/\\\/doi.org\\\/10.18383\\\/j.tom.2019.00031).\\u00a0 in addition, we have provided detailed information about the software packages used (table 1 in that publication) as well as the individual feature value results for each image dataset and each software package that was used to create the summary tables (tables 2, 3 and 4) in that publication.\\r\\n\\r\\nfor that project, nine common quantitative imaging features were selected for comparison including features that describe morphology, intensity, shape, and texture and that are described in detail in the international biomarker standardisation initiative (ibsi,\\u00a0https:\\\/\\\/arxiv.org\\\/abs\\\/1612.07003\\u00a0\\u00a0and\\u00a0 publication (zwanenburg a. valli\\u00e8res m, et al, the image biomarker\\u00a0standardization\\u00a0initiative: standardized quantitative radiomics for high-throughput image-based phenotyping. radiology. 2020 may;295(2):328-338. doi: https:\\\/\\\/doi.org\\\/10.1148\\\/radiol.2020191145).\\r\\n\\r\\nthere are three datasets provided \\u2013 two image datasets and one dataset consisting of four excel spreadsheets containing feature values.\\r\\n\\r\\n \\tthe first image dataset is a set of three digital reference objects (dros) used in the project, which are: (a) a sphere with uniform intensity, (b) a sphere with intensity variation (c) a nonspherical (but mathematically defined) object with uniform intensity. these dros were created by the team at stanford university and are described in (jaggi a, mattonen sa, mcnitt-gray m, napel s. stanford dro toolkit: digital reference objects for standardization of radiomic features. tomography. 2019;6:\\u2013.) and are a subset of the dros described in dro toolkit. each dro is represented in both dicom and nifti format and the voi was provided in each format as well (dicom segmentation object (dso) as well as nifti segmentation boundary).\\r\\n \\tthe second image dataset is the set of 10 patient ct scans, originating from the lidc-idri dataset, that were used in the qin multi-site collection of lung ct data with nodule segmentations project ( https:\\\/\\\/doi.org\\\/10.7937\\\/k9\\\/tcia.2015.1buvfjr7 ). in that qin study, a single lesion from each case was identified for analysis and then nine vois were generated using three repeat runs of three segmentation algorithms (one from each of three academic institutions) on each lesion.\\u00a0 to eliminate one source of variability in our project, only one of the vois previously created for each lesion was identified and all sites used that same voi definition. the specific voi chosen for each lesion was the first run of the first algorithm (algorithm 1, run 1). dicom images were provided for each dataset and the voi was provided in both dicom segmentation object (dso) and nifti segmentation formats.\\r\\n \\tthe third dataset is a collection of four excel spreadsheets, each of which contains detailed information corresponding to each of the four tables in the publication. for example, the raw feature values and the summary tables for tables 2,3 and 4 reported in the publication cited (https:\\\/\\\/doi.org\\\/10.18383\\\/j.tom.2019.00031). these tables are:\\r\\n\\r\\n software package details : this table contains detailed information about the software packages used in the study (and listed in table 1 in the publication) including version number and any parameters specified in the calculation of the features reported.\\r\\n\\r\\n dro results : this contains the original feature values obtained for each software package for each dro as well as the table summarizing results across software packages (table 2 in the publication) .\\r\\n\\r\\npatient dataset results: this contains the original feature values for each software package for each patient dataset (1 lesion per case) as well as the table summarizing results across software packages and patient datasets (table 3 in the publication).\\r\\n\\r\\n harmonized glcm entropy results : this contains the values for the \\u201charmonized\\u201d glcm entropy feature for each patient dataset and each software package as well as the summary across software packages (table 4 in the publication). lung chest segmentation other radiomic feature multi-center comparison of radiomic features. qin\"},{\"id\":45881,\"type\":\"analysis\",\"slug\":\"rider-lungct-seg\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/rider-lungct-seg\\\/\",\"title\":\"RIDER Lung CT Segmentation Labels from: Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach\",\"short_title\":\"RIDER-LungCT-Seg\",\"doi\":\"10.7937\\\/tcia.2020.jit9grk8\",\"summary\":\"This dataset contains images from 31 out of the 32 non-small cell lung cancer (NSCLC) patients in the\\u00a0RIDER Lung CT\\u00a0collection on TCIA. For these subjects a radiation oncologist was blinded to the all delineations of the 3D volume of the gross tumor volume. They were then asked to manually delineate the gross tumour volume in both the test image and the re-test image. The process was repeated using an in-house autosegmentation method. There is no clinical outcome data associated with this dataset.\\r\\n\\r\\nThis dataset refers to the RIDER dataset of the study published in Nature Communications (http:\\\/\\\/doi.org\\\/10.1038\\\/ncomms5006). In short, this publication used the dataset to select for repeatable radiomics features in a test-retest context. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In the published analysis, 440 features quantifying tumour image intensity, shape and texture, were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.\\r\\n\\r\\n\\u00a0\\r\\n\\r\\nOther data sets in the Cancer Imaging Archive that were used in the same\\u00a0study published in Nature Communications:\\u00a0NSCLC-Radiomics,\\u00a0NSCLC-Radiomics-Genomics,\\u00a0NSCLC-Radiomics-Interobserver1,\\u00a0HEAD-NECK-RADIOMICS-HN1.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/NSCLC-RADIOMICS-GRAPHIC.jpg\",\"program\":\"Community\",\"cancer_types\":[\"Lung\"],\"cancer_locations\":[\"Chest\"],\"data_types\":[\"RTSTRUCT\",\"SEG\"],\"supporting_data\":[\"Tumor segmentations\"],\"species\":[\"Human\"],\"subjects\":31,\"date_updated\":\"2021-10-28\",\"status\":\"\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/RIDER-Lung-CT-RTSTRUCTS-DICOM-SEGS-Leonard-Wee-Feb-10-2020.tcia\",\"blob\":\"rider lung ct segmentation labels from: decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach rider-lungct-seg this dataset contains images from 31 out of the 32 non-small cell lung cancer (nsclc) patients in the\\u00a0rider lung ct\\u00a0collection on tcia. for these subjects a radiation oncologist was blinded to the all delineations of the 3d volume of the gross tumor volume. they were then asked to manually delineate the gross tumour volume in both the test image and the re-test image. the process was repeated using an in-house autosegmentation method. there is no clinical outcome data associated with this dataset.\\r\\n\\r\\nthis dataset refers to the rider dataset of the study published in nature communications (http:\\\/\\\/doi.org\\\/10.1038\\\/ncomms5006). in short, this publication used the dataset to select for repeatable radiomics features in a test-retest context. radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. in the published analysis, 440 features quantifying tumour image intensity, shape and texture, were extracted. we found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. these data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. this may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost.\\r\\n\\r\\n\\u00a0\\r\\n\\r\\nother data sets in the cancer imaging archive that were used in the same\\u00a0study published in nature communications:\\u00a0nsclc-radiomics,\\u00a0nsclc-radiomics-genomics,\\u00a0nsclc-radiomics-interobserver1,\\u00a0head-neck-radiomics-hn1. lung chest rtstruct seg tumor segmentations community\"},{\"id\":46609,\"type\":\"analysis\",\"slug\":\"rsna-asnr-miccai-brats-2021\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/rsna-asnr-miccai-brats-2021\\\/\",\"title\":\"RSNA-ASNR-MICCAI-BraTS-2021\",\"short_title\":\"RSNA-ASNR-MICCAI-BraTS-2021\",\"doi\":\"10.7937\\\/jc8x-9874\",\"summary\":\"This dataset includes brain MRI scans of adult brain glioma patients, comprising of 4 structural modalities (i.e., T1, T1c, T2, T2-FLAIR) and associated manually generated ground truth labels for each tumor sub-region (enhancement, necrosis, edema), as well as their MGMT promoter methylation status. These scans are a collection of data from existing TCIA collections, but also cases provided by individual institutions and willing to share with a cc-by license.\\r\\n\\r\\nThe BraTS dataset describes a retrospective collection of brain tumor structural mpMRI scans of 2,040 patients (1,480 here), acquired from multiple different institutions under standard clinical conditions, but with different equipment and imaging protocols, resulting in a vastly heterogeneous image quality reflecting diverse clinical practice across different institutions. The 4 structural mpMRI scans included in the BraTS challenge describe a) native (T1) and b) post-contrast T1-weighted (T1Gd (Gadolinium)), c) T2-weighted (T2), and d) T2 Fluid Attenuated Inversion Recovery (T2-FLAIR) volumes, acquired with different protocols and various scanners from multiple institutions. Furthermore, data on the O[6]-methylguanine-DNA methyltransferase (MGMT) promoter methylation status is provided as a binary label. Notably, MGMT is a DNA repair enzyme that the methylation of its promoter in newly diagnosed glioblastoma has been identified as a favorable prognostic factor and a predictor of chemotherapy response.\\r\\n\\r\\nIt is curated for computational image analysis of segmentation and prediction of the MGMT promoter methylation status.\\r\\nA note about available TCIA data which were\\u00a0converted for use in this Challenge: (Training, Validation, Test)\\r\\nDr. Bakas's group here provides brain-extracted Segmentation task BraTS 2021 challenge TRAINING and VALIDATION set data in NIfTI that do not pose DUA-level risk of potential facial reidentification, and segmentations to go with them.\\r\\nThis group has provided some of the brain-extracted BraTS challenge TEST data in NIfTI, and segmentations to go with them (here and here, from the 2018 challenge, request via TCIA's Helpdesk.\\r\\n\\r\\nThis group here provides brain-extracted Classification task BraTS 2021 challenge TRAINING and VALIDATION set data includes DICOM\\u2192 NIfTI\\u2192 dcm files, registered to original orientation, data files that do not strictly adhere to the DICOM standard. BraTS 2021 Classification challenge TEST files are unavailable at this time.\\r\\n\\r\\nYou may want the original corresponding DICOM-format files drawn from TCIA Collections; please note that these original data are not brain-extracted and may pose enough reidentification risk that TCIA must keep them behind an explicit usage agreement.\\r\\n\\r\\nPlease also note that specificity of which exact series in DICOM became which exact volume in NIfTI has, unfortunately, been lost to time but the available lists below represent our best effort at reconstructing the link to the BraTS source files.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/BRATS_banner_noCaption.png\",\"program\":\"Community\",\"cancer_types\":[\"Glioma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"Segmentation\",\"MR\",\"Other\"],\"supporting_data\":[\"Tumor segmentations\"],\"species\":[],\"subjects\":1480,\"date_updated\":\"2023-08-25\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/faspex.cancerimagingarchive.net\\\/aspera\\\/faspex\\\/public\\\/package?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjYzNiIsInBhc3Njb2RlIjoiNDM5YTVhZjM3NGRhYjk3OGExYjExMzA4MTcyZDhlMDdkY2Q5OWMzMSIsInBhY2thZ2VfaWQiOiI2MzYiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\",\"blob\":\"rsna-asnr-miccai-brats-2021 rsna-asnr-miccai-brats-2021 this dataset includes brain mri scans of adult brain glioma patients, comprising of 4 structural modalities (i.e., t1, t1c, t2, t2-flair) and associated manually generated ground truth labels for each tumor sub-region (enhancement, necrosis, edema), as well as their mgmt promoter methylation status. these scans are a collection of data from existing tcia collections, but also cases provided by individual institutions and willing to share with a cc-by license.\\r\\n\\r\\nthe brats dataset describes a retrospective collection of brain tumor structural mpmri scans of 2,040 patients (1,480 here), acquired from multiple different institutions under standard clinical conditions, but with different equipment and imaging protocols, resulting in a vastly heterogeneous image quality reflecting diverse clinical practice across different institutions. the 4 structural mpmri scans included in the brats challenge describe a) native (t1) and b) post-contrast t1-weighted (t1gd (gadolinium)), c) t2-weighted (t2), and d) t2 fluid attenuated inversion recovery (t2-flair) volumes, acquired with different protocols and various scanners from multiple institutions. furthermore, data on the o[6]-methylguanine-dna methyltransferase (mgmt) promoter methylation status is provided as a binary label. notably, mgmt is a dna repair enzyme that the methylation of its promoter in newly diagnosed glioblastoma has been identified as a favorable prognostic factor and a predictor of chemotherapy response.\\r\\n\\r\\nit is curated for computational image analysis of segmentation and prediction of the mgmt promoter methylation status.\\r\\na note about available tcia data which were\\u00a0converted for use in this challenge: (training, validation, test)\\r\\ndr. bakas's group here provides brain-extracted segmentation task brats 2021 challenge training and validation set data in nifti that do not pose dua-level risk of potential facial reidentification, and segmentations to go with them.\\r\\nthis group has provided some of the brain-extracted brats challenge test data in nifti, and segmentations to go with them (here and here, from the 2018 challenge, request via tcia's helpdesk.\\r\\n\\r\\nthis group here provides brain-extracted classification task brats 2021 challenge training and validation set data includes dicom\\u2192 nifti\\u2192 dcm files, registered to original orientation, data files that do not strictly adhere to the dicom standard. brats 2021 classification challenge test files are unavailable at this time.\\r\\n\\r\\nyou may want the original corresponding dicom-format files drawn from tcia collections; please note that these original data are not brain-extracted and may pose enough reidentification risk that tcia must keep them behind an explicit usage agreement.\\r\\n\\r\\nplease also note that specificity of which exact series in dicom became which exact volume in nifti has, unfortunately, been lost to time but the available lists below represent our best effort at reconstructing the link to the brats source files. glioma brain segmentation mr other tumor segmentations community\"},{\"id\":47675,\"type\":\"analysis\",\"slug\":\"s0819-tumor-annotations\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/s0819-tumor-annotations\\\/\",\"title\":\"Annotations for A Randomized Phase III Study Comparing Carboplatin\\\/Paclitaxel or Carboplatin\\\/Paclitaxel\\\/Bevacizumab With or Without Concurrent Cetuximab in Patients With Advanced Non-small Cell Lung Cancer\",\"short_title\":\"S0819-Tumor-Annotations\",\"doi\":\"10.7937\\\/R0R8-BN93\",\"summary\":\"This dataset contains image annotations derived from the NCI Clinical Trial \\\"A Randomized Phase III Study Comparing Carboplatin\\\/Paclitaxel or Carboplatin\\\/Paclitaxel\\\/Bevacizumab With or Without Concurrent Cetuximab in Patients With Advanced Non-small Cell Lung Cancer\\u201d. \\u00a0This dataset was generated as part of an NCI project to augment TCIA datasets with annotations that will improve their value for cancer researchers and AI developers.\\r\\nAnnotation Protocol\\r\\nFor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. Scans were initially annotated by an international team of radiologists holding MBBS degrees or higher, which were then reviewed by US-based board-certified radiologists to ensure accuracy. In a typical patient, all available time points were annotated. Every exam from the first available time point was annotated. One additional time point was annotated for each patient. The clinical data in the NCTN Archive was utilized to help determine the first evidence of disease progression. The first time point demonstrating disease progression was annotated. If that document was not accurate and did not demonstrate disease progression, then later time points were reviewed to assess for disease progression and the first time point demonstrating disease progression was annotated. If there was no evidence of disease progression on any time point, then the last available time point was annotated. Again, every exam from each chosen time point was annotated. For example, if there was a CT and a PET\\\/CT, the PET was annotated along with one CT. If there was an MRI, that was annotated as well.\\r\\n\\r\\nThe following annotation rules were followed:\\r\\n\\r\\n \\tPERCIST criteria was followed for PET imaging. Specifically, the lesions estimated to have the most elevated SUVmax were annotated.\\r\\n \\tRECIST 1.1 was otherwise generally followed for any MR and CT imaging. A maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. The same 5 lesions were annotated at each time point. Lymph nodes were however annotated if > 1 cm in short axis. Other lesions were annotated if > 1 cm. If the primary lesion is < 1 cm, it was still annotated. If there was evidence of disease progression with new lesions then additional annotations were allowed to demonstrate that progression. A representative sample of the new lesions was annotated at the radiologist's discretion.\\r\\n \\tLesions were annotated in the axial plane. If no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tMRIs were annotated using the T1-weighted post contrast sequence, fat saturated if available. If not available, T2-weighted sequences were utilized.\\r\\n \\tCTs were annotated using the axial post contrast series. If not available, the non contrast series was annotated.\\r\\n \\tPET\\\/CTs were annotated on the CT and attenuation corrected PET images. However, if the post contrast CT was performed the same day as the PET\\\/CT, the non contrast CT portion of the PET\\\/CT was annotated.\\r\\n \\tLesions were labeled separately.\\r\\n \\tThe volume of each annotated lesion was calculated and reported in cubic centimeters [cc] in a dataset metadata report.\\r\\n \\tSeed points were automatically generated but reviewed by a radiologist.\\r\\n \\tA \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nAt each time point:\\r\\n\\r\\n \\tA seed point (kernel) was created for each segmented structure. The seed points for each segmentation are provided in a separate DICOM RTSS file.\\r\\n \\tSNOMED-CT \\u201cAnatomic Region Sequence\\u201d and \\u201cSegmented Property Category Code Sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201cTracking ID\\u201d and \\u201cTracking UID\\u201d tags were inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\tImaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cClinical Trial Time Point ID\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d\\r\\n \\tContent Item in \\u201cAcquisition Context Sequence\\u201d was added containing \\\"Time Point Type\\\" using Concept Code Sequence (0040,A168) selected from:\\r\\n\\r\\n \\t(255235001, SCT, \\u201cPre-dose\\u201d)\\r\\n \\t(262502001, SCT, \\\"Post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nImportant supplementary information and sample code\\r\\n\\r\\n \\tA spreadsheet containing key details about the annotations is available in the\\u00a0Data Access\\u00a0section below.\\r\\n \\tA\\u00a0Jupyter notebook demonstrating how to use the\\u00a0NBIA Data Retriever Command-Line Interface\\u00a0application and the\\u00a0REST API\\u00a0to access these data can be found in the\\u00a0Additional Resources\\u00a0section below.\",\"image\":false,\"program\":\"NCI Trials\",\"cancer_types\":[\"Lung Cancer\"],\"cancer_locations\":[\"Lung\"],\"data_types\":[\"RTSTRUCT\",\"Measurement\",\"Classification\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":1297,\"date_updated\":\"2024-11-09\",\"status\":\"Complete\",\"license\":\"NIH Controlled Data Access Policy\",\"search_url\":\"https:\\\/\\\/nbia.cancerimagingarchive.net\\\/nbia-search\\\/?ImageModalityCriteria=RTSTRUCT&MinNumberOfStudiesCriteria=1&DateRange=11\\\/08\\\/2024-11\\\/09\\\/2024&CollectionCriteria=S0819&ThirdPartyCriteria=YES\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/S0819_Tumor-Annotations-manifest-v3_11-09-2024.tcia\",\"blob\":\"annotations for a randomized phase iii study comparing carboplatin\\\/paclitaxel or carboplatin\\\/paclitaxel\\\/bevacizumab with or without concurrent cetuximab in patients with advanced non-small cell lung cancer s0819-tumor-annotations this dataset contains image annotations derived from the nci clinical trial \\\"a randomized phase iii study comparing carboplatin\\\/paclitaxel or carboplatin\\\/paclitaxel\\\/bevacizumab with or without concurrent cetuximab in patients with advanced non-small cell lung cancer\\u201d. \\u00a0this dataset was generated as part of an nci project to augment tcia datasets with annotations that will improve their value for cancer researchers and ai developers.\\r\\nannotation protocol\\r\\nfor each patient, all scans were reviewed to identify and annotate the clinically relevant time points and sequences\\\/series. scans were initially annotated by an international team of radiologists holding mbbs degrees or higher, which were then reviewed by us-based board-certified radiologists to ensure accuracy. in a typical patient, all available time points were annotated. every exam from the first available time point was annotated. one additional time point was annotated for each patient. the clinical data in the nctn archive was utilized to help determine the first evidence of disease progression. the first time point demonstrating disease progression was annotated. if that document was not accurate and did not demonstrate disease progression, then later time points were reviewed to assess for disease progression and the first time point demonstrating disease progression was annotated. if there was no evidence of disease progression on any time point, then the last available time point was annotated. again, every exam from each chosen time point was annotated. for example, if there was a ct and a pet\\\/ct, the pet was annotated along with one ct. if there was an mri, that was annotated as well.\\r\\n\\r\\nthe following annotation rules were followed:\\r\\n\\r\\n \\tpercist criteria was followed for pet imaging. specifically, the lesions estimated to have the most elevated suvmax were annotated.\\r\\n \\trecist 1.1 was otherwise generally followed for any mr and ct imaging. a maximum of 5 lesions were annotated per patient scan (timepoint); no more than 2 per organ. the same 5 lesions were annotated at each time point. lymph nodes were however annotated if > 1 cm in short axis. other lesions were annotated if > 1 cm. if the primary lesion is < 1 cm, it was still annotated. if there was evidence of disease progression with new lesions then additional annotations were allowed to demonstrate that progression. a representative sample of the new lesions was annotated at the radiologist's discretion.\\r\\n \\tlesions were annotated in the axial plane. if no axial plane was available, lesions were annotated in the coronal plane.\\r\\n \\tmris were annotated using the t1-weighted post contrast sequence, fat saturated if available. if not available, t2-weighted sequences were utilized.\\r\\n \\tcts were annotated using the axial post contrast series. if not available, the non contrast series was annotated.\\r\\n \\tpet\\\/cts were annotated on the ct and attenuation corrected pet images. however, if the post contrast ct was performed the same day as the pet\\\/ct, the non contrast ct portion of the pet\\\/ct was annotated.\\r\\n \\tlesions were labeled separately.\\r\\n \\tthe volume of each annotated lesion was calculated and reported in cubic centimeters [cc] in a dataset metadata report.\\r\\n \\tseed points were automatically generated but reviewed by a radiologist.\\r\\n \\ta \\u201cnegative\\u201d annotation was created for any exam without findings.\\r\\n\\r\\nat each time point:\\r\\n\\r\\n \\ta seed point (kernel) was created for each segmented structure. the seed points for each segmentation are provided in a separate dicom rtss file.\\r\\n \\tsnomed-ct \\u201canatomic region sequence\\u201d and \\u201csegmented property category code sequence\\u201d and codes were inserted for all segmented structures.\\r\\n \\t\\u201ctracking id\\u201d and \\u201ctracking uid\\u201d tags were inserted for each segmented structure to enable longitudinal lesion tracking.\\r\\n \\timaging time point codes were inserted to help identify each annotation in the context of the clinical trial assessment protocol.\\r\\n\\r\\n \\t\\u201cclinical trial time point id\\u201d was used to encode time point type using one of the following strings as applicable: \\u201cpre-dose\\u201d or \\u201cpost-chemotherapy\\u201d\\r\\n \\tcontent item in \\u201cacquisition context sequence\\u201d was added containing \\\"time point type\\\" using concept code sequence (0040,a168) selected from:\\r\\n\\r\\n \\t(255235001, sct, \\u201cpre-dose\\u201d)\\r\\n \\t(262502001, sct, \\\"post-chemotherapy\\\")\\r\\n\\r\\n\\r\\n\\r\\n\\r\\n\\r\\nimportant supplementary information and sample code\\r\\n\\r\\n \\ta spreadsheet containing key details about the annotations is available in the\\u00a0data access\\u00a0section below.\\r\\n \\ta\\u00a0jupyter notebook demonstrating how to use the\\u00a0nbia data retriever command-line interface\\u00a0application and the\\u00a0rest api\\u00a0to access these data can be found in the\\u00a0additional resources\\u00a0section below. lung cancer lung rtstruct measurement classification nci trials\"},{\"id\":46301,\"type\":\"analysis\",\"slug\":\"saros\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/saros\\\/\",\"title\":\"SAROS - A large, heterogeneous, and sparsely annotated segmentation dataset on CT imaging data\",\"short_title\":\"SAROS\",\"doi\":\"10.25737\\\/SZ96-ZG60\",\"summary\":\"Sparsely Annotated Region and Organ Segmentation (SAROS) contributes a large heterogeneous semantic segmentation annotation dataset for existing CT imaging cases on TCIA. The goal of this dataset is to provide high-quality annotations for building body composition analysis tools (References: Koitka 2020\\u00a0and Haubold 2023). Existing in-house segmentation models were employed to generate annotation candidates on randomly selected cases. All generated annotations were manually reviewed and corrected by medical residents and students on every fifth axial slice while other slices were set to an ignore label (numeric value 255).\\r\\n\\r\\n900 CT series from 882 patients were randomly selected from the following TCIA collections (number of CTs per collection in parenthesis):\\u00a0 ACRIN-FLT-Breast (32), ACRIN-HNSCC-FDG-PET\\\/CT (48), ACRIN-NSCLC-FDG-PET (129), Anti-PD-1_Lung (12), Anti-PD-1_MELANOMA (2), C4KC-KiTS (175), COVID-19-NY-SBU (1), CPTAC-CM (1), CPTAC-LSCC (3), CPTAC-LUAD (1), CPTAC-PDA (8), CPTAC-UCEC (26), HNSCC (17), Head-Neck Cetuximab (12), LIDC-IDRI (133), Lung-PET-CT-Dx (17), NSCLC Radiogenomics (7), NSCLC-Radiomics (56), NSCLC-Radiomics-Genomics (20), Pancreas-CT (58), QIN-HEADNECK (94), Soft-tissue-Sarcoma (6), TCGA-HNSC (1), TCGA-LIHC (33), TCGA-LUAD (2), TCGA-LUSC (3), TCGA-STAD (2), TCGA-UCEC (1).\\r\\n\\r\\nA script to download and resample the images is provided in our GitHub repository: https:\\\/\\\/github.com\\\/UMEssen\\\/saros-dataset\\r\\n\\r\\nThe annotations are provided in NIfTI format and were performed on 5mm slice thickness. The annotation files define foreground labels on the same axial slices and match pixel-perfect. In total, 13 semantic body regions and 6 body part\\u00a0labels were annotated with an index that corresponds to a numeric value in the segmentation file.\\u00a0\\r\\nBody Regions\\r\\n\\r\\n \\tSubcutaneous Tissue\\r\\n \\tMuscle\\r\\n \\tAbdominal Cavity\\r\\n \\tThoracic Cavity\\r\\n \\tBones\\r\\n \\tParotid Glands\\r\\n \\tPericardium\\r\\n \\tBreast Implant\\r\\n \\tMediastinum\\r\\n \\tBrain\\r\\n \\tSpinal Cord\\r\\n \\tThyroid Glands\\r\\n \\tSubmandibular Glands\\r\\n\\r\\nBody Parts\\r\\n\\r\\n \\tTorso\\r\\n \\tHead\\r\\n \\tRight Leg\\r\\n \\tLeft Leg\\r\\n \\tRight Arm\\r\\n \\tLeft Arm\\r\\n\\r\\nThe labels which were modified or require further commentary are listed and explained below:\\r\\n\\r\\n \\tSubcutaneous Adipose Tissue: The cutis was included into this label due to its limited differentiation in 5mm-CT.\\r\\n \\tMuscle: All muscular tissue was segmented contiguously and not separated into single muscles. Thus, fascias and intermuscular fat were included into the label. Inter- and intramuscular fat is subtracted automatically in the process.\\r\\n \\tAbdominal Cavity: This label includes the pelvis. The label does not separate between the positional relationships of the peritoneum.\\r\\n \\tMediastinum:\\u00a0The International Thymic Malignancy Group\\u00a0(ITMIG) scheme was used for the segmentation guidelines.\\r\\n \\tHead + Neck: The neck is confined by the base of the trapezius muscle.\\r\\n \\tRight + Left Leg: The legs are separated from the torso by the line between the two lowest points of the Rami ossa pubis.\\r\\n \\tRight + Left Arm: The arms are separated from the torso by the diagonal between the most lateral point of the acromion and the tuberculum infraglenoidale.\\r\\n\\r\\nFor reproducibility on downstream tasks, five cross-validation folds and a test set were pre-defined and are described in the provided spreadsheet. Segmentation was conducted strictly in accordance with anatomical guidelines and only modified if required for the gain of segmentation efficiency.\",\"image\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/SAROS-Collection-Photo.png\",\"program\":\"Community\",\"cancer_types\":[\"Adenocarcinoma\",\"Breast\",\"Corpus Endometrial Carcinoma\",\"COVID-19(non-cancer)\",\"Cutaneous Melanoma\",\"Ductal Adenocarcinoma\",\"Head and Neck Carcinomas\",\"Head and Neck Squamous Cell Carcinoma\",\"Healthy Controls (non-cancer)\",\"Kidney Cancer\",\"Liver Hepatocellular Carcinoma\",\"Lung Adenocarcinoma\",\"Lung Cancer\",\"Lung Squamous Cell Carcinoma\",\"Melanoma\",\"Non-small Cell Lung Cancer\",\"Soft-tissue Sarcoma\",\"Squamous Cell Carcinoma\",\"Stomach Adenocarcinoma\",\"Uterine Corpus Endometrial Carcinoma\"],\"cancer_locations\":[\"Breast\",\"Chest\",\"Extremities\",\"Head-Neck\",\"Kidney\",\"Liver\",\"Lung\",\"Pancreas\",\"Skin\",\"Stomach\",\"Uterus\"],\"data_types\":[\"Segmentation\",\"Radiomic Feature\"],\"supporting_data\":[\"Segmentations\"],\"species\":[\"Human\"],\"subjects\":882,\"date_updated\":\"2024-03-07\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/SAROS-Collection-NIfTI-files-v2_03-70-2024.zip\",\"blob\":\"saros - a large, heterogeneous, and sparsely annotated segmentation dataset on ct imaging data saros sparsely annotated region and organ segmentation (saros) contributes a large heterogeneous semantic segmentation annotation dataset for existing ct imaging cases on tcia. the goal of this dataset is to provide high-quality annotations for building body composition analysis tools (references: koitka 2020\\u00a0and haubold 2023). existing in-house segmentation models were employed to generate annotation candidates on randomly selected cases. all generated annotations were manually reviewed and corrected by medical residents and students on every fifth axial slice while other slices were set to an ignore label (numeric value 255).\\r\\n\\r\\n900 ct series from 882 patients were randomly selected from the following tcia collections (number of cts per collection in parenthesis):\\u00a0 acrin-flt-breast (32), acrin-hnscc-fdg-pet\\\/ct (48), acrin-nsclc-fdg-pet (129), anti-pd-1_lung (12), anti-pd-1_melanoma (2), c4kc-kits (175), covid-19-ny-sbu (1), cptac-cm (1), cptac-lscc (3), cptac-luad (1), cptac-pda (8), cptac-ucec (26), hnscc (17), head-neck cetuximab (12), lidc-idri (133), lung-pet-ct-dx (17), nsclc radiogenomics (7), nsclc-radiomics (56), nsclc-radiomics-genomics (20), pancreas-ct (58), qin-headneck (94), soft-tissue-sarcoma (6), tcga-hnsc (1), tcga-lihc (33), tcga-luad (2), tcga-lusc (3), tcga-stad (2), tcga-ucec (1).\\r\\n\\r\\na script to download and resample the images is provided in our github repository: https:\\\/\\\/github.com\\\/umessen\\\/saros-dataset\\r\\n\\r\\nthe annotations are provided in nifti format and were performed on 5mm slice thickness. the annotation files define foreground labels on the same axial slices and match pixel-perfect. in total, 13 semantic body regions and 6 body part\\u00a0labels were annotated with an index that corresponds to a numeric value in the segmentation file.\\u00a0\\r\\nbody regions\\r\\n\\r\\n \\tsubcutaneous tissue\\r\\n \\tmuscle\\r\\n \\tabdominal cavity\\r\\n \\tthoracic cavity\\r\\n \\tbones\\r\\n \\tparotid glands\\r\\n \\tpericardium\\r\\n \\tbreast implant\\r\\n \\tmediastinum\\r\\n \\tbrain\\r\\n \\tspinal cord\\r\\n \\tthyroid glands\\r\\n \\tsubmandibular glands\\r\\n\\r\\nbody parts\\r\\n\\r\\n \\ttorso\\r\\n \\thead\\r\\n \\tright leg\\r\\n \\tleft leg\\r\\n \\tright arm\\r\\n \\tleft arm\\r\\n\\r\\nthe labels which were modified or require further commentary are listed and explained below:\\r\\n\\r\\n \\tsubcutaneous adipose tissue: the cutis was included into this label due to its limited differentiation in 5mm-ct.\\r\\n \\tmuscle: all muscular tissue was segmented contiguously and not separated into single muscles. thus, fascias and intermuscular fat were included into the label. inter- and intramuscular fat is subtracted automatically in the process.\\r\\n \\tabdominal cavity: this label includes the pelvis. the label does not separate between the positional relationships of the peritoneum.\\r\\n \\tmediastinum:\\u00a0the international thymic malignancy group\\u00a0(itmig) scheme was used for the segmentation guidelines.\\r\\n \\thead + neck: the neck is confined by the base of the trapezius muscle.\\r\\n \\tright + left leg: the legs are separated from the torso by the line between the two lowest points of the rami ossa pubis.\\r\\n \\tright + left arm: the arms are separated from the torso by the diagonal between the most lateral point of the acromion and the tuberculum infraglenoidale.\\r\\n\\r\\nfor reproducibility on downstream tasks, five cross-validation folds and a test set were pre-defined and are described in the provided spreadsheet. segmentation was conducted strictly in accordance with anatomical guidelines and only modified if required for the gain of segmentation efficiency. adenocarcinoma breast corpus endometrial carcinoma covid-19(non-cancer) cutaneous melanoma ductal adenocarcinoma head and neck carcinomas head and neck squamous cell carcinoma healthy controls (non-cancer) kidney cancer liver hepatocellular carcinoma lung adenocarcinoma lung cancer lung squamous cell carcinoma melanoma non-small cell lung cancer soft-tissue sarcoma squamous cell carcinoma stomach adenocarcinoma uterine corpus endometrial carcinoma breast chest extremities head-neck kidney liver lung pancreas skin stomach uterus segmentation radiomic feature segmentations community\"},{\"id\":45635,\"type\":\"analysis\",\"slug\":\"tcga-breast-radiogenomics\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/tcga-breast-radiogenomics\\\/\",\"title\":\"TCGA Breast Phenotype Research Group Data sets\",\"short_title\":\"TCGA-Breast-Radiogenomics\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2014.8SIPIY6G\",\"summary\":\"At the time of our study, 108 cases with breast MRI data were available in the\\u00a0The Cancer Genome Atlas Breast Invasive Carcinoma Collection (TCGA-BRCA)\\u00a0collection. In order to minimize variations in image quality across the multi-institutional cases we included only breast MRI studies acquired on GE 1.5 Tesla magnet strength scanners (GE Medical Systems, Milwaukee, Wisconsin, USA) scanners, yielding a total of 93 cases. We then excluded cases that had missing images in the dynamic sequence (1 patient), or at the time did not have gene expression analysis available in the\\u00a0TCGA Data Portal (8 patients). After these criteria, a dataset of 84 breast cancer patients resulted, with MRIs from four institutions: Memorial Sloan Kettering Cancer Center, the Mayo Clinic, the University of Pittsburgh Medical Center, and the Roswell Park Cancer Institute. The resulting cases contributed by each institution were 9 (date range 1999-2002), 5 (1999-2003), 46 (1999-2004), and 24 (1999-2002), respectively. The dataset of biopsy proven invasive breast cancers included 74 (88%) ductal, 8 (10%) lobular, and 2 (2%) mixed. Of these, 73 (87%) were ER+, 67 (80%) were PR+, and 19 (23%) were HER2+.\\u00a0 Various types of analyses were conducted using the combined imaging, genomic, and clinical data.\\u00a0 Those analyses are described within several manuscripts created by the group (cited below).\\u00a0 Additional information about the methodology for how the Radiologist Annotations file can be found on the TCGA Breast Image Feature Scoring Project page.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Breast\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"Radiomic Feature\",\"Segmentation\",\"Classification\",\"Demographic\",\"Diagnosis\",\"Follow-Up\",\"Molecular Test\",\"Treatment\"],\"supporting_data\":[\"multi-gene assays\"],\"species\":[\"Human\"],\"subjects\":84,\"date_updated\":\"2018-09-04\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/tcga-breast-radiologist-reads.xls\",\"blob\":\"tcga breast phenotype research group data sets tcga-breast-radiogenomics at the time of our study, 108 cases with breast mri data were available in the\\u00a0the cancer genome atlas breast invasive carcinoma collection (tcga-brca)\\u00a0collection. in order to minimize variations in image quality across the multi-institutional cases we included only breast mri studies acquired on ge 1.5 tesla magnet strength scanners (ge medical systems, milwaukee, wisconsin, usa) scanners, yielding a total of 93 cases. we then excluded cases that had missing images in the dynamic sequence (1 patient), or at the time did not have gene expression analysis available in the\\u00a0tcga data portal (8 patients). after these criteria, a dataset of 84 breast cancer patients resulted, with mris from four institutions: memorial sloan kettering cancer center, the mayo clinic, the university of pittsburgh medical center, and the roswell park cancer institute. the resulting cases contributed by each institution were 9 (date range 1999-2002), 5 (1999-2003), 46 (1999-2004), and 24 (1999-2002), respectively. the dataset of biopsy proven invasive breast cancers included 74 (88%) ductal, 8 (10%) lobular, and 2 (2%) mixed. of these, 73 (87%) were er+, 67 (80%) were pr+, and 19 (23%) were her2+.\\u00a0 various types of analyses were conducted using the combined imaging, genomic, and clinical data.\\u00a0 those analyses are described within several manuscripts created by the group (cited below).\\u00a0 additional information about the methodology for how the radiologist annotations file can be found on the tcga breast image feature scoring project page. breast breast radiomic feature segmentation classification demographic diagnosis follow-up molecular test treatment multi-gene assays tcga\"},{\"id\":45571,\"type\":\"analysis\",\"slug\":\"tcga-gbm-qi-radiogenomics\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/tcga-gbm-qi-radiogenomics\\\/\",\"title\":\"Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features\",\"short_title\":\"TCGA-GBM-QI-Radiogenomics\",\"doi\":\"10.7937\\\/k9\\\/tcia.2014.rjeftjbu\",\"summary\":\"PURPOSE:\\r\\nTo derive\\u00a0quantitative\\u00a0image\\u00a0features\\u00a0from magnetic resonance (MR) images that characterize the radiographic phenotype of glioblastoma multiforme (GBM) lesions and to create\\u00a0radiogenomic\\u00a0maps associating these\\u00a0features\\u00a0with various molecular data.\\r\\nMATERIALS AND METHODS:\\r\\nClinical, molecular, and MR imaging data for GBMs in 55 patients were obtained from the\\u00a0The Cancer Genome Atlas Glioblastoma Multiforme Collection (TCGA-GBM) collection after local ethics committee and institutional review board approval. Regions of interest (ROIs) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn and saved in AIM format. Quantitative\\u00a0image\\u00a0features\\u00a0were derived from these ROIs. Robust\\u00a0quantitative\\u00a0image\\u00a0features\\u00a0were defined on the basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-retest\\u00a0analysis. The robust\\u00a0features\\u00a0were visualized by using hierarchic clustering and were correlated with survival by using Cox proportional hazards modeling. Next, these robust\\u00a0image\\u00a0features\\u00a0were correlated with manual radiologist annotations from the Visually Accessible Rembrandt Images (VASARI) feature set and GBM molecular subgroups by using nonparametric statistical tests. A bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the module's expression pattern. Modules were correlated with robust\\u00a0image\\u00a0features\\u00a0by using the Spearman correlation test to create\\u00a0radiogenomic\\u00a0maps and to link robust\\u00a0image\\u00a0features\\u00a0with molecular pathways.\\r\\nRESULTS:\\r\\nEighteen\\u00a0image\\u00a0features\\u00a0passed the robustness\\u00a0analysis\\u00a0and were further analyzed for the three types of ROIs, for a total of 54\\u00a0image\\u00a0features. Three enhancement\\u00a0features\\u00a0were significantly correlated with survival, 77 significant correlations were found between robust\\u00a0quantitative\\u00a0features\\u00a0and the VASARI feature set, and seven\\u00a0image\\u00a0features\\u00a0were correlated with molecular subgroups (P < .05 for all). A radiogenomics map was created to link\\u00a0image\\u00a0features\\u00a0with gene expression modules and allowed linkage of 56% (30 of 54) of the\\u00a0image\\u00a0features\\u00a0with biologic processes.\\r\\nCONCLUSION:\\r\\nRadiogenomic\\u00a0approaches in GBM have the potential to predict clinical and molecular characteristics of tumors noninvasively.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Glioblastoma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"Segmentation\",\"Classification\"],\"supporting_data\":[\"Tumor segmentations\"],\"species\":[\"Human\"],\"subjects\":55,\"date_updated\":\"2020-10-07\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/SupplementaryDataZIP_AIMfiles.zip\",\"blob\":\"glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features tcga-gbm-qi-radiogenomics purpose:\\r\\nto derive\\u00a0quantitative\\u00a0image\\u00a0features\\u00a0from magnetic resonance (mr) images that characterize the radiographic phenotype of glioblastoma multiforme (gbm) lesions and to create\\u00a0radiogenomic\\u00a0maps associating these\\u00a0features\\u00a0with various molecular data.\\r\\nmaterials and methods:\\r\\nclinical, molecular, and mr imaging data for gbms in 55 patients were obtained from the\\u00a0the cancer genome atlas glioblastoma multiforme collection (tcga-gbm) collection after local ethics committee and institutional review board approval. regions of interest (rois) corresponding to enhancing necrotic portions of tumor and peritumoral edema were drawn and saved in aim format. quantitative\\u00a0image\\u00a0features\\u00a0were derived from these rois. robust\\u00a0quantitative\\u00a0image\\u00a0features\\u00a0were defined on the basis of an intraclass correlation coefficient of 0.6 for a digital algorithmic modification and a test-retest\\u00a0analysis. the robust\\u00a0features\\u00a0were visualized by using hierarchic clustering and were correlated with survival by using cox proportional hazards modeling. next, these robust\\u00a0image\\u00a0features\\u00a0were correlated with manual radiologist annotations from the visually accessible rembrandt images (vasari) feature set and gbm molecular subgroups by using nonparametric statistical tests. a bioinformatic algorithm was used to create gene expression modules, defined as a set of coexpressed genes together with a multivariate model of cancer driver genes predictive of the module's expression pattern. modules were correlated with robust\\u00a0image\\u00a0features\\u00a0by using the spearman correlation test to create\\u00a0radiogenomic\\u00a0maps and to link robust\\u00a0image\\u00a0features\\u00a0with molecular pathways.\\r\\nresults:\\r\\neighteen\\u00a0image\\u00a0features\\u00a0passed the robustness\\u00a0analysis\\u00a0and were further analyzed for the three types of rois, for a total of 54\\u00a0image\\u00a0features. three enhancement\\u00a0features\\u00a0were significantly correlated with survival, 77 significant correlations were found between robust\\u00a0quantitative\\u00a0features\\u00a0and the vasari feature set, and seven\\u00a0image\\u00a0features\\u00a0were correlated with molecular subgroups (p < .05 for all). a radiogenomics map was created to link\\u00a0image\\u00a0features\\u00a0with gene expression modules and allowed linkage of 56% (30 of 54) of the\\u00a0image\\u00a0features\\u00a0with biologic processes.\\r\\nconclusion:\\r\\nradiogenomic\\u00a0approaches in gbm have the potential to predict clinical and molecular characteristics of tumors noninvasively. glioblastoma brain segmentation classification tumor segmentations community\"},{\"id\":45601,\"type\":\"analysis\",\"slug\":\"tcga-gbm-radiogenomics\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/tcga-gbm-radiogenomics\\\/\",\"title\":\"MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set\",\"short_title\":\"TCGA-GBM-Radiogenomics\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2014.4HTXYRCN\",\"summary\":\"PURPOSE:\\r\\nTo conduct a comprehensive analysis of radiologist-made assessments of glioblastoma (GBM) tumor size and composition by using a community-developed controlled terminology of magnetic resonance (MR) imaging visual features as they relate to genetic alterations, gene expression class, and patient survival.\\r\\nMATERIALS AND METHODS:\\r\\nBecause all study patients had been previously deidentified by the Cancer Genome Atlas (TCGA), a publicly available data set that contains no linkage to patient identifiers and that is HIPAA compliant, no institutional review board approval was required. Presurgical MR images of 75 patients with GBM with genetic data in the TCGA portal were rated by three neuroradiologists for size, location, and tumor morphology by using a standardized feature set. Interrater agreements were analyzed by using the Krippendorff \\u03b1 statistic and intraclass correlation coefficient. Associations between survival, tumor size, and morphology were determined by using multivariate Cox regression models; associations between imaging features and genomics were studied by using the Fisher exact test.\\r\\nRESULTS:\\r\\nInterrater analysis showed significant agreement in terms of contrast material enhancement, nonenhancement, necrosis, edema, and size variables. Contrast-enhanced tumor volume and longest axis length of tumor were strongly associated with poor survival (respectively, hazard ratio: 8.84, P = .0253, and hazard ratio: 1.02, P = .00973), even after adjusting for Karnofsky performance score (P = .0208). Proneural class GBM had significantly lower levels of contrast enhancement (P = .02) than other subtypes, while mesenchymal GBM showed lower levels of nonenhanced tumor (P < .01).\\r\\nCONCLUSION:\\r\\nThis analysis demonstrates a method for consistent image feature annotation capable of reproducibly characterizing brain tumors; this study shows that radiologists' estimations of macroscopic imaging features can be combined with genetic alterations and gene expression subtypes to provide deeper insight to the underlying biologic properties of GBM subsets.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Glioblastoma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"Measurement\",\"Molecular Test\",\"Other\",\"Demographic\",\"Follow-Up\",\"Treatment\",\"Diagnosis\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":75,\"date_updated\":\"2014-11-12\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/Gutman2012_Supp.xlsx\",\"blob\":\"mr imaging predictors of molecular profile and survival: multi-institutional study of the tcga glioblastoma data set tcga-gbm-radiogenomics purpose:\\r\\nto conduct a comprehensive analysis of radiologist-made assessments of glioblastoma (gbm) tumor size and composition by using a community-developed controlled terminology of magnetic resonance (mr) imaging visual features as they relate to genetic alterations, gene expression class, and patient survival.\\r\\nmaterials and methods:\\r\\nbecause all study patients had been previously deidentified by the cancer genome atlas (tcga), a publicly available data set that contains no linkage to patient identifiers and that is hipaa compliant, no institutional review board approval was required. presurgical mr images of 75 patients with gbm with genetic data in the tcga portal were rated by three neuroradiologists for size, location, and tumor morphology by using a standardized feature set. interrater agreements were analyzed by using the krippendorff \\u03b1 statistic and intraclass correlation coefficient. associations between survival, tumor size, and morphology were determined by using multivariate cox regression models; associations between imaging features and genomics were studied by using the fisher exact test.\\r\\nresults:\\r\\ninterrater analysis showed significant agreement in terms of contrast material enhancement, nonenhancement, necrosis, edema, and size variables. contrast-enhanced tumor volume and longest axis length of tumor were strongly associated with poor survival (respectively, hazard ratio: 8.84, p = .0253, and hazard ratio: 1.02, p = .00973), even after adjusting for karnofsky performance score (p = .0208). proneural class gbm had significantly lower levels of contrast enhancement (p = .02) than other subtypes, while mesenchymal gbm showed lower levels of nonenhanced tumor (p < .01).\\r\\nconclusion:\\r\\nthis analysis demonstrates a method for consistent image feature annotation capable of reproducibly characterizing brain tumors; this study shows that radiologists' estimations of macroscopic imaging features can be combined with genetic alterations and gene expression subtypes to provide deeper insight to the underlying biologic properties of gbm subsets. glioblastoma brain measurement molecular test other demographic follow-up treatment diagnosis tcga\"},{\"id\":45581,\"type\":\"analysis\",\"slug\":\"tcga-kirc-radiogenomics\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/tcga-kirc-radiogenomics\\\/\",\"title\":\"Radiogenomics of Clear Cell Renal Cell Carcinoma: Preliminary Findings of The Cancer Genome Atlas-Renal Cell Carcinoma (TCGA-RCC) Research Group\",\"short_title\":\"TCGA-KIRC-Radiogenomics\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2014.K6M61GDW\",\"summary\":\"PURPOSE:\\r\\nTo investigate associations between imaging features and mutational status of clear cell renal cell carcinoma (ccRCC).\\r\\nMATERIALS AND METHODS:\\r\\nThis multi-institutional, multi-reader study included 103 patients (77 men; median age 59 years, range 34-79) with ccRCC examined with CT in 81 patients, MRI in 19, and both CT and MRI in three; images were downloaded from The Cancer Imaging Archive, an NCI-funded project for genome-mapping and analyses. Imaging features [size (mm), margin (well-defined or ill-defined), composition (solid or cystic), necrosis (for solid tumors: 0%, 1%-33%, 34%-66% or >66%), growth pattern (endophytic, <50% exophytic, or \\u226550% exophytic), and calcification (present, absent, or indeterminate)] were reviewed independently by three readers blinded to mutational data. The association of imaging features with mutational status (VHL, BAP1, PBRM1, SETD2, KDM5C, and MUC4) was assessed.\\r\\nRESULTS:\\r\\nMedian tumor size was 49 mm (range 14-162 mm), 73 (71%) tumors had well-defined margins, 98 (95%) tumors were solid, 95 (92%) showed presence of necrosis, 46 (45%) had \\u226550% exophytic component, and 18 (19.8%) had calcification. VHL (n = 52) and PBRM1 (n = 24) were the most common mutations. BAP1 mutation was associated with ill-defined margin and presence of calcification (p = 0.02 and 0.002, respectively, Pearson's x2 test); MUC4 mutation was associated with an exophytic growth pattern (p = 0.002, Mann-Whitney U test).\\r\\nCONCLUSIONS:\\r\\nBAP1 mutation was associated with ill-defined tumor margins and presence of calcification; MUC4 mutation was associated with exophytic growth. Given the known prognostic implications of BAP1 and MUC4 mutations, these results support using radiogenomics to aid in prognostication and management.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"Renal Clear Cell Carcinoma\"],\"cancer_locations\":[\"Kidney\"],\"data_types\":[\"Demographic\",\"Measurement\",\"Molecular Test\",\"Classification\"],\"supporting_data\":[\"Radiologist assessments of image features\"],\"species\":[],\"subjects\":103,\"date_updated\":\"2015-05-28\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/FullDataforAnalysis071714.csv\",\"blob\":\"radiogenomics of clear cell renal cell carcinoma: preliminary findings of the cancer genome atlas-renal cell carcinoma (tcga-rcc) research group tcga-kirc-radiogenomics purpose:\\r\\nto investigate associations between imaging features and mutational status of clear cell renal cell carcinoma (ccrcc).\\r\\nmaterials and methods:\\r\\nthis multi-institutional, multi-reader study included 103 patients (77 men; median age 59 years, range 34-79) with ccrcc examined with ct in 81 patients, mri in 19, and both ct and mri in three; images were downloaded from the cancer imaging archive, an nci-funded project for genome-mapping and analyses. imaging features [size (mm), margin (well-defined or ill-defined), composition (solid or cystic), necrosis (for solid tumors: 0%, 1%-33%, 34%-66% or >66%), growth pattern (endophytic, <50% exophytic, or \\u226550% exophytic), and calcification (present, absent, or indeterminate)] were reviewed independently by three readers blinded to mutational data. the association of imaging features with mutational status (vhl, bap1, pbrm1, setd2, kdm5c, and muc4) was assessed.\\r\\nresults:\\r\\nmedian tumor size was 49 mm (range 14-162 mm), 73 (71%) tumors had well-defined margins, 98 (95%) tumors were solid, 95 (92%) showed presence of necrosis, 46 (45%) had \\u226550% exophytic component, and 18 (19.8%) had calcification. vhl (n = 52) and pbrm1 (n = 24) were the most common mutations. bap1 mutation was associated with ill-defined margin and presence of calcification (p = 0.02 and 0.002, respectively, pearson's x2 test); muc4 mutation was associated with an exophytic growth pattern (p = 0.002, mann-whitney u test).\\r\\nconclusions:\\r\\nbap1 mutation was associated with ill-defined tumor margins and presence of calcification; muc4 mutation was associated with exophytic growth. given the known prognostic implications of bap1 and muc4 mutations, these results support using radiogenomics to aid in prognostication and management. renal clear cell carcinoma kidney demographic measurement molecular test classification radiologist assessments of image features tcga\"},{\"id\":45755,\"type\":\"analysis\",\"slug\":\"tcga-lgg-mask\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/tcga-lgg-mask\\\/\",\"title\":\"ROI Masks Defining Low-Grade Glioma Tumor Regions In the TCGA-LGG Image Collection\",\"short_title\":\"TCGA-LGG-Mask\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2017.BD7SGWCA\",\"summary\":\"This collection contains 406 ROI masks in MATLAB format defining the low grade glioma (LGG) tumour region on T1-weighted (T1W), T2-weighted (T2W), T1-weighted post-contrast (T1CE) and T2-flair (T2F) MR images of 108 different patients from the TCGA-LGG collection. From this subset of 108 patients, 81 patients have ROI masks drawn for the four MRI sequences (T1W, T2W, T1CE and T2F), and 27 patients have ROI masks drawn for three or less of the four MRI sequences. The ROI masks were used to extract texture features in order to develop radiomic-based multivariable models for the prediction of isocitrate dehydrogenase 1 (IDH1) mutation, 1p\\\/19q codeletion status, histological grade and tumour progression.\\r\\n\\r\\nClinical data (188 patients in total from the TCGA-LGG collection, some incomplete depending on the clinical attribute), VASARI scores (188 patients in total from the TCGA-LGG collection, 178 complete) with feature keys, and source code used in this study are also available with this collection. Please contact Martin Valli\\u00e8res (mart.vallieres@gmail.com) of the Medical Physics Unit of McGill University for any scientific inquiries about this dataset.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Low Grade Glioma\"],\"cancer_locations\":[\"Brain\"],\"data_types\":[\"Segmentation\",\"Molecular Test\",\"Follow-Up\",\"Diagnosis\",\"Radiomic Feature\",\"Other\"],\"supporting_data\":[\"Software\\\/Source Code\"],\"species\":[\"Human\"],\"subjects\":188,\"date_updated\":\"2017-03-17\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/ROIdata-TCGA-LGG-20170131.zip\",\"blob\":\"roi masks defining low-grade glioma tumor regions in the tcga-lgg image collection tcga-lgg-mask this collection contains 406 roi masks in matlab format defining the low grade glioma (lgg) tumour region on t1-weighted (t1w), t2-weighted (t2w), t1-weighted post-contrast (t1ce) and t2-flair (t2f) mr images of 108 different patients from the tcga-lgg collection. from this subset of 108 patients, 81 patients have roi masks drawn for the four mri sequences (t1w, t2w, t1ce and t2f), and 27 patients have roi masks drawn for three or less of the four mri sequences. the roi masks were used to extract texture features in order to develop radiomic-based multivariable models for the prediction of isocitrate dehydrogenase 1 (idh1) mutation, 1p\\\/19q codeletion status, histological grade and tumour progression.\\r\\n\\r\\nclinical data (188 patients in total from the tcga-lgg collection, some incomplete depending on the clinical attribute), vasari scores (188 patients in total from the tcga-lgg collection, 178 complete) with feature keys, and source code used in this study are also available with this collection. please contact martin valli\\u00e8res (mart.vallieres@gmail.com) of the medical physics unit of mcgill university for any scientific inquiries about this dataset. low grade glioma brain segmentation molecular test follow-up diagnosis radiomic feature other software\\\/source code community\"},{\"id\":45891,\"type\":\"analysis\",\"slug\":\"tcga-ov-proteogenomics\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/tcga-ov-proteogenomics\\\/\",\"title\":\"Integration of CT-based Qualitative and Radiomic Features with Proteomic Variables in Patients with High-Grade Serous Ovarian Cancer: An Exploratory Analysis\",\"short_title\":\"TCGA-OV-Proteogenomics\",\"doi\":\"10.7937\\\/TCIA.2019.9stoinf1\",\"summary\":\"Objectives\\r\\n\\r\\nTo investigate the association between CT imaging traits and texture metrics with proteomic data in patients with high-grade serous ovarian cancer (HGSOC).\\r\\n\\r\\nMethods\\r\\n\\r\\nThis retrospective, hypothesis-generating study included 20 The Cancer Genome Atlas Ovarian Cancer Collection (TCGA-OV) patients with HGSOC prior to primary cytoreductive surgery. Two readers independently assessed the contrast-enhanced computed tomography (CT) images and extracted 33 imaging traits, with a third reader adjudicating in the event of a disagreement. In addition, all sites of suspected HGSOC were manually segmented texture features were computed from each tumour site. Three texture features that represented intra-and inter-site tumour heterogeneity were used for analysis. An integrated analysis of transcriptomic and proteomic data identified proteins with conserved expression between primary tumour sites and metastasis. Correlations between protein-abundance and various CT imaging traits and texture features were assessed using the Kendall tau rank correlation-coefficient and the Mann-Whitney U test, whereas the area under the receiver-operating characteristic curve (AUC) was reported as a metric of the strength and the direction of the association. p values < 0.05 were considered significant.\\r\\n\\r\\nResults\\r\\n\\r\\nFour proteins were associated with CT-based imaging traits, with the strongest correlation observed between the CRIP2 protein and disease in the mesentery (p<0.001, AUC=0.05). The abundance of three proteins was associated with texture features that represented intra-and inter-site tumour heterogeneity, with the strongest negative correlation between the CKB protein and cluster dissimilarity (p=0.047, t =0.326).\\r\\n\\r\\nConclusion\\r\\n\\r\\nThis study provides the first insights into the potential associations between standard-of-care CT imaging traits and texture measures of intra-and inter-site heterogeneity, and the abundance of several proteins.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"High grade serous ovarian cancer\"],\"cancer_locations\":[\"Ovary\"],\"data_types\":[\"Molecular Test\",\"Measurement\",\"Follow-Up\",\"Classification\"],\"supporting_data\":[\"Radiologist assessments of image features\",\"proteogenomic features\",\"Clinical\"],\"species\":[],\"subjects\":20,\"date_updated\":\"2020-04-06\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/2020_02_26_HGSOC_proteomics.csv\",\"blob\":\"integration of ct-based qualitative and radiomic features with proteomic variables in patients with high-grade serous ovarian cancer: an exploratory analysis tcga-ov-proteogenomics objectives\\r\\n\\r\\nto investigate the association between ct imaging traits and texture metrics with proteomic data in patients with high-grade serous ovarian cancer (hgsoc).\\r\\n\\r\\nmethods\\r\\n\\r\\nthis retrospective, hypothesis-generating study included 20 the cancer genome atlas ovarian cancer collection (tcga-ov) patients with hgsoc prior to primary cytoreductive surgery. two readers independently assessed the contrast-enhanced computed tomography (ct) images and extracted 33 imaging traits, with a third reader adjudicating in the event of a disagreement. in addition, all sites of suspected hgsoc were manually segmented texture features were computed from each tumour site. three texture features that represented intra-and inter-site tumour heterogeneity were used for analysis. an integrated analysis of transcriptomic and proteomic data identified proteins with conserved expression between primary tumour sites and metastasis. correlations between protein-abundance and various ct imaging traits and texture features were assessed using the kendall tau rank correlation-coefficient and the mann-whitney u test, whereas the area under the receiver-operating characteristic curve (auc) was reported as a metric of the strength and the direction of the association. p values < 0.05 were considered significant.\\r\\n\\r\\nresults\\r\\n\\r\\nfour proteins were associated with ct-based imaging traits, with the strongest correlation observed between the crip2 protein and disease in the mesentery (p<0.001, auc=0.05). the abundance of three proteins was associated with texture features that represented intra-and inter-site tumour heterogeneity, with the strongest negative correlation between the ckb protein and cluster dissimilarity (p=0.047, t =0.326).\\r\\n\\r\\nconclusion\\r\\n\\r\\nthis study provides the first insights into the potential associations between standard-of-care ct imaging traits and texture measures of intra-and inter-site heterogeneity, and the abundance of several proteins. high grade serous ovarian cancer ovary molecular test measurement follow-up classification radiologist assessments of image features proteogenomic features clinical community\"},{\"id\":45699,\"type\":\"analysis\",\"slug\":\"tcga-ov-radiogenomics\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/tcga-ov-radiogenomics\\\/\",\"title\":\"Imaging Features, and Correlations with Genomic and Clinical Data from the TCGA Ovarian Radiology Research Group\",\"short_title\":\"TCGA-OV-Radiogenomics\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2016.PSJOXM47\",\"summary\":\"This study was a multi-reader, multi-institutional, IRB-approved retrospective analysis of 93 HGSOC patients with abdominal and pelvic CT scans prior to primary debulking that were available through The Cancer Imaging Archive (TCIA). Eight radiologists from The Cancer Genome Atlas-Ovarian Cancer (TCGA-OV) Imaging Research Group developed and subsequently independently recorded the following CT features in each patient: primary ovarian mass(es) characteristics (if present), presence and distribution of peritoneal tumor spread, lymphadenopathy, and distant metastases. Inter-observer agreement for the CT features was assessed, as were associations of these features with time-to-disease progression (TTP) and CLOVAR subtypes and abilities of combinations of these features to predict TTP and CLOVAR subtypes. Results of analyzing this data are published in a manuscript titled\\u00a0Radiogenomics of High-Grade Serous Ovarian Cancer: Multi- Reader Multi-Institutional Study from The Cancer Genome Atlas-Ovarian Cancer (TCGA-OV) Imaging Research Group.\",\"image\":false,\"program\":\"TCGA\",\"cancer_types\":[\"High grade serous ovarian cancer\"],\"cancer_locations\":[\"Ovary\"],\"data_types\":[\"Classification\",\"Follow-Up\",\"Measurement\"],\"supporting_data\":[],\"species\":[\"Human\"],\"subjects\":93,\"date_updated\":\"2016-08-02\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/CompleteImageFeatureData012116.csv\",\"blob\":\"imaging features, and correlations with genomic and clinical data from the tcga ovarian radiology research group tcga-ov-radiogenomics this study was a multi-reader, multi-institutional, irb-approved retrospective analysis of 93 hgsoc patients with abdominal and pelvic ct scans prior to primary debulking that were available through the cancer imaging archive (tcia). eight radiologists from the cancer genome atlas-ovarian cancer (tcga-ov) imaging research group developed and subsequently independently recorded the following ct features in each patient: primary ovarian mass(es) characteristics (if present), presence and distribution of peritoneal tumor spread, lymphadenopathy, and distant metastases. inter-observer agreement for the ct features was assessed, as were associations of these features with time-to-disease progression (ttp) and clovar subtypes and abilities of combinations of these features to predict ttp and clovar subtypes. results of analyzing this data are published in a manuscript titled\\u00a0radiogenomics of high-grade serous ovarian cancer: multi- reader multi-institutional study from the cancer genome atlas-ovarian cancer (tcga-ov) imaging research group. high grade serous ovarian cancer ovary classification follow-up measurement tcga\"},{\"id\":45803,\"type\":\"analysis\",\"slug\":\"til-wsi-tcga\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/til-wsi-tcga\\\/\",\"title\":\"Tumor-Infiltrating Lymphocytes Maps from TCGA H&E Whole Slide Pathology Images\",\"short_title\":\"TIL-WSI-TCGA\",\"doi\":\"10.7937\\\/K9\\\/TCIA.2018.Y75F9W1\",\"summary\":\"Mappings of tumor-infiltrating lymphocytes (TILs), based on H&E images from 13 of The Cancer Genome Atlas (TCGA) tumor types are available here. These TIL maps are derived through computational staining, using a convolutional neural network trained to classify patches of images. In addition to the TIL Maps, the analysis codes and the software used to extract TILs are also available.\\u00a0 The accompanying paper contains detailed information about our methods and our findings.\\u00a0 The source histopathology, molecular correlates and clinical data used in this study can be found on the Genomic Data Commons.\\u00a0 More information about the tools used to generate these results can be found\\u00a0 on the QuIP Software Stack and TIL Classification Software pages.\\u00a0 Answers to commonly asked questions about these data are contained in this\\u00a0FAQs\\u00a0document.\\r\\nTCGA Tumor Types Used in this Study\\r\\n\\r\\n \\tBLCA Bladder urothelial carcinoma\\r\\n \\tBRCA Breast invasive carcinoma\\r\\n \\tCESC Cervical squamous cell carcinoma and endocervical adenocarcinoma\\r\\n \\tCOAD Colon adenocarcinoma\\r\\n \\tLUAD Lung adenocarcinoma\\r\\n \\tLUSC Lung squamous cell carcinoma\\r\\n \\tPAAD Pancreatic adenocarcinoma\\r\\n \\tPRAD Prostate adenocarcinoma\\r\\n \\tREAD Rectum adenocarcinoma\\r\\n \\tSKCM Skin Cutaneous Melanoma\\r\\n \\tSTAD Stomach adenocarcinoma\\r\\n \\tUCEC Uterine Corpus Endometrial Carcinoma\\r\\n \\tUVM Uveal Melanoma\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Bladder Urothelial Carcinoma\",\"Breast Invasive Carcinoma\",\"Cervical Squamous Cell Carcinoma\",\"Endocervical Adenocarcinoma\",\"Colon adenocarcinoma\",\"Lung Adenocarcinoma\",\"Lung Squamous Cell Carcinoma\",\"Pancreatic adenocarcinoma\",\"Prostate Adenocarcinoma\",\"Rectum Adenocarcinoma\",\"Skin Cutaneous Melanoma\",\"Stomach Adenocarcinoma\",\"Uterine Corpus Endometrial Carcinoma\",\"Uveal Melanoma\"],\"cancer_locations\":[\"Bladder\",\"Breast\",\"Pelvic Cervix\",\"Chest\",\"Colon\",\"Eye\",\"Lung\",\"Pancreas\",\"Prostate\",\"Rectum\",\"Skin\",\"Stomach\",\"Uterus\"],\"data_types\":[\"Histopathology\"],\"supporting_data\":[\"Deep learning based computational stain for staining tumor-infiltrating lymphocytes (TILs)\",\"Software\\\/Source Code\",\"Histopathology\"],\"species\":[],\"subjects\":4759,\"date_updated\":\"2018-12-17\",\"status\":\"Complete\",\"license\":\"CC BY 3.0\",\"search_url\":\"https:\\\/\\\/cancerimagingarchive.net\\\/datascope\\\/TCGA_TilMap\\\/\",\"download_url\":\"\",\"blob\":\"tumor-infiltrating lymphocytes maps from tcga h&e whole slide pathology images til-wsi-tcga mappings of tumor-infiltrating lymphocytes (tils), based on h&e images from 13 of the cancer genome atlas (tcga) tumor types are available here. these til maps are derived through computational staining, using a convolutional neural network trained to classify patches of images. in addition to the til maps, the analysis codes and the software used to extract tils are also available.\\u00a0 the accompanying paper contains detailed information about our methods and our findings.\\u00a0 the source histopathology, molecular correlates and clinical data used in this study can be found on the genomic data commons.\\u00a0 more information about the tools used to generate these results can be found\\u00a0 on the quip software stack and til classification software pages.\\u00a0 answers to commonly asked questions about these data are contained in this\\u00a0faqs\\u00a0document.\\r\\ntcga tumor types used in this study\\r\\n\\r\\n \\tblca bladder urothelial carcinoma\\r\\n \\tbrca breast invasive carcinoma\\r\\n \\tcesc cervical squamous cell carcinoma and endocervical adenocarcinoma\\r\\n \\tcoad colon adenocarcinoma\\r\\n \\tluad lung adenocarcinoma\\r\\n \\tlusc lung squamous cell carcinoma\\r\\n \\tpaad pancreatic adenocarcinoma\\r\\n \\tprad prostate adenocarcinoma\\r\\n \\tread rectum adenocarcinoma\\r\\n \\tskcm skin cutaneous melanoma\\r\\n \\tstad stomach adenocarcinoma\\r\\n \\tucec uterine corpus endometrial carcinoma\\r\\n \\tuvm uveal melanoma bladder urothelial carcinoma breast invasive carcinoma cervical squamous cell carcinoma endocervical adenocarcinoma colon adenocarcinoma lung adenocarcinoma lung squamous cell carcinoma pancreatic adenocarcinoma prostate adenocarcinoma rectum adenocarcinoma skin cutaneous melanoma stomach adenocarcinoma uterine corpus endometrial carcinoma uveal melanoma bladder breast pelvic cervix chest colon eye lung pancreas prostate rectum skin stomach uterus histopathology deep learning based computational stain for staining tumor-infiltrating lymphocytes (tils) software\\\/source code histopathology community\"},{\"id\":49945,\"type\":\"analysis\",\"slug\":\"tompei-cmmd\",\"url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/analysis-result\\\/tompei-cmmd\\\/\",\"title\":\"TOMPEI-CMMD Dataset\",\"short_title\":\"TOMPEI-CMMD\",\"doi\":\"10.7937\\\/wezw-bh22\",\"summary\":\"The TOMPEI-CMMD dataset adds the following image analyses to the original CMMD dataset\\u00a0on TCIA:\\r\\n\\r\\n \\tSegmentations of 1385 breast lesions with supporting clinical data.\\r\\n \\tAccuracy Improvements: Corrected images with obvious errors in the labeling of the left (L) and right (R), as well as the CC and MLO orientations, resulting in a more accurate dataset (see clinical data spreadsheet).\\r\\n \\t\\u00a0Addition of Breast Cancer Location Information: Unlike the original data, which contained only per-image labels, the TOMPEI-CMMD clinical data spreadsheet includes mask information in the clinical data spreadsheet. This allows researchers to use the precise location data of breast cancer within images, facilitating detailed studies of disease characteristics and treatment efficacy.\\r\\n\\r\\nThe source CMMD collection consisted of 3,728 breast images, including MLO (mediolateral oblique) and CC (craniocaudal) views, from 1,775 Chinese patients. The patients were examined between July 2012 and January 2016. Images were captured using a GE Senographe DS mammography system. Images from the left and right breasts of the patients were treated as independent breast images in the TOMPEI-CMMD dataset. The dataset exclusively used MLO views from the original CMMD dataset, excluding 1,127 CC views. This resulted in 2,601 breast images derived from 1,775 Chinese patients who were initially included for radiological assessment in the TOMPEI-CMMD dataset.\\r\\n\\r\\nA board-certified radiologist with 20 years of experience in breast imaging assessed the radiological interpretation of all 2,601 breast images in the TOMPEI-CMMD dataset with prior knowledge of the final outcomes of malignant\\\/benign diagnoses annotated in the original CMMD dataset. For lesions recognized as breast cancer, the radiologist documented the radiological findings, including mass, calcification, focal asymmetric density, and distortion, along with their specific locations.\\r\\n\\r\\nSegmentation was performed by five radiological technologists who collaborated with the radiologist to annotate the images in accordance with evaluations made by the radiologist.\\r\\n\\r\\nEach breast image in the TOMPEI-CMMD dataset was independently annotated using segmentation masks for the radiological findings, including tags for masses (blue), calcifications (yellow), fibroadenomas (red), distortions (green), focal asymmetric densities (purple), and lipomas (light blue). Breast images with multiple radiological findings were assigned multiple tags.\\r\\n\\r\\nSegmentation masks for the radiological findings were annotated as JSON files. The JSON files contain tags linked to unique identifiers and attributes including '_id' of the patient; 'type' and 'color' for the radiological findings; 'cgPoints', coordinate values surrounding the annotation masks; and 'x' and 'y' values representing the lesion's location.\\r\\n\\r\\nFollowing the radiological assessment, 140 breast images with mammographically undetectable lesions from patients with pathologically proven breast cancer were excluded from the TOMPEI-CMMD dataset. In addition, 25 breast images were excluded because of insufficient image quality. Consequently, 2,436 breast images were included in the final TOMPEI-CMMD clinical spreadsheet.\\r\\n\\r\\nThe analysis dataset comprises 1385 breasts with lesions for 1363 patients; breasts without lesions were not included. The 1385 json annotation files have 1773 segmentation masks for the following: 255 Benign lesions, 1515 malignant lesions, and 3 lesions that were subsequently excluded from the published paper. Details are provided in the clinical data spreadsheet.\",\"image\":false,\"program\":\"Community\",\"cancer_types\":[\"Breast Cancer\"],\"cancer_locations\":[\"Breast\"],\"data_types\":[\"Segmentation\",\"Classification\",\"Demographic\",\"Diagnosis\",\"Pathology Detail\"],\"supporting_data\":[\"Segmentations\",\"Clinical\"],\"species\":[\"Human\"],\"subjects\":1363,\"date_updated\":\"2025-01-24\",\"status\":\"Complete\",\"license\":\"CC BY 4.0\",\"search_url\":\"\",\"download_url\":\"https:\\\/\\\/stage.cancerimagingarchive.net\\\/wp-content\\\/uploads\\\/TOMPEI-CMMD_v01_20250123.zip\",\"blob\":\"tompei-cmmd dataset tompei-cmmd the tompei-cmmd dataset adds the following image analyses to the original cmmd dataset\\u00a0on tcia:\\r\\n\\r\\n \\tsegmentations of 1385 breast lesions with supporting clinical data.\\r\\n \\taccuracy improvements: corrected images with obvious errors in the labeling of the left (l) and right (r), as well as the cc and mlo orientations, resulting in a more accurate dataset (see clinical data spreadsheet).\\r\\n \\t\\u00a0addition of breast cancer location information: unlike the original data, which contained only per-image labels, the tompei-cmmd clinical data spreadsheet includes mask information in the clinical data spreadsheet. this allows researchers to use the precise location data of breast cancer within images, facilitating detailed studies of disease characteristics and treatment efficacy.\\r\\n\\r\\nthe source cmmd collection consisted of 3,728 breast images, including mlo (mediolateral oblique) and cc (craniocaudal) views, from 1,775 chinese patients. the patients were examined between july 2012 and january 2016. images were captured using a ge senographe ds mammography system. images from the left and right breasts of the patients were treated as independent breast images in the tompei-cmmd dataset. the dataset exclusively used mlo views from the original cmmd dataset, excluding 1,127 cc views. this resulted in 2,601 breast images derived from 1,775 chinese patients who were initially included for radiological assessment in the tompei-cmmd dataset.\\r\\n\\r\\na board-certified radiologist with 20 years of experience in breast imaging assessed the radiological interpretation of all 2,601 breast images in the tompei-cmmd dataset with prior knowledge of the final outcomes of malignant\\\/benign diagnoses annotated in the original cmmd dataset. for lesions recognized as breast cancer, the radiologist documented the radiological findings, including mass, calcification, focal asymmetric density, and distortion, along with their specific locations.\\r\\n\\r\\nsegmentation was performed by five radiological technologists who collaborated with the radiologist to annotate the images in accordance with evaluations made by the radiologist.\\r\\n\\r\\neach breast image in the tompei-cmmd dataset was independently annotated using segmentation masks for the radiological findings, including tags for masses (blue), calcifications (yellow), fibroadenomas (red), distortions (green), focal asymmetric densities (purple), and lipomas (light blue). breast images with multiple radiological findings were assigned multiple tags.\\r\\n\\r\\nsegmentation masks for the radiological findings were annotated as json files. the json files contain tags linked to unique identifiers and attributes including '_id' of the patient; 'type' and 'color' for the radiological findings; 'cgpoints', coordinate values surrounding the annotation masks; and 'x' and 'y' values representing the lesion's location.\\r\\n\\r\\nfollowing the radiological assessment, 140 breast images with mammographically undetectable lesions from patients with pathologically proven breast cancer were excluded from the tompei-cmmd dataset. in addition, 25 breast images were excluded because of insufficient image quality. consequently, 2,436 breast images were included in the final tompei-cmmd clinical spreadsheet.\\r\\n\\r\\nthe analysis dataset comprises 1385 breasts with lesions for 1363 patients; breasts without lesions were not included. the 1385 json annotation files have 1773 segmentation masks for the following: 255 benign lesions, 1515 malignant lesions, and 3 lesions that were subsequently excluded from the published paper. details are provided in the clinical data spreadsheet. breast cancer breast segmentation classification demographic diagnosis pathology detail segmentations clinical community\"}]<\/script>\r\n    <script type=\"application\/json\" id=\"tcb-6a1bf86e65251-cfg\">{\"iid\":\"tcb-6a1bf86e65251\",\"accent\":\"#1a5a8a\",\"accentRed\":\"#d4531a\",\"perPage\":24,\"schema\":{\"filters\":[{\"key\":\"data_types\",\"label\":\"Data Type\",\"options\":[{\"value\":\"CODEX images\",\"label\":\"CODEX 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