{"id":45815,"date":"2023-11-20T05:36:08","date_gmt":"2023-11-20T11:36:08","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/crowds-cure-2017\/"},"modified":"2025-03-20T13:46:22","modified_gmt":"2025-03-20T18:46:22","slug":"crowds-cure-2017","status":"publish","type":"tcia_analysis_result","link":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/crowds-cure-2017\/","title":{"rendered":"CROWDS-CURE-2017"},"featured_media":0,"template":"","class_list":["post-45815","tcia_analysis_result","type-tcia_analysis_result","status-publish"],"cancer_types":["Lung Adenocarcinoma","Renal Clear Cell","Liver","Ovarian"],"citations":[45805,9225],"result_doi":"10.7937\/K9\/TCIA.2018.OW73VLO2","result_download_info":"","result_downloads":[45807,45809,45811],"version_change_log_archived":"Version 1 (Current): 2018\/05\/17\r\n   Data TypeDownload all or Query\/FilterImages (DICOM, 24.2 GB)\u00a0(Open this *.tcia manifest with NBIA Data Retriever)Image Annotations (CSV)DICOM-SR files (ZIP) *Clinical Data (CSV) **\r\n* The conversion\u00a0XSLT and Makefile  depends on pixelmed.jar as a DICOM toolkit,\u00a0 and dicom3tools, dcsrdump and dciodvfy for validation.\r\n** Because all subjects were pulled from The Cancer Genome Atlas cohorts, clinical data was available through the NCI Genomic Data Commons.\u00a0 A CSV dump of that data is provided here for convenience.","versions":false,"additional_resources":"","cancer_locations":["Chest","Kidney","Liver","Ovary"],"publications_related":"","result_page_accessibility":"Public","detailed_description":"<ul>\r\n \t<li><strong>DICOM-SR note<\/strong>:\u00a0 The <a href=\"\/wp-content\/uploads\/CrowdsCureCancerCSV_converttoDICOMSR_20180830.zip\" download=\"CrowdsCureCancerCSV_converttoDICOMSR_20180830.zip\" data-linked-resource-container-id=\"33948774\" data-linked-resource-container-version=\"29\" data-linked-resource-content-type=\"application\/zip\" data-linked-resource-default-alias=\"CrowdsCureCancerCSV_converttoDICOMSR_20180830.zip\" data-linked-resource-id=\"44499362\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\" data-nice-type=\"Zip Archive\">conversion\u00a0XSLT and Makefile <\/a> depends on pixelmed.jar as a DICOM toolkit (<a href=\"https:\/\/www.pixelmed.com\/dicomtoolkit.html\">https:\/\/www.pixelmed.com\/dicomtoolkit.html<\/a>) ,\u00a0 and dicom3tools, dcsrdump and dciodvfy for validation.<\/li>\r\n \t<li><strong>Clinical data note:\u00a0<\/strong> Because all subjects were pulled from The Cancer Genome Atlas cohorts, clinical data was available through the <a href=\"https:\/\/portal.gdc.cancer.gov\/projects?filters=%7B%22op%22%3A%22and%22%2C%22content%22%3A%5B%7B%22op%22%3A%22in%22%2C%22content%22%3A%7B%22field%22%3A%22projects.program.name%22%2C%22value%22%3A%5B%22TCGA%22%5D%7D%7D%5D%7D\">NCI Genomic Data Commons<\/a>.\u00a0 A CSV dump of that data is provided here for convenience.<\/li>\r\n<\/ul>\r\n<h4>Booth posters<\/h4>\r\n<a href=\"\/wp-content\/uploads\/quad.png\" rel=\"prettyPhoto noopener\"><img class=\"cm-inline-img-css alignright wp-image-1873 size-medium\" src=\"\/wp-content\/uploads\/quad.png\" \/><\/a>","publications_using":"TCIA maintains\u00a0<a href=\"https:\/\/commons.datacite.org\/doi.org\/10.7937\/K9\/TCIA.2018.OW73VLO2\">a list of publications<\/a>\u00a0that leveraged this dataset. If you have a manuscript you\u2019d like to add please\u00a0<a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\">contact TCIA\u2019s Helpdesk<\/a>.","result_title":"Crowds Cure Cancer: Data collected at the RSNA 2017 annual meeting","species":["Human"],"version_number":"1","date_updated":"2018-05-17","related_collections":[44001,44041,44155,44097],"result_short_title":"Crowds-Cure-2017","subjects":"352","related_analysis_results":[45927],"result_browse_title":"Crowds Cure Cancer: Data collected at the RSNA 2017 annual meeting (Crowds-Cure-2017)","supporting_data":false,"version_change_log":"","collections":"Below is a list of the Collections used in these analyses:\r\n<table><colgroup> <col \/> <col \/> <col \/><\/colgroup>\r\n<tbody>\r\n<tr>\r\n<th>Source Data Type<\/th>\r\n<th>Download all or Query\/Filter<\/th>\r\n<th>License<\/th>\r\n<\/tr>\r\n<tr>\r\n<td>Corresponding original source Images from TCGA-LUAD, TCGA-KIRC, TCGA-LIHC, TCGA-OV (DICOM, 24.2 GB)<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/RSNA2017CCC-doiJNLP-e8nBWDCC.tcia\" download=\"RSNA2017CCC-doiJNLP-e8nBWDCC.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n(Requires <a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\">NBIA Data Retriever<\/a>)\r\n\r\n<\/div><\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"https:\/\/creativecommons.org\/licenses\/by\/3.0\/\">CC BY 3.0<\/a>\r\n\r\n<\/div><\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n<ul>\r\n \t<li><a href=\"https:\/\/cancerimagingarchive.net\/collection\/tcga-luad\/\" target=\"_blank\" rel=\"noopener\">The Cancer Genome Atlas Lung Adenocarcinoma Collection (TCGA-LUAD)<\/a><\/li>\r\n \t<li><a href=\"https:\/\/cancerimagingarchive.net\/collection\/tcga-kirc\/\" target=\"_blank\" rel=\"noopener\">The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma Collection (TCGA-KIRC)<\/a><\/li>\r\n \t<li><a href=\"https:\/\/cancerimagingarchive.net\/collection\/tcga-lihc\/\" target=\"_blank\" rel=\"noopener\">The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC)<\/a><\/li>\r\n \t<li><a href=\"https:\/\/cancerimagingarchive.net\/collection\/tcga-ov\/\" target=\"_blank\" rel=\"noopener\">The Cancer Genome Atlas Ovarian Cancer Collection (TCGA-OV)<\/a><\/li>\r\n<\/ul>","result_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\u00a0<a href=\"https:\/\/cancerimagingarchive.net\/collection\/tcga-luad\/\" target=\"_blank\" rel=\"noopener\">The Cancer Genome Atlas Lung Adenocarcinoma Collection (TCGA-LUAD)<\/a>,\u00a0<a href=\"https:\/\/cancerimagingarchive.net\/collection\/tcga-kirc\/\" target=\"_blank\" rel=\"noopener\">The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma Collection (TCGA-KIRC)<\/a>,\u00a0<a href=\"https:\/\/cancerimagingarchive.net\/collection\/tcga-lihc\/\" target=\"_blank\" rel=\"noopener\">The Cancer Genome Atlas Liver Hepatocellular Carcinoma Collection (TCGA-LIHC)<\/a>, and\u00a0<a href=\"https:\/\/cancerimagingarchive.net\/collection\/tcga-ov\/\" target=\"_blank\" rel=\"noopener\">The Cancer Genome Atlas Ovarian Cancer Collection (TCGA-OV)<\/a>\u00a0collections on TCIA.\r\n\r\nA full explanation of the project can be seen in the Detailed Description.","collection_downloads":[45813],"result_featured_image":false,"result_acknowledgements":"","hide_from_browse_table":"0","program":["Community"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45815","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results"}],"about":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/types\/tcia_analysis_result"}],"version-history":[{"count":1,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45815\/revisions"}],"predecessor-version":[{"id":47449,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45815\/revisions\/47449"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=45815"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}