{"id":43699,"date":"2023-11-27T03:46:40","date_gmt":"2023-11-27T09:46:40","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/collection\/prostate-anatomical-edge-cases\/"},"modified":"2023-11-27T15:37:28","modified_gmt":"2023-11-27T21:37:28","slug":"prostate-anatomical-edge-cases","status":"publish","type":"tcia_collection","link":"https:\/\/stage.cancerimagingarchive.net\/collection\/prostate-anatomical-edge-cases\/","title":{"rendered":"PROSTATE-ANATOMICAL-EDGE-CASES"},"featured_media":7833,"template":"","class_list":["post-43699","tcia_collection","type-tcia_collection","status-publish","has-post-thumbnail"],"cancer_types":["Prostate Cancer"],"citations":[43693,43695,9225],"collection_doi":"10.7937\/QSTF-ST65","collection_download_info":"","collection_downloads":[43697],"versions":false,"additional_resources":"The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.\r\n<ul>\r\n \t<li><a href=\"https:\/\/portal.imaging.datacommons.cancer.gov\/explore\/filters\/?collection_id=prostate_anatomical_edge_cases\">Imaging Data Commons (IDC)<\/a>\u00a0(Imaging Data)<\/li>\r\n<\/ul>\r\n&nbsp;","cancer_locations":["Prostate"],"collection_page_accessibility":"Public","publications_related":"","version_change_log_archived":"<h3>Version 1 (Current): Updated 2023\/05\/18<\/h3><table><colgroup><col \/><col \/><col \/><\/colgroup><tbody><tr><th>Data Type<\/th><th>Download all or Query\/Filter<\/th><th>License<\/th><\/tr><tr><td><p>Images and Radiation Therapy Structures (DICOM, 17 GB)<\/p><\/td><td><div><p><a href=\"\/wp-content\/uploads\/Prostate-Anatomical-Edge-Cases-May-2023-manifest.tcia\" download=\"Prostate-Anatomical-Edge-Cases-May-2023-manifest.tcia\"><button><i><\/i> Download<\/button><\/a>\u00a0 <a href=\"https:\/\/nbia.cancerimagingarchive.net\/nbia-search\/?MinNumberOfStudiesCriteria=1&amp;CollectionCriteria=Prostate-Anatomical-Edge-Cases\"><button><i><\/i> Search<\/button><\/a>\u00a0<\/p><p>(Download requires\u00a0the\u00a0<a href=\"https:\/\/wiki.cancerimagingarchive.net\/display\/NBIA\/Downloading+TCIA+Images\">NBIA Data Retriever<\/a>)<\/p><\/div><\/td><td><div><p><a href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\">CC BY 4.0<\/a><\/p><\/div><\/td><\/tr><\/tbody><\/table>","collection_status":"Complete","publications_using":"TCIA maintains\u00a0<a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\">a list of publications<\/a> which leverage TCIA data. If you have a manuscript you'd like to add please<a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\"> contact the TCIA Helpdesk<\/a>.","related_analysis_results":false,"species":["Human"],"version_number":"1","collection_title":"Stress-Testing Pelvic Autosegmentation Algorithms Using Anatomical Edge Cases","date_updated":"2023-05-18","related_collection":false,"subjects":"131","analysis_results":"","collection_short_title":"Prostate-Anatomical-Edge-Cases","data_types":["CT","RTSTRUCT"],"version_change_log":"","collection_browse_title":"Prostate-Anatomical-Edge-Cases","detailed_description":"","supporting_data":false,"collection_featured_image":{"ID":"7833","post_author":"29","post_date":"2023-09-13 09:48:44","post_date_gmt":"2023-09-13 14:48:44","post_content":"","post_title":"gr2_lrg","post_excerpt":"","post_status":"inherit","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"gr2_lrg","to_ping":"","pinged":"","post_modified":"2023-11-20 03:46:40","post_modified_gmt":"2023-11-20 09:46:40","post_content_filtered":"","post_parent":"43699","guid":"https:\/\/stage.cancerimagingarchive.net\/wp-content\/uploads\/gr2_lrg.jpg","menu_order":"0","post_type":"attachment","post_mime_type":"image\/jpeg","comment_count":"0","pod_item_id":"7833"},"collection_summary":"<p>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.<\/p><p>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: <a href=\"https:\/\/doi.org\/10.1016\/j.phro.2023.100413\">https:\/\/doi.org\/10.1016\/j.phro.2023.100413<\/a>.\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.<\/p><p>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.<\/p><p>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).<\/p>","collection_acknowledgements":"","collection_funding":"","hide_from_browse_table":"0","program":["Community"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/43699","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections"}],"about":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/types\/tcia_collection"}],"version-history":[{"count":0,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/collections\/43699\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media\/7833"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=43699"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}