{"id":45601,"date":"2023-11-20T05:22:51","date_gmt":"2023-11-20T11:22:51","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/tcga-gbm-radiogenomics\/"},"modified":"2025-03-20T13:48:20","modified_gmt":"2025-03-20T18:48:20","slug":"tcga-gbm-radiogenomics","status":"publish","type":"tcia_analysis_result","link":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/tcga-gbm-radiogenomics\/","title":{"rendered":"TCGA-GBM-RADIOGENOMICS"},"featured_media":0,"template":"","class_list":["post-45601","tcia_analysis_result","type-tcia_analysis_result","status-publish"],"cancer_types":["Glioblastoma"],"citations":[45593,45595,9225],"result_doi":"10.7937\/K9\/TCIA.2014.4HTXYRCN","result_download_info":"Some data in this collection contains images that could potentially be used to reconstruct a human face. To safeguard the privacy of participants, users must sign and submit a <a href=\"https:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/4556915\/TCIA%20Restricted%20License%2020220519.pdf?version=1&amp;modificationDate=1652964581655&amp;api=v2\">TCIA Restricted License Agreement<\/a> to <a href=\"mailto:help@cancerimagingarchive.net\">help@cancerimagingarchive.net<\/a> before accessing the data.\r\n\r\n&nbsp;\r\n<h3>Other Resources<\/h3>\r\n<ul>\r\n \t<li>Clinical data, radiologist observations, and genomics analysis :\u00a0<a href=\"https:\/\/docs.google.com\/spreadsheets\/d\/1zuN4PSTNOQN1BbfbVvlEzJ4jyHBPy6ddqxGQ-FRR9qo\/edit?usp=sharing\">https:\/\/docs.google.com\/spreadsheets\/d\/1zuN4PSTNOQN1BbfbVvlEzJ4jyHBPy6ddqxGQ-FRR9qo\/edit?usp=sharing<\/a><\/li>\r\n<\/ul>","result_downloads":[45597],"version_change_log_archived":"Version 1 (Current): Updated 2014\/11\/12\r\nData TypeDownload all or Query\/FilterLicenseClinical data, radiologist observations, and genomics analysis (30kB, XLSX)\r\n Download\u00a0\r\nCC BY 3.0Original Image Data manifest subset from TCGA-GBM (DICOM, 71 subjects, 198578 files, 21.52 GB)\r\n Download\u00a0\r\nRequires NBIA Data RetrieverTCIA Restricted","versions":false,"additional_resources":"","cancer_locations":["Brain"],"publications_related":"The Collection authors recommend these readings to give context to this dataset","result_page_accessibility":"Limited","detailed_description":"No images were created in this analysis.","publications_using":"TCIA maintains\u00a0<a href=\"https:\/\/commons.datacite.org\/doi.org\/10.7937\/K9\/TCIA.2014.4HTXYRCN\">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>.\r\n\r\n&nbsp;","result_title":"MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set","species":["Human"],"version_number":"1","date_updated":"2014-11-12","related_collections":[43903],"result_short_title":"TCGA-GBM-Radiogenomics","subjects":"75","related_analysis_results":false,"result_browse_title":"MR Imaging Predictors of Molecular Profile and Survival: Multi-institutional Study of the TCGA Glioblastoma Data Set (TCGA-GBM-Radiogenomics)","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<\/th>\r\n<th>License<\/th>\r\n<\/tr>\r\n<tr>\r\n<td>Original Image Data manifest subset from TCGA-GBM (DICOM, 71 subjects, 198,578 files, 21.52 GB)<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/doiJNLP-UPX9noQx.tcia\" download=\"doiJNLP-UPX9noQx.tcia\"><button><i><\/i> Download<\/button><\/a>\r\nRequires <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:\/\/wiki.cancerimagingarchive.net\/download\/attachments\/4556915\/TCIA%20Restricted%20License%2020220519.pdf?version=1&amp;modificationDate=1652964581655&amp;api=v2\">TCIA Restricted<\/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:\/\/doi.org\/10.7937\/K9\/TCIA.2016.RNYFUYE9\">TCGA-GBM<\/a><\/li>\r\n<\/ul>","result_summary":"<h4>PURPOSE:<\/h4>\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\n<h4>MATERIALS AND METHODS:<\/h4>\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\n<h4>RESULTS:<\/h4>\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 &lt; .01).\r\n<h4>CONCLUSION:<\/h4>\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.","collection_downloads":[45599],"result_featured_image":false,"result_acknowledgements":"","hide_from_browse_table":"0","program":["TCGA"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45601","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\/45601\/revisions"}],"predecessor-version":[{"id":47319,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45601\/revisions\/47319"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=45601"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}