{"id":45571,"date":"2023-11-20T05:20:59","date_gmt":"2023-11-20T11:20:59","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/tcga-gbm-qi-radiogenomics\/"},"modified":"2025-03-20T13:38:31","modified_gmt":"2025-03-20T18:38:31","slug":"tcga-gbm-qi-radiogenomics","status":"publish","type":"tcia_analysis_result","link":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/tcga-gbm-qi-radiogenomics\/","title":{"rendered":"TCGA-GBM-QI-RADIOGENOMICS"},"featured_media":0,"template":"","class_list":["post-45571","tcia_analysis_result","type-tcia_analysis_result","status-publish"],"cancer_types":["Glioblastoma"],"citations":[45553,45555,9225],"result_doi":"10.7937\/k9\/tcia.2014.rjeftjbu","result_download_info":"","result_downloads":[45557,45559],"version_change_log_archived":"Version 2 (Current): 2020\/10\/07\r\nData TypeDownload all or Query\/FilterImage Data (DICOM, 1.73 GB)(Requires\u00a0NBIA Data Retriever)Segmentations (ZIP, 597kB)Segmentation Summary (XLS, 123kB)\r\n4 The Cancer Genome Atlas Glioblastoma Multiforme Collection (TCGA-GBM) patients were removed from the collection, which had been previously analyzed by this group.\u00a0 Since the images are no longer available this Analysis Result dataset was updated accordingly.\u00a0Version 1: 2014\/11\/05\r\nData TypeDownload all or Query\/FilterImage Data (DICOM)No longer available. See v2 note.Segmentations (ZIP)Segmentation Summary (XLS)","versions":[45569],"additional_resources":"","cancer_locations":["Brain"],"publications_related":"","result_page_accessibility":"Limited","detailed_description":"","publications_using":"TCIA maintains\u00a0a list of publications<\/a>\u00a0that leveraged this dataset. If you have a manuscript you\u2019d like to add please\u00a0contact TCIA\u2019s Helpdesk<\/a>.","result_title":"Glioblastoma multiforme: exploratory radiogenomic analysis by using quantitative image features","species":["Human"],"version_number":"2","date_updated":"2020-10-07","related_collections":[43903],"result_short_title":"TCGA-GBM-QI-Radiogenomics","subjects":"55","related_analysis_results":false,"result_browse_title":"TCGA-GBM-QI-Radiogenomics","supporting_data":["Tumor segmentations"],"version_change_log":"4 TCGA-GBM<\/a>\u00a0patients were removed from the collection, which had been previously analyzed by this group.\u00a0 Since the images are no longer available this Analysis Result dataset was updated accordingly.","collections":"","result_summary":"

PURPOSE:<\/h4>\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\n

MATERIALS AND METHODS:<\/h4>\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)<\/a> 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<\/a>. 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\n

RESULTS:<\/h4>\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\n

CONCLUSION:<\/h4>\r\nRadiogenomic\u00a0approaches in GBM have the potential to predict clinical and molecular characteristics of tumors noninvasively.","collection_downloads":[45561],"result_featured_image":false,"result_acknowledgements":"","hide_from_browse_table":"0","program":["Community"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45571","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":2,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45571\/revisions"}],"predecessor-version":[{"id":49797,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45571\/revisions\/49797"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=45571"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}