{"id":46609,"date":"2023-11-20T11:41:25","date_gmt":"2023-11-20T17:41:25","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/rsna-asnr-miccai-brats-2021\/"},"modified":"2024-04-17T10:41:47","modified_gmt":"2024-04-17T15:41:47","slug":"rsna-asnr-miccai-brats-2021","status":"publish","type":"tcia_analysis_result","link":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/rsna-asnr-miccai-brats-2021\/","title":{"rendered":"RSNA-ASNR-MICCAI-BRATS-2021"},"featured_media":9095,"template":"","class_list":["post-46609","tcia_analysis_result","type-tcia_analysis_result","status-publish","has-post-thumbnail"],"cancer_types":["Glioma"],"citations":[46585,46587,46589,46591,9225,46593],"result_doi":"10.7937\/jc8x-9874","result_download_info":"","result_downloads":[46595,46597],"version_change_log_archived":"Version 1 (Current): Updated 2023\/08\/25\r\nData TypeDownload all or Query\/FilterLicenseChallenge data (both tasks, 142 GB, *.nii.gz or *.dcm)\r\n Download\u00a0\r\n(Download and apply the\u00a0IBM-Aspera-Connect plugin\u00a0to your browser to retrieve this faspex package)\u00a0CC BY 4.0ID Crosswalk between BraTS ID and TCIA ID (xlsx, 79 kB)\r\n Download\u00a0\r\nCC BY 4.0","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\/\">Imaging Data Commons (IDC)<\/a>\u00a0(Imaging Data)\r\n<ul>\r\n \t<li><a href=\"https:\/\/portal.imaging.datacommons.cancer.gov\/explore\/filters\/?collection_id=cptac_gbm\">CPTAC-GBM<\/a>\u00a0, <a href=\"https:\/\/portal.imaging.datacommons.cancer.gov\/explore\/filters\/?collection_id=tcga_gbm\">TCGA-GBM<\/a> ,\u00a0<a href=\"https:\/\/portal.imaging.datacommons.cancer.gov\/explore\/filters\/?collection_id=tcga_lgg\">TCGA-LGG<\/a> , <a href=\"https:\/\/portal.imaging.datacommons.cancer.gov\/explore\/filters\/?collection_id=upenn_gbm\">UPENN-GBM<\/a><\/li>\r\n<\/ul>\r\n<\/li>\r\n \t<li><a href=\"https:\/\/portal.gdc.cancer.gov\/projects\/\">Genomic Data Commons (GDC)<\/a>\u00a0(Genomic,\u00a0Digitized Histopathology\u00a0&amp; Clinical Data)\r\n<ul>\r\n \t<li><a href=\"https:\/\/portal.gdc.cancer.gov\/exploration?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%22cases.primary_site%22%2C%22value%22%3A%5B%22brain%22%5D%7D%7D%2C%7B%22op%22%3A%22in%22%2C%22content%22%3A%7B%22field%22%3A%22cases.project.program.name%22%2C%22value%22%3A%5B%22CPTAC%22%5D%7D%7D%2C%7B%22op%22%3A%22in%22%2C%22content%22%3A%7B%22field%22%3A%22cases.project.project_id%22%2C%22value%22%3A%5B%22CPTAC-3%22%5D%7D%7D%5D%7D&amp;searchTableTab=cases\">CPTAC-GBM<\/a> ,\u00a0<a href=\"https:\/\/portal.gdc.cancer.gov\/projects\/TCGA-GBM\">TCGA-GBM<\/a> ,\u00a0<a href=\"https:\/\/portal.gdc.cancer.gov\/projects\/TCGA-LGG\">TCGA-LGG<\/a><\/li>\r\n<\/ul>\r\n<\/li>\r\n \t<li><a href=\"https:\/\/pdc.cancer.gov\/pdc\/browse\/filters\/primary_site:Brain\">Proteomic Data Commons (PDC)<\/a>\u00a0(Proteomic &amp; Clinical Data)\r\n<ul>\r\n \t<li><a href=\"https:\/\/pdc.cancer.gov\/pdc\/browse\/filters\/study_name:CPTAC%20GBM%20Discovery%20Study%20-%20CompRef%20Proteome%7CCPTAC%20GBM%20Discovery%20Study%20-%20Proteome%7CCPTAC%20GBM%20Discovery%20Study%20-%20Phosphoproteome%7CCPTAC%20GBM%20Discovery%20Study%20-%20CompRef%20Phosphoproteome%7CCPTAC%20GBM%20Discovery%20Study%20-%20Acetylome%7CCPTAC%20GBM%20Discovery%20Study%20-%20CompRef%20Acetylome\">CPTAC-GBM<\/a><\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\nThe following external resources have been made available by the data submitters.\u00a0 These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.\r\n<ul>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.XLwaN6nL\">IvyGAP<\/a> provides access to additional resources for this data:\r\n<ul>\r\n \t<li><a href=\"http:\/\/glioblastoma.alleninstitute.org\/\">Summary ISH, RNA, gene expression and clinical data<\/a><\/li>\r\n \t<li><a href=\"https:\/\/ivygap.org\/home\">Detailed clinical, genomic, and expression array data<\/a><\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>","cancer_locations":["Brain"],"publications_related":"The Collection authors suggest the below will give context to this dataset:\r\n\r\nYou are free to use and\/or refer to the BraTS datasets in your own research. In addition, please be specific and also cite the following <strong>datasets <\/strong>that were part of this Challenge:\r\n<ol>\r\n \t<li>Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., &amp; Davatzikos, C. (2017). Segmentation Labels for the Pre-operative Scans of the TCGA-GBM collection [Data set]. The Cancer Imaging Archive. <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.KLXWJJ1Q\">https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.KLXWJJ1Q<\/a><\/li>\r\n \t<li>Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J., Freymann, J., Farahani, K., &amp; Davatzikos, C. (2017). Segmentation Labels for the Pre-operative Scans of the TCGA-LGG collection [Data set]. The Cancer Imaging Archive. <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.GJQ7R0EF\">https:\/\/doi.org\/10.7937\/K9\/TCIA.2017.GJQ7R0EF<\/a><\/li>\r\n \t<li>Scarpace, L., Mikkelsen, T., Cha, S., Rao, S., Tekchandani, S., Gutman, D., Saltz, J. H., Erickson, B. J., Pedano, N., Flanders, A. E., Barnholtz-Sloan, J., Ostrom, Q., Barboriak, D., &amp; Pierce, L. J. (2016). The Cancer Genome Atlas Glioblastoma Multiforme Collection (TCGA-GBM) (Version 4) [Data set]. The Cancer Imaging Archive. <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.RNYFUYE9\">https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.RNYFUYE9<\/a><\/li>\r\n \t<li>Pedano, N., Flanders, A. E., Scarpace, L., Mikkelsen, T., Eschbacher, J. M., Hermes, B., Sisneros, V., Barnholtz-Sloan, J., &amp; Ostrom, Q. (2016). The Cancer Genome Atlas Low Grade Glioma Collection (TCGA-LGG) (Version 3) [Data set]. The Cancer Imaging Archive.\u00a0<a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.L4LTD3TK\">https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.L4LTD3TK<\/a><\/li>\r\n \t<li>Calabrese, E., Villanueva-Meyer, J., Rudie, J., Rauschecker, A., Baid, U., Bakas, S., Cha, S., Mongan, J., &amp; Hess, C. (2022). The University of California San Francisco Preoperative Diffuse Glioma MRI (UCSF-PDGM) (Version 1) [Data set].\u00a0\u00a0The Cancer Imaging Archive.\u00a0 <a href=\"https:\/\/doi.org\/10.7937\/tcia.bdgf-8v37\">https:\/\/doi.org\/10.7937\/tcia.bdgf-8v37<\/a><\/li>\r\n \t<li>Bakas, S., Sako, C., Akbari, H., Bilello, M., Sotiras, A., Shukla, G., Rudie, J. D., Flores Santamaria, N., Fathi Kazerooni, A., Pati, S., Rathore, S., Mamourian, E., Ha, S. M., Parker, W., Doshi, J., Baid, U., Bergman, M., Binder, Z. A., Verma, R., \u2026 Davatzikos, C. (2021). Multi-parametric magnetic resonance imaging (mpMRI) scans for de novo Glioblastoma (GBM) patients from the University of Pennsylvania Health System (UPENN-GBM) (Version 2) [Data set]. The Cancer Imaging Archive. <a href=\"https:\/\/doi.org\/10.7937\/TCIA.709X-DN49\">https:\/\/doi.org\/10.7937\/TCIA.709X-DN49<\/a><\/li>\r\n<\/ol>","result_page_accessibility":"Limited","detailed_description":"NOTE:\u00a0 The \"challenge test set dataset\" is sequestered on <a href=\"https:\/\/www.synapse.org\/#!Synapse:syn25829067\/wiki\/610863\">synapse.org<\/a> (Project SynID: syn25829067). Please see their site for more detail.\r\n\r\nNOTE: Segmentation task nifti: Number of Images\u00a0 7,131 (Seg) , Images Size (GB)12 (Seg)\r\n\r\nNOTE: Classification task nifti+DICOM: Number of Images 400,114 (Class), Images Size (GB) 128 (Class)\r\n\r\nSegmentation labels of the different glioma sub-regions considered for evaluation are the \"enhancing tumor\" (ET), the \"tumor core\" (TC), and the \"whole tumor\" (WT). The ET is described by areas that show hyper-intensity in T1Gd when compared to T1, but also when compared to \u201chealthy\u201d white matter in T1Gd. The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (NCR) parts of the tumor. The appearance of NCR is typically hypo-intense in T1-Gd when compared to T1. The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edematous\/invaded tissue (ED), which is typically depicted by hyper-intense signal in FLAIR.\u00a0<strong>The provided segmentation labels have values of 1 for NCR, 2 for ED, 4 for ET, and 0 for everything else.<\/strong>\r\n\r\nThe data used in BraTS Challenges often have some overlap with other TCIA Collections, cases, and series. Some filters for handling these, so that you can work with statistically not-duplicated images, include these below:\r\n<ul>\r\n \t<li>Manifest of case identifiers between BraTS and TCIA, NOTE: includes <strong>new series<\/strong> files with no TCIA equivalent: <a href=\"\/wp-content\/uploads\/BraTS2021_MappingToTCIA.xlsx\" download=\"BraTS2021_MappingToTCIA.xlsx\" data-linked-resource-container-id=\"133073473\" data-linked-resource-container-version=\"41\" data-linked-resource-content-type=\"application\/vnd.openxmlformats-officedocument.spreadsheetml.sheet\" data-linked-resource-default-alias=\"BraTS2021_MappingToTCIA.xlsx\" data-linked-resource-id=\"145755389\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\" data-nice-type=\"Excel Spreadsheet\">BraTS2021_MappingToTCIA.xlsx<\/a><\/li>\r\n \t<li>Spreadsheet list of cases and series used in prior year BraTS Challenges may also refer to these:\r\n<ul>\r\n \t<li>Multimodal Brain Tumor Segmentation Challenge 2018 (BraTS):\r\n<ul>\r\n \t<li><span class=\"conf-macro output-inline\" data-hasbody=\"true\" data-macro-name=\"excerpt\"><strong>BraTS 2018<\/strong>\u00a0utilizes multi-institutional pre-operative MRI scans and\u00a0<strong>focuses on the segmentation of<\/strong>\u00a0intrinsically heterogeneous (in appearance, shape, and histology)\u00a0<strong>brain tumors<\/strong>, namely gliomas. Furthemore, to pinpoint the clinical relevance of this segmentation task, BraTS\u201918 also focuses\u00a0<strong>on the prediction of patient overall survival<\/strong>, via integrative analyses of radiomic features and machine learning algorithms.\u00a0 More information can be found at\u00a0<a class=\"external-link\" href=\"http:\/\/www.med.upenn.edu\/sbia\/brats2018.html\" rel=\"nofollow\">http:\/\/www.med.upenn.edu\/sbia\/brats2018.html<\/a>. This challenge utilizes subsets of\u00a0<a href=\"\/collection\/tcga-gbm\">The Cancer Genome Atlas Glioblastoma Multiforme Collection (TCGA-GBM)<\/a>\u00a0and\u00a0<a href=\"\/collection\/tcga-lgg\">The Cancer Genome Atlas Low Grade Glioma Collection (TCGA-LGG)<\/a>\u00a0primary data set, and has resulted in multiple\u00a0<a href=\"https:\/\/www.cancerimagingarchive.net\/browse-analysis-results\/\">TCIA Analysis Results<\/a>\u00a0data sets.<\/span>\r\n<ul>\r\n \t<li class=\"auto-cursor-target\"><a href=\"\/analysis-result\/brats-tcga-lgg\/\">BraTS-TCGA-LGG<\/a><\/li>\r\n \t<li class=\"auto-cursor-target\"><a href=\"\/analysis-result\/brats-tcga-gbm\/\">BraTS-TCGA-GBM<\/a><\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<\/li>\r\n \t<li>Multimodal Brain Tumor Segmentation Challenge 2019:\r\n<ul>\r\n \t<li>From the challenge web site:\u00a0<a class=\"external-link\" href=\"https:\/\/www.med.upenn.edu\/cbica\/brats2019\/data.html\" rel=\"nofollow\">https:\/\/www.med.upenn.edu\/cbica\/brats2019\/data.html<\/a>BraTS has always been focusing on the evaluation of state-of-the-art methods for the segmentation of brain tumors in multimodal magnetic resonance imaging (MRI) scans. BraTS 2019 utilizes multi-institutional pre-operative MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. Furthermore, to pinpoint the clinical relevance of this segmentation task, BraTS\u201919 also focuses on the prediction of patient overall survival, via integrative analyses of radiomic features and machine learning algorithms. Finally, BraTS'19 intends to experimentally evaluate the uncertainty in tumor segmentations.BraTS 2019 ran in conjunction with the <a class=\"external-link\" href=\"http:\/\/www.miccai2019.org\/\" rel=\"nofollow\">MICCAI 2019 conference<\/a>, on Oct. 17 2019, as part of the full-day <a class=\"external-link\" href=\"http:\/\/www.brainlesion-workshop.org\/\" rel=\"nofollow\">BrainLes Workshop<\/a>.<\/li>\r\n<\/ul>\r\n<\/li>\r\n \t<li><a href=\"\/analysis-result\/brats-tcga-gbm\/\" target=\"_blank\" rel=\"noopener\">BraTS-TCGA-GBM<\/a><\/li>\r\n \t<li><a href=\"\/analysis-result\/brats-tcga-lgg\/\" target=\"_blank\" rel=\"noopener\">BraTS-TCGA-LGG<\/a><\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<ul>\r\n \t<li>Spreadsheet list of <strong>new (NIfTI) series<\/strong> files with no TCIA DICOM equivalent:\u00a0<a href=\"\/wp-content\/uploads\/NotPreviouslyInTCIA.csv\" download=\"NotPreviouslyInTCIA.csv\" data-linked-resource-container-id=\"133073473\" data-linked-resource-container-version=\"41\" data-linked-resource-content-type=\"text\/csv\" data-linked-resource-default-alias=\"NotPreviouslyInTCIA.csv\" data-linked-resource-id=\"157288228\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\">NotPreviouslyInTCIA.csv<\/a><\/li>\r\n<\/ul>\r\n<ul>\r\n \t<li>You might find these splits useful to navigate accidental duplication while making superset cohorts. These were processed as input to the BraTS Collection, and will require a Usage Agreement on file.\r\n<ul>\r\n \t<li>Segmentation Task (Training sets) <a title=\"BraTS2021_TCIAderived_Seg-Task-Training.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_TCIAderived_Seg-Task-Training.tcia\" download=\"BraTS2021_TCIAderived_Seg-Task-Training.tcia\">BraTS2021_TCIAderived_Seg-Task-Training.tcia<\/a>\r\n<ul>\r\n \t<li><a title=\"BraTS2021_ACRIN-FMISO-Brain_Seg-Task-Training.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_ACRIN-FMISO-Brain_Seg-Task-Training.tcia\" download=\"BraTS2021_ACRIN-FMISO-Brain_Seg-Task-Training.tcia\">BraTS2021_ACRIN-FMISO-Brain_Seg-Task-Training.tcia<\/a><\/li>\r\n \t<li><a title=\"BraTS2021_TCGA-LGG_Seg-Task-Training.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_TCGA-LGG_Seg-Task-Training.tcia\" download=\"BraTS2021_TCGA-LGG_Seg-Task-Training.tcia\">BraTS2021_TCGA-LGG_Seg-Task-Training.tcia<\/a><\/li>\r\n \t<li><a title=\"BraTS2021_TCGA-GBM_Seg-Task-Training.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_TCGA-GBM_Seg-Task-Training.tcia\" download=\"BraTS2021_TCGA-GBM_Seg-Task-Training.tcia\">BraTS2021_TCGA-GBM_Seg-Task-Training.tcia<\/a><\/li>\r\n \t<li><a title=\"BraTS2021_IvyGAP_Seg-Task-Training.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_IvyGAP_Seg-Task-Training.tcia\" download=\"BraTS2021_IvyGAP_Seg-Task-Training.tcia\">BraTS2021_IvyGAP_Seg-Task-Training.tcia<\/a><\/li>\r\n \t<li><a title=\"BraTS2021_CPTAC-GBM_Seg-Task-Training.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_CPTAC-GBM_Seg-Task-Training.tcia\" download=\"BraTS2021_CPTAC-GBM_Seg-Task-Training.tcia\">BraTS2021_CPTAC-GBM_Seg-Task-Training.tcia<\/a><\/li>\r\n \t<li><a title=\"BraTS2021_UPENN-GBM_Seg-Task-Training.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_UPENN-GBM_Seg-Task-Training.tcia\" download=\"BraTS2021_UPENN-GBM_Seg-Task-Training.tcia\">BraTS2021_UPENN-GBM_Seg-Task-Training.tcia<\/a><\/li>\r\n<\/ul>\r\n<\/li>\r\n \t<li>Classification Task (Training sets) \u00a0<a title=\"BraTS2021_TCIAderived_Class-Task-Training.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_TCIAderived_Class-Task-Training.tcia\" download=\"BraTS2021_TCIAderived_Class-Task-Training.tcia\">BraTS2021_TCIAderived_Class-Task-Training.tcia<\/a>\r\n<ul>\r\n \t<li><a href=\"\/wp-content\/uploads\/BraTS2021_CPTAC-GBM_Class-Task-Training.tcia\" download=\"BraTS2021_CPTAC-GBM_Class-Task-Training.tcia\" data-linked-resource-container-id=\"133073473\" data-linked-resource-container-version=\"41\" data-linked-resource-content-type=\"application\/octet-stream\" data-linked-resource-default-alias=\"BraTS2021_CPTAC-GBM_Class-Task-Training.tcia\" data-linked-resource-id=\"145755289\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\">BraTS2021_CPTAC-GBM_Class-Task-Training.tcia<\/a><\/li>\r\n \t<li><a href=\"\/wp-content\/uploads\/BraTS2021_TCGA-GBM_Class-Task-Training.tcia\" download=\"BraTS2021_TCGA-GBM_Class-Task-Training.tcia\" data-linked-resource-container-id=\"133073473\" data-linked-resource-container-version=\"41\" data-linked-resource-content-type=\"application\/octet-stream\" data-linked-resource-default-alias=\"BraTS2021_TCGA-GBM_Class-Task-Training.tcia\" data-linked-resource-id=\"145755340\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\">BraTS2021_TCGA-GBM_Class-Task-Training.tcia<\/a><\/li>\r\n \t<li><a title=\"BraTS2021_IvyGAP_Class-Task-Training.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_IvyGAP_Class-Task-Training.tcia\" download=\"BraTS2021_IvyGAP_Class-Task-Training.tcia\">BraTS2021_IvyGAP_Class-Task-Training.tcia<\/a><\/li>\r\n \t<li><a title=\"BraTS2021_UPENN-GBM_Class-Task-Training.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_UPENN-GBM_Class-Task-Training.tcia\" download=\"BraTS2021_UPENN-GBM_Class-Task-Training.tcia\">BraTS2021_UPENN-GBM_Class-Task-Training.tcia<\/a><\/li>\r\n<\/ul>\r\n<\/li>\r\n \t<li>Segmentation Task (Validation sets) <a title=\"BraTS2021_TCIAderived_Seg-Task-Validation.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_TCIAderived_Seg-Task-Validation.tcia\" download=\"BraTS2021_TCIAderived_Seg-Task-Validation.tcia\">BraTS2021_TCIAderived_Seg-Task-Validation.tcia<\/a>\r\n<ul>\r\n \t<li><a href=\"\/wp-content\/uploads\/BraTS2021_UPENN-GBM_Seg-Task-Validation.tcia\" download=\"BraTS2021_UPENN-GBM_Seg-Task-Validation.tcia\" data-linked-resource-container-id=\"133073473\" data-linked-resource-container-version=\"41\" data-linked-resource-content-type=\"application\/octet-stream\" data-linked-resource-default-alias=\"BraTS2021_UPENN-GBM_Seg-Task-Validation.tcia\" data-linked-resource-id=\"145755285\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\">BraTS2021_UPENN-GBM_Seg-Task-Validation.tcia<\/a><\/li>\r\n \t<li><a title=\"BraTS2021_IvyGAP_Seg-Task-Validation.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_IvyGAP_Seg-Task-Validation.tcia\" download=\"BraTS2021_IvyGAP_Seg-Task-Validation.tcia\">BraTS2021_IvyGAP_Seg-Task-Validation.tcia<\/a><\/li>\r\n \t<li><a title=\"BraTS2021_TCGA-LGG_Seg-Task-Validation.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_TCGA-LGG_Seg-Task-Validation.tcia\" download=\"BraTS2021_TCGA-LGG_Seg-Task-Validation.tcia\">BraTS2021_TCGA-LGG_Seg-Task-Validation.tcia<\/a><\/li>\r\n \t<li><a title=\"BraTS2021_CPTAC-GBM_Seg-Task-Validation.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_CPTAC-GBM_Seg-Task-Validation.tcia\" download=\"BraTS2021_CPTAC-GBM_Seg-Task-Validation.tcia\">BraTS2021_CPTAC-GBM_Seg-Task-Validation.tcia<\/a><\/li>\r\n \t<li><a title=\"BraTS2021_TCGA-GBM_Seg-Task-Validation.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_TCGA-GBM_Seg-Task-Validation.tcia\" download=\"BraTS2021_TCGA-GBM_Seg-Task-Validation.tcia\">BraTS2021_TCGA-GBM_Seg-Task-Validation.tcia<\/a><\/li>\r\n<\/ul>\r\n<\/li>\r\n \t<li>Classification Task (Validation sets) \u00a0<a title=\"BraTS2021_TCIAderived_Class-Task-Validation.tcia\" href=\"\/wp-content\/uploads\/BraTS2021_TCIAderived_Class-Task-Validation.tcia\" download=\"BraTS2021_TCIAderived_Class-Task-Validation.tcia\">BraTS2021_TCIAderived_Class-Task-Validation.tcia<\/a>\r\n<ul>\r\n \t<li><a href=\"\/wp-content\/uploads\/BraTS2021_UPENN-GBM_Class-Task-Validation.tcia\" download=\"BraTS2021_UPENN-GBM_Class-Task-Validation.tcia\" data-linked-resource-container-id=\"133073473\" data-linked-resource-container-version=\"41\" data-linked-resource-content-type=\"application\/octet-stream\" data-linked-resource-default-alias=\"BraTS2021_UPENN-GBM_Class-Task-Validation.tcia\" data-linked-resource-id=\"145755357\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\">BraTS2021_UPENN-GBM_Class-Task-Validation.tcia<\/a><\/li>\r\n \t<li><a href=\"\/wp-content\/uploads\/BraTS2021_IvyGAP_Class-Task-Validation.tcia\" download=\"BraTS2021_IvyGAP_Class-Task-Validation.tcia\" data-linked-resource-container-id=\"133073473\" data-linked-resource-container-version=\"41\" data-linked-resource-content-type=\"application\/octet-stream\" data-linked-resource-default-alias=\"BraTS2021_IvyGAP_Class-Task-Validation.tcia\" data-linked-resource-id=\"145755356\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\">BraTS2021_IvyGAP_Class-Task-Validation.tcia<\/a><\/li>\r\n \t<li><a href=\"\/wp-content\/uploads\/BraTS2021_TCGA-GBM_Class-Task-Validation.tcia\" download=\"BraTS2021_TCGA-GBM_Class-Task-Validation.tcia\" data-linked-resource-container-id=\"133073473\" data-linked-resource-container-version=\"41\" data-linked-resource-content-type=\"application\/octet-stream\" data-linked-resource-default-alias=\"BraTS2021_TCGA-GBM_Class-Task-Validation.tcia\" data-linked-resource-id=\"145755348\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\">BraTS2021_TCGA-GBM_Class-Task-Validation.tcia<\/a><\/li>\r\n \t<li><a href=\"\/wp-content\/uploads\/BraTS2021_CPTAC-GBM_Class-Task-Validation.tcia\" download=\"BraTS2021_CPTAC-GBM_Class-Task-Validation.tcia\" data-linked-resource-container-id=\"133073473\" data-linked-resource-container-version=\"41\" data-linked-resource-content-type=\"application\/octet-stream\" data-linked-resource-default-alias=\"BraTS2021_CPTAC-GBM_Class-Task-Validation.tcia\" data-linked-resource-id=\"145755346\" data-linked-resource-type=\"attachment\" data-linked-resource-version=\"1\">BraTS2021_CPTAC-GBM_Class-Task-Validation.tcia<\/a><\/li>\r\n<\/ul>\r\n<\/li>\r\n \t<li>We didn't split the UCSF-PDGM v1 data by BraTS task, but excerpted series in 299 cases are here as a faspex package:\u00a0 <a href=\"https:\/\/faspex.cancerimagingarchive.net\/aspera\/faspex?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjY3OSIsInBhc3Njb2RlIjoiZmEwODZjMDQyNGNkOGM4OTllZTRjY2VmZTE0ZGUyM2FkMjA3N2M5NSIsInBhY2thZ2VfaWQiOiI2NzkiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\">BraTS2021_UCSF-PDGMv1<\/a><\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>\r\n<h3>Notes about Image Registration:<\/h3>\r\n<ul>\r\n \t<li>Transformation matrices DICOM to NIfTI are not available.<\/li>\r\n \t<li>Segmentation task image volume have been set to <strong>x=y=240 voxels by z=155 voxels<\/strong>.<\/li>\r\n \t<li>All Radiogenomics Classifier task files are restored to<strong> original DICOM resolution &amp; orientation<\/strong> (thus volume may vary).<\/li>\r\n<\/ul>","publications_using":"TCIA maintains\u00a0<a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\">a list of publications<\/a> which leverage our data. If you have a manuscript you'd like to add please<a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\"> contact TCIA's Helpdesk<\/a>.","result_title":"RSNA-ASNR-MICCAI-BraTS-2021","species":false,"version_number":"1","date_updated":"2023-08-25","related_collections":[44065,42917,43903,42621,45385,41199,44721],"result_short_title":"RSNA-ASNR-MICCAI-BraTS-2021","subjects":"1480","related_analysis_results":false,"result_browse_title":"RSNA-ASNR-MICCAI-BraTS-2021","supporting_data":["Tumor segmentations"],"version_change_log":"","collections":"Below is a list of the Collections used in these analyses.\r\n\r\nSome 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=\"\/wp-content\/uploads\/TCIA-Restricted-License-20220519.pdf\">TCIA Restricted License Agreement<\/a> to <a href=\"mailto:help@cancerimagingarchive.net\">help@cancerimagingarchive.net<\/a> before accessing the data.\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 corresponding DICOM used in BraTS 2021 Segmentation Training set from\r\n\r\nCPTAC-GBM ,\u00a0TCGA-GBM ,\u00a0TCGA-LGG ,\u00a0ACRIN-FMISO-Brain (ACRIN 6684) ,\u00a0IvyGAP ,UPENN-GBM<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/BraTS2021_TCIAderived_Seg-Task-Training.tcia\" download=\"BraTS2021_TCIAderived_Seg-Task-Training.tcia\"><button><i><\/i> Download<\/button><\/a>\r\nDownload requires the <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<tr>\r\n<td>Original corresponding DICOM used in BraTS 2021 MGMT Classifier Training set from\r\n\r\nCPTAC-GBM ,\u00a0TCGA-GBM , IvyGAP ,\u00a0UPENN-GBM<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/BraTS2021_TCIAderived_Class-Task-Training.tcia\" download=\"BraTS2021_TCIAderived_Class-Task-Training.tcia\"><button><i><\/i> Download<\/button><\/a>\r\nDownload requires the <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<tr>\r\n<td>Original corresponding DICOM used in BraTS 2021 Segmentation Validation set from CPTAC-GBM ,\u00a0TCGA-GBM ,\u00a0TCGA-LGG ,\u00a0IvyGAP ,\u00a0UPENN-GBM<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/BraTS2021_TCIAderived_Seg-Task-Validation.tcia\" download=\"BraTS2021_TCIAderived_Seg-Task-Validation.tcia\"><button><i><\/i> Download<\/button><\/a>\r\nDownload requires the <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<tr>\r\n<td>Original corresponding DICOM used in BraTS 2021 MGMT Classifier Validation set from\r\n\r\nCPTAC-GBM ,\u00a0TCGA-GBM , \u00a0IvyGAP ,\u00a0UPENN-GBM<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/BraTS2021_TCIAderived_Class-Task-Validation.tcia\" download=\"BraTS2021_TCIAderived_Class-Task-Validation.tcia\"><button><i><\/i> Download<\/button><\/a>\r\nDownload requires the <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<tr>\r\n<td>Original corresponding imaging from UCSF-PDGM v1<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"https:\/\/faspex.cancerimagingarchive.net\/aspera\/faspex?context=eyJyZXNvdXJjZSI6InBhY2thZ2VzIiwidHlwZSI6ImV4dGVybmFsX2Rvd25sb2FkX3BhY2thZ2UiLCJpZCI6IjY3OSIsInBhc3Njb2RlIjoiZmEwODZjMDQyNGNkOGM4OTllZTRjY2VmZTE0ZGUyM2FkMjA3N2M5NSIsInBhY2thZ2VfaWQiOiI2NzkiLCJlbWFpbCI6ImhlbHBAY2FuY2VyaW1hZ2luZ2FyY2hpdmUubmV0In0=\"><button><i><\/i> Download<\/button><\/a>\r\n(Download and apply the\u00a0<a href=\"https:\/\/www.ibm.com\/aspera\/connect\/\">IBM-Aspera-Connect plugin\u00a0<\/a>to your browser to retrieve this faspex package)\r\n\r\n<\/div><\/td>\r\n<td><a href=\"https:\/\/creativecommons.org\/licenses\/by\/4.0\/\">CC BY 4.0<\/a><\/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.2018.3RJE41Q1\">CPTAC-GBM<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.RNYFUYE9\">TCGA-GBM<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.L4LTD3TK\">TCGA-LGG<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2018.vohlekok\">ACRIN-FMISO-Brain (ACRIN 6684)<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.XLwaN6nL\">IvyGAP<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/TCIA.709X-DN49\">UPENN-GBM<\/a><\/li>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/tcia.bdgf-8v37\" target=\"_blank\" rel=\"noopener\">UCSF-PDGM<\/a><\/li>\r\n<\/ul>","result_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\n<h3>A note about available TCIA data which were\u00a0<em>converted <\/em>for use in this Challenge: (Training, Validation, Test)<\/h3>\r\nDr. Bakas's group here provides <em>brain-extracted Segmentation task <\/em>BraTS 2021 challenge <strong>TRAINING <\/strong>and <strong>VALIDATION <\/strong>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 <em>brain-extracted <\/em>BraTS challenge <strong>TEST <\/strong>data in NIfTI, and segmentations to go with them (<a href=\"\/analysis-result\/brats-tcga-lgg\/\" target=\"_blank\" rel=\"noopener\">here<\/a> and <a href=\"\/analysis-result\/brats-tcga-gbm\/\" target=\"_blank\" rel=\"noopener\">here<\/a>, from the 2018 challenge, request via <a href=\"http:\/\/www.cancerimagingarchive.net\/support\/\">TCIA's Helpdesk<\/a>.\r\n\r\nThis group here provides <em>brain-extracted Classification task <\/em>BraTS 2021 challenge <strong>TRAINING <\/strong>and <strong>VALIDATION <\/strong>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 <strong>TEST <\/strong>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\n<strong>Please also note<\/strong> 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 <strong>represent our best effort <\/strong>at reconstructing the link to the BraTS source files.","collection_downloads":[46599,46601,46603,46605,46607],"result_featured_image":{"ID":"9095","post_author":"29","post_date":"2023-09-14 01:07:08","post_date_gmt":"2023-09-14 06:07:08","post_content":"","post_title":"BRATS_banner_noCaption","post_excerpt":"","post_status":"inherit","comment_status":"closed","ping_status":"closed","post_password":"","post_name":"brats_banner_nocaption","to_ping":"","pinged":"","post_modified":"2023-11-30 13:22:07","post_modified_gmt":"2023-11-30 19:22:07","post_content_filtered":"","post_parent":"45739","guid":"https:\/\/stage.cancerimagingarchive.net\/wp-content\/uploads\/BRATS_banner_noCaption.png","menu_order":"0","post_type":"attachment","post_mime_type":"image\/png","comment_count":"0","pod_item_id":"9095"},"result_acknowledgements":"We would like to acknowledge the individuals and institutions that have provided data for this collection:\r\n<ul>\r\n \t<li>Data used in this publication were obtained as part of the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge project through Synapse ID (syn25829067).<\/li>\r\n<\/ul>","hide_from_browse_table":"0","program":["Community"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/46609","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\/46609\/revisions"}],"predecessor-version":[{"id":47049,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/46609\/revisions\/47049"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media\/9095"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=46609"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}