{"id":45739,"date":"2023-11-20T05:31:15","date_gmt":"2023-11-20T11:31:15","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/brats-tcga-lgg\/"},"modified":"2024-11-07T13:00:49","modified_gmt":"2024-11-07T19:00:49","slug":"brats-tcga-lgg","status":"publish","type":"tcia_analysis_result","link":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/brats-tcga-lgg\/","title":{"rendered":"BRATS-TCGA-LGG"},"featured_media":0,"template":"","class_list":["post-45739","tcia_analysis_result","type-tcia_analysis_result","status-publish"],"cancer_types":["Low Grade Glioma"],"citations":[45727,45729,9225],"result_doi":"10.7937\/K9\/TCIA.2017.GJQ7R0EF","result_download_info":"Please contact the\u00a0<a href=\"https:\/\/www.cancerimagingarchive.net\/support\">helpdesk<\/a> to request access to the <strong>Test<\/strong> arm of the NIfTI data files (43 Participants, 366 MB).","result_downloads":[45731,45733],"version_change_log_archived":"Version 1 (Current): 2017\/07\/17\r\n   Data TypeDownload all or Query\/FilterImages - 108 subjects (DICOM, 8.5 GB)  Note:\u00a0Limited Access.Click the\u00a0Download\u00a0\u00a0button\u00a0to save a \".tcia\" manifest file to your computer, which you must open with the\u00a0\u00a0NBIA Data RetrieverProcessed images with segmentations and radiomic features - 65 subjects (NIFTI, 536 MB)  Download and apply the\u00a0IBM-Aspera-Connect plugin\u00a0to your browserBRATS 2018 Test Data Set - 43 subjects (NIFTI, 366 MB)Please contact the\u00a0  helpdesk\u00a0  to request access to these files.","versions":false,"additional_resources":"","cancer_locations":["Brain"],"publications_related":"","result_page_accessibility":"Limited","detailed_description":"Data resulting from this experiment is available in the following formats:\r\n<ul>\r\n \t<li>(source data in DICOM image format)<\/li>\r\n \t<li>Processed images with segmentations (NIFTI) and radiomic features (CSV):\r\n<ul>\r\n \t<li>TrainingProcessed images with segmentations and radiomic features - 65 subjects (NIfTI, zip, 536 MB)<\/li>\r\n \t<li>BraTS Test Data Set - 43 subjects (NIfTI, zip, 366 MB)<\/li>\r\n<\/ul>\r\n<\/li>\r\n<\/ul>","publications_using":"","result_title":"Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection","species":false,"version_number":"1","date_updated":"2017-07-17","related_collections":[44065],"result_short_title":"BraTS-TCGA-LGG","subjects":"108","related_analysis_results":[45847],"result_browse_title":"Segmentation Labels and Radiomic Features for the Pre-operative Scans of the TCGA-LGG collection (BraTS-TCGA-LGG)","supporting_data":["Tumor segmentations","radiomic image features"],"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 Images from <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.L4LTD3TK\">TCGA-LGG<\/a> (DICOM, 8.5 GB, 108 Subjects)<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/doiJNLP-JAMS4RFq.tcia\" download=\"doiJNLP-JAMS4RFq.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n(Requires\u00a0<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\nPlease (for DICOM format files) request both Collections <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.L4LTD3TK\">TCGA-LGG<\/a> and\u00a0<a href=\"https:\/\/cancerimagingarchive.net\/analysis-result\/brats-tcga-lgg\/\" target=\"_blank\" rel=\"noopener\">BraTS-TCGA-LGG<\/a> in your Agreement.\r\n<ul>\r\n \t<li><a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.L4LTD3TK\">TCGA-LGG<\/a><\/li>\r\n<\/ul>","result_summary":"This data container describes both computer-aided and manually-corrected segmentation labels for the pre-operative multi-institutional scans of <a href=\"https:\/\/doi.org\/10.7937\/K9\/TCIA.2016.L4LTD3TK\">The Cancer Genome Atlas (TCGA) Low Grade Glioma (LGG) collection<\/a>, publicly available in The Cancer Imaging Archive (TCIA), coupled with a rich panel of radiomic features along with their corresponding skull-stripped and co-registered multimodal (i.e. T1, T1-Gd, T2, T2-FLAIR) magnetic resonance imaging (MRI) volumes in NIfTI format. Pre-operative multimodal MRI scans were identified in the TCGA-LGG collection via radiological assessment. These scans were initially skull-stripped and co-registered, before their tumor segmentation labels were produced by an automated hybrid generative-discriminative method, ranked first during the International multimodal BRAin Tumor Segmentation challenge (BRATS 2015). These segmentation labels were revised and any label misclassifications were manually corrected by an expert board-certified neuroradiologist. The final labels were used to extract a rich panel of imaging features, including intensity, volumetric, morphologic, histogram-based and textural parameters, as well as spatial information and diffusion properties extracted from glioma growth models. The generated computer-aided and manually-revised labels enable quantitative computational and clinical studies without the need to repeat manual annotations whilst allowing for comparison across studies. They can also serve as a set of manually-annotated gold standard labels for performance evaluation in computational challenges. The provided panel of radiomic features may facilitate research integrative of the molecular characterization offered by TCGA, and hence allow associations with molecular markers, clinical outcomes, treatment responses and other endpoints, by researchers without sufficient computational background to extract such features.","collection_downloads":[45737],"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":"","hide_from_browse_table":"0","program":["Community"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45739","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\/45739\/revisions"}],"predecessor-version":[{"id":47033,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45739\/revisions\/47033"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=45739"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}