{"id":45635,"date":"2023-11-20T05:24:48","date_gmt":"2023-11-20T11:24:48","guid":{"rendered":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/tcga-breast-radiogenomics\/"},"modified":"2025-03-20T13:45:37","modified_gmt":"2025-03-20T18:45:37","slug":"tcga-breast-radiogenomics","status":"publish","type":"tcia_analysis_result","link":"https:\/\/stage.cancerimagingarchive.net\/analysis-result\/tcga-breast-radiogenomics\/","title":{"rendered":"TCGA-BREAST-RADIOGENOMICS"},"featured_media":0,"template":"","class_list":["post-45635","tcia_analysis_result","type-tcia_analysis_result","status-publish"],"cancer_types":["Breast"],"citations":[45611,45613,45615,45617,45619,45621,9225],"result_doi":"10.7937\/K9\/TCIA.2014.8SIPIY6G","result_download_info":"","result_downloads":[45623,45625,45627,45629,45631],"version_change_log_archived":"Version 1 (Current): 2018\/09\/04\r\nData TypeDownload all or Query\/FilterImages (DICOM)Annotations (XLS)Segmentations (ZIP, XLS) Multi-gene Assays (XLS)Clinical Data (XLS)","versions":false,"additional_resources":"","cancer_locations":["Breast"],"publications_related":"","result_page_accessibility":"Public","detailed_description":"<u><strong>How to use the Segmentations<\/strong><\/u>\r\n\r\nWith regards to the naming structure, *S2-1.les: S2 means DCE-MRI sequence 2, lesion #1. Sometimes, there are multiple DCE-MRI sequences on TCIA data, and so\u00a0the team used the sequence that corresponded to\u00a0the one on which the radiologists annotated the truth.\u00a0 Each of our tumor segmentation files is a binary file, consisting of the following format:\r\n\r\n1. six uint16 values for the inclusive coordinates of the lesion\u2019s cuboid , relative to the image:\r\ny_start y_end\r\nx_start x_end\r\nz_start z_end\r\n\r\n2. the N int8 on\/off voxels (0 or 1) for the above specified cube, where N = (y_end y_start +1) * (x_end - x_start + 1) * (z_end - z_start + 1).\r\n\r\nA voxel value of 1 denotes that it is part of the lesion, while a value of zero denotes it is not.\r\n\r\nPlease reference these data\u00a0 extracted using version\u00a0 <strong> <u>V2010<\/u> <\/strong> \u00a0of the UChicago MRI Quantitative Radiomics workstation.\r\n\r\n&nbsp;\r\n\r\nThe LES file is binary in format and contains the coordinates and shape mask volume of the lesion.\u00a0It consists of six 2-byte short integer values which represent a 3x2 array of y,x,z start and end points.\u00a0The remainder of the file contains the 1-byte mask values for the lesion voxels.\r\n\r\nThe following\u00a0<b>MATLAB<\/b>\u00a0statement reads a LES file:\r\n\r\n&nbsp;\r\n\r\n<span style=\"color: #00875a;\"><span class=\"error\">[binles, lesRange]<\/span><\/span>\u00a0=\u00a0<span style=\"color: #00875a;\">loadlesion(targetfile)<\/span>;\r\n\r\n&nbsp;\r\n\r\nwhere\u00a0<span style=\"color: #00875a;\">targetfile\u00a0<\/span>is a string of the file name path.\r\n\r\n&nbsp;\r\n\r\nand where<span style=\"color: #de350b;\">\u00a0loadlesion.m<\/span>\u00a0is:\r\n\r\n&nbsp;\r\n\r\n<span style=\"color: #de350b;\">function\u00a0<span class=\"error\">[binles,rg]<\/span>=loadlesion(targetfile)<\/span>\r\n\r\n<span style=\"color: #de350b;\">\u00a0 \u00a0 fid=fopen(targetfile,'r');<\/span>\r\n\r\n<span style=\"color: #de350b;\">\u00a0 \u00a0 if fid ~=-1<\/span>\r\n\r\n<span style=\"color: #de350b;\">\u00a0 \u00a0 \u00a0 \u00a0 rg=fread(fid,<span class=\"error\">[3 2]<\/span>,'uint16')<\/span>\r\n\r\n<span style=\"color: #de350b;\">\u00a0 \u00a0 \u00a0 \u00a0 sz=rg(:,2)-rg(:,1)+1;<\/span>\r\n\r\n<span style=\"color: #de350b;\">\u00a0 \u00a0 \u00a0 \u00a0 binles=fread(fid,<span class=\"error\">[prod(sz) 1]<\/span>,'int8');<\/span>\r\n\r\n<span style=\"color: #de350b;\">\u00a0 \u00a0 \u00a0 \u00a0 binles=reshape(binles,sz');<\/span>\r\n\r\n<span style=\"color: #de350b;\">\u00a0 \u00a0 \u00a0 \u00a0 fclose(fid);<\/span>\r\n\r\n<span style=\"color: #de350b;\">\u00a0 \u00a0 \u00a0 \u00a0 range=<span class=\"error\">[rg(5)-rg(2)+1,rg(4)-rg(1)+1,rg(6)-rg(3)+1]<\/span><\/span>\r\n\r\n<span style=\"color: #de350b;\">\u00a0 \u00a0 else<\/span>\r\n\r\n<span style=\"color: #de350b;\">\u00a0 \u00a0 \u00a0 \u00a0 binles=[];<\/span>\r\n\r\n<span style=\"color: #de350b;\">\u00a0 \u00a0 \u00a0 \u00a0 rg=[];<\/span>\r\n\r\n<span style=\"color: #de350b;\">\u00a0 \u00a0 end<\/span>\r\n\r\n<span style=\"color: #de350b;\">end<\/span>\r\n\r\n&nbsp;\r\n\r\nFor example, if\r\n\r\nrg =\r\n\r\n122\u00a0\u00a0\u00a0150\r\n\r\n327\u00a0\u00a0\u00a0379\r\n\r\n71\u00a0 \u00a0\u00a084\r\n\r\nthen\r\n\r\nyEnd = 150 and yStart = 122\r\n\r\nxEnd = 379 and xStart = 327\r\n\r\nzEnd = 84 and zStart = 71","publications_using":"TCIA maintains <a href=\"https:\/\/www.cancerimagingarchive.net\/publications\/\"> a list of publications<\/a> that leverage TCIA 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":"TCGA Breast Phenotype Research Group Data sets","species":["Human"],"version_number":"1","date_updated":"2018-09-04","related_collections":[43753],"result_short_title":"TCGA-Breast-Radiogenomics","subjects":"84","related_analysis_results":false,"result_browse_title":"TCGA Breast Phenotype Research Group Data sets (TCGA-Breast-Radiogenomics)","supporting_data":["multi-gene assays"],"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.AB2NAZRP\">TCGA-BRCA<\/a> (DICOM, 52GB)<\/td>\r\n<td>\r\n<div>\r\n\r\n<a href=\"\/wp-content\/uploads\/doiJNLP-I50pw3Gc.tcia\" download=\"doiJNLP-I50pw3Gc.tcia\"><button><i><\/i> Download<\/button><\/a>\r\n\r\n(Open with 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:\/\/creativecommons.org\/licenses\/by\/3.0\/\">CC BY 3.0<\/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.AB2NAZRP\">TCGA-BRCA<\/a><\/li>\r\n<\/ul>","result_summary":"At the time of our study, 108 cases with breast MRI data were available in the\u00a0<a href=\"\/collection\/tcga-brca\/\" target=\"_blank\" rel=\"noopener\">The Cancer Genome Atlas Breast Invasive Carcinoma Collection (TCGA-BRCA)<\/a>\u00a0collection. In order to minimize variations in image quality across the multi-institutional cases we included only breast MRI studies acquired on GE 1.5 Tesla magnet strength scanners (GE Medical Systems, Milwaukee, Wisconsin, USA) scanners, yielding a total of 93 cases. We then excluded cases that had missing images in the dynamic sequence (1 patient), or at the time did not have gene expression analysis available in the\u00a0<a href=\"http:\/\/tcga-data.nci.nih.gov\/\">TCGA Data Portal<\/a> (8 patients). After these criteria, a dataset of 84 breast cancer patients resulted, with MRIs from four institutions: Memorial Sloan Kettering Cancer Center, the Mayo Clinic, the University of Pittsburgh Medical Center, and the Roswell Park Cancer Institute. The resulting cases contributed by each institution were 9 (date range 1999-2002), 5 (1999-2003), 46 (1999-2004), and 24 (1999-2002), respectively. The dataset of biopsy proven invasive breast cancers included 74 (88%) ductal, 8 (10%) lobular, and 2 (2%) mixed. Of these, 73 (87%) were ER+, 67 (80%) were PR+, and 19 (23%) were HER2+.\u00a0 Various types of analyses were conducted using the combined imaging, genomic, and clinical data.\u00a0 Those analyses are described within several manuscripts created by the group (cited below).\u00a0 Additional information about the methodology for how the Radiologist Annotations file can be found on the <a href=\"https:\/\/wiki.cancerimagingarchive.net\/x\/soRF\">TCGA Breast Image Feature Scoring Project<\/a> page.","collection_downloads":[45633],"result_featured_image":false,"result_acknowledgements":"","hide_from_browse_table":"0","program":["TCGA"],"_links":{"self":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45635","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\/45635\/revisions"}],"predecessor-version":[{"id":46987,"href":"https:\/\/stage.cancerimagingarchive.net\/api\/v1\/analysis-results\/45635\/revisions\/46987"}],"wp:attachment":[{"href":"https:\/\/stage.cancerimagingarchive.net\/api\/wp\/v2\/media?parent=45635"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}