{"id":5555,"date":"2023-09-04T03:07:56","date_gmt":"2023-09-04T03:07:56","guid":{"rendered":"https:\/\/cm.vastapps.dev\/tcia-collection\/brain-tumor-progression\/"},"modified":"2023-09-13T11:57:02","modified_gmt":"2023-09-13T11:57:02","slug":"brain-tumor-progression","status":"publish","type":"tcia_collection","link":"https:\/\/cm.vastapps.dev\/tcia-collection\/brain-tumor-progression\/","title":{"rendered":"BRAIN-TUMOR-PROGRESSION"},"featured_media":0,"template":"","citation-tax":[],"cancer_types":["Brain Cancer"],"citations":[4395,2925],"collection_doi":"10.7937\/K9\/TCIA.2018.15quzvnb","collection_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 TCIA Restricted License Agreement<\/a> to help@cancerimagingarchive.net<\/a> before accessing the data.\n\nClick the Versions tab for more info about data releases.","collection_downloads":[4926],"full_export":"

Summary<\/h2>This collection includes datasets from 20 subjects with primary newly diagnosed glioblastoma who were treated with surgery and standard concomitant chemo-radiation therapy (CRT) followed by adjuvant chemotherapy.\u00a0 Two MRI exams are included for each patient: within 90 days following CRT completion and at progression (determined clinically, and based on a combination of clinical performance and\/or imaging findings, and punctuated by a change in treatment or intervention).\u00a0 <\/span>

All image sets are in DICOM format and contain T1w (pre and post-contrast agent), FLAIR, T2w, ADC, normalized cerebral blood flow, normalized relative cerebral blood volume, standardized relative cerebral blood volume, and binary tumor masks (generated using T1w images).\u00a0 The perfusion images were generated from dynamic susceptibility contrast (GRE-EPI DSC) imaging following a preload of contrast agent.\u00a0 All of the series are co-registered with the T1+C images.\u00a0 The intent of this dataset is for assessing deep learning algorithm performance to predict tumor progression.<\/span> <\/span><\/p>

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