{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["LaBella D"],"funding":["Foundation for the National Institutes of Health","Foundation for the National Institutes of Health (Foundation for the National Institutes of Health, Inc.)"],"pagination":["306"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12948943"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["13(1)"],"pubmed_abstract":["Meningiomas are the most common primary intracranial tumors, frequently requiring radiotherapy as a part of management. Effective radiotherapy planning for meningiomas necessitates accurate and consistent segmentation of target volumes on MRI, a process that is complex, labor-intensive, and dependent on expert expertise. The 2024 Brain Tumor Segmentation Challenge Meningioma Radiotherapy (BraTS-MEN-RT) Dataset addresses this problem by providing the largest multi-institutional collection of systematically annotated radiotherapy planning MRIs for meningiomas. Publicly accessible, this dataset comprises 570 radiotherapy planning 3D T1-weighted post-contrast MRIs at native resolutions, with 500 cases featuring expert-annotated gross tumor volumes (GTV). Annotations follow standardized radioth"],"journal":["Scientific data"],"pubmed_title":["The 2024 Brain Tumor Segmentation Challenge Meningioma Radiotherapy (BraTS-MEN-RT) dataset."],"pmcid":["PMC12948943"],"funding_grant_id":["NCI/ITCR U01CA242871","U24CA279629","NCI K08CA256045","U01CA242871"],"pubmed_authors":["Wang C","Mix M","Halasz LM","Moassefi M","Rudie JD","Al-Salihi O","Saluja R","Taylor P","de Verdier MC","Huang R","Barfoot T","McBurney-Lin S","Schumacher K","Mullikin TC","Vercauteren T","Gagnon L","Villanueva-Meyer J","Floyd SR","Ivory M","Menze B","Bakas S","Sachdev S","Kofler F","Anwar SM","Shapey J","Nada A","Anazodo U","Nedelec P","Bagci U","Warman P","Maleki N","Vollmuth P","Raleigh DR","Seibert TM","Faghani S","Abayazeed AH","Kirkpatrick JP","Sheller M","Baid U","Reitman ZJ","Adewole M","Kazerooni AF","Karargyris A","Jakab A","Moawad AW","Kassem H","Shiue K","Albrecht J","Linguraru MG","Aboian M","Conte GM","Puett C","Chung V","Chai R","Tahon NH","Hongwei B L","Pati S","Aristizabal A","Rauschecker AM","Lohmann P","Pease MW","Schwarz CG","LaBella D","Farid N","Calabrese E","Chia K","Leu J","Leu K","Velichko Y","Wiestler B","Vaios EJ","Hattangadi-Gluth JA"],"additional_accession":[]},"is_claimable":false,"name":"The 2024 Brain Tumor Segmentation Challenge Meningioma Radiotherapy (BraTS-MEN-RT) dataset.","description":"Meningiomas are the most common primary intracranial tumors, frequently requiring radiotherapy as a part of management. Effective radiotherapy planning for meningiomas necessitates accurate and consistent segmentation of target volumes on MRI, a process that is complex, labor-intensive, and dependent on expert expertise. The 2024 Brain Tumor Segmentation Challenge Meningioma Radiotherapy (BraTS-MEN-RT) Dataset addresses this problem by providing the largest multi-institutional collection of systematically annotated radiotherapy planning MRIs for meningiomas. Publicly accessible, this dataset comprises 570 radiotherapy planning 3D T1-weighted post-contrast MRIs at native resolutions, with 500 cases featuring expert-annotated gross tumor volumes (GTV). Annotations follow standardized radioth","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-07-16T23:24:03.006Z","creation":"2026-07-12T03:11:11.408Z"},"accession":"S-EPMC12948943","cross_references":{"pubmed":["41593091"],"doi":["10.1038/s41597-026-06649-x"]}}