{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T07:14:22Z","timestamp":1775632462630,"version":"3.50.1"},"reference-count":13,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T00:00:00Z","timestamp":1696896000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>Meningiomas are the most common primary brain tumors in adults with management varying widely based on World Health Organization (WHO) grade. However, there are limited datasets available for researchers to develop and validate radiomic models. The purpose of our manuscript is to report on the first dataset of meningiomas in The Cancer Imaging Archive (TCIA).<\/jats:p><\/jats:sec><jats:sec><jats:title>Acquisition and validation methods<\/jats:title><jats:p>The dataset consists of pre\u2010operative MRIs from 96 patients with meningiomas who underwent resection from 2010\u20132019 and include axial T1post and T2\u2010FLAIR sequences\u201455 grade 1 and 41 grade 2. Meningioma grade was confirmed based on the 2016 WHO Bluebook classification guideline by two neuropathologists and one neuropathology fellow. The hyperintense T1post tumor and hyperintense T2\u2010FLAIR regions were manually contoured on both sequences and resampled to an isotropic resolution of 1\u00a0\u00d7\u00a01\u00a0\u00d7\u00a01 mm<jats:sup>3<\/jats:sup>. The entire dataset was reviewed by a certified medical physicist.<\/jats:p><\/jats:sec><jats:sec><jats:title>Data format and usage notes<\/jats:title><jats:p>The data was imported into TCIA for storage and can be accessed at <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"https:\/\/doi.org\/10.7937\/0TKV-1A36\">https:\/\/doi.org\/10.7937\/0TKV\u20101A36<\/jats:ext-link>. The total size of the dataset is 8.8GB, with 47\u00a0519 individual Digital Imaging and Communications in Medicine (DICOM) files consisting of 384 image series, and 192 structures.<\/jats:p><\/jats:sec><jats:sec><jats:title>Potential applications<\/jats:title><jats:p>Grade 1 and 2 meningiomas have different treatment paradigms and are often treated based on radiologic diagnosis alone. Therefore, predicting grade prior to treatment is essential in clinical decision\u2010making. This dataset will allow researchers to create models to auto\u2010differentiate grade 1 and 2 meningiomas as well as evaluate for other pathologic features including mitotic index, brain invasion, and atypical features. Limitations of this study are the small sample size and inclusion of only two MRI sequences. However, there are no meningioma datasets on TCIA and limited datasets elsewhere although meningiomas are the most common intracranial tumor in adults.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16763","type":"journal-article","created":{"date-parts":[[2023,10,10]],"date-time":"2023-10-10T12:29:10Z","timestamp":1696940950000},"page":"2334-2344","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A repository of grade 1 and 2 meningioma MRIs in a public dataset for radiomics reproducibility tests"],"prefix":"10.1002","volume":"51","author":[{"given":"April","family":"Vassantachart","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology LAC+USC Medical Center  Los Angeles California USA"},{"name":"Department of Radiation Oncology Keck School of Medicine University of Southern California  Los Angeles California 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