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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Brain extraction is essential in neuroimaging studies for patient privacy and optimizing computational analyses. Manual creation of 3D brain masks is labor-intensive, prompting the development of automatic computational methods. Robust quality control (QC) is hence necessary for the effective use of these methods in large-scale studies. However, previous automated QC methods have been limited in flexibility regarding algorithmic architecture and data adaptability. We introduce a novel approach inspired by a statistical outlier detection paradigm to efficiently identify potentially erroneous data. Our QC method is unsupervised, resource-efficient, and requires minimal parameter tuning. We quantitatively evaluated its performance using morphological features of brain masks generated from three automated brain extraction tools across multi-institutional pre- and post-operative brain glioblastoma MRI scans. We achieved an accuracy of 0\n                    <jats:italic>.<\/jats:italic>\n                    9 for pre- and 0\n                    <jats:italic>.<\/jats:italic>\n                    87 for post-operative scans, thus demonstrating the effectiveness of our proposed QC tool for brain extraction. Additionally, the method shows potential for other tasks where a user-defined feature space can be defined. Our novel QC approach offers significant improvements in flexibility and efficiency over previous methods. It is a valuable tool, targeting reassurance of brain masks in neuroimaging and can be adapted for other applications requiring robust QC mechanisms.\n                  <\/jats:p>","DOI":"10.1007\/s10278-025-01570-y","type":"journal-article","created":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T12:58:55Z","timestamp":1750856335000},"page":"1608-1618","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["An Unsupervised Brain Extraction Quality Control Approach for Efficient Neuro-Oncology Studies"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2243-8487","authenticated-orcid":false,"given":"Sarthak","family":"Pati","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stefan","family":"Wagner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Siddhesh","family":"Thakur","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Evan","family":"Calabrese","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Russell","family":"Shinohara","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Spyridon","family":"Bakas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,25]]},"reference":[{"key":"1570_CR1","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1038\/s41698-024-00575-0","volume":"8","author":"S Khalighi","year":"2024","unstructured":"Khalighi S, Reddy K, Midya A, Pandav KB, Madabhushi A, and Abe- dalthagafi M. 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