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Volumetric assessment typically requires manual segmentation, which is time-consuming and associated with interrater variability. This study aimed to develop and validate a deep learning-based model for the automated segmentation of meningiomas and associated peritumoral edema on preoperative magnetic resonance imaging (MRI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We trained a standard nnU-Net deep learning model on contrast-enhanced T1-weighted and FLAIR MRI scans from 100 patients treated at the University Hospital of Zurich. The model was then externally validated on 88 cases from the meningioma SEG-Class dataset from the Cancer Imaging Archive. Segmentation performance was assessed using the Dice similarity coefficient, Jaccard index, and 95th percentile Hausdorff distance.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model achieved mean Dice scores of 0.87\u2009\u00b1\u20090.23 for meningioma segmentation and 0.63\u2009\u00b1\u20090.38 for peritumoral edema in internal cross-validation. On the external validation set, the model achieved scores of 0.86\u2009\u00b1\u20090.17 for meningioma segmentation and 0.31\u2009\u00b1\u20090.35 for edema.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The deep learning model demonstrated high accuracy in segmenting meningiomas and modest performance for peritumoral edema. These results support the potential utility of automated segmentation tools in clinical workflows. Future work should focus on validating model performance across larger multi-center datasets.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00234-026-04152-z","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T09:09:10Z","timestamp":1787216950000},"page":"2917-2926","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated deep learning\u2013based segmentation and volumetric analysis of meningiomas"],"prefix":"10.1007","volume":"68","author":[{"given":"Daniel","family":"de Wilde","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Olivier","family":"Zanier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aron","family":"Alakmeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stefanos","family":"Voglis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ulf C.","family":"Schneider","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Regli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carlo","family":"Serra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Victor E.","family":"Staartjes","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gustav","family":"Burstr\u00f6m","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raffaele","family":"Da Mutten","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,20]]},"reference":[{"issue":"Suppl 5","key":"4152_CR1","doi-asserted-by":"publisher","first-page":"v1","DOI":"10.1093\/neuonc\/noz150","volume":"21","author":"QT Ostrom","year":"2019","unstructured":"Ostrom QT et al (2019) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2012\u20132016. 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Ethical approval was obtained from the Cantonal Ethics Committee of Zurich (BASEC ID: 2023\u2009\u2212\u200900688). Patients consented to research use of their collected medical data. The TCIA data usage policy was considered.","order":1,"name":"Ethics","label":"Ethics approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that the article and its contents were composed in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.","order":3,"name":"Ethics","label":"Conflict of interest","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"During the preparation of this work the authors used ChatGPT-4o (Open AI, San Francisco, CA) to receive suggestions for improving sentence structure and word choice. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.","order":4,"name":"Ethics","label":"Generative AI and AI-assisted technologies in the writing process","group":{"name":"EthicsHeading","label":"Declarations"}}]}}