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Four base models used contrast-enhanced T1-weighted MRI: tumor\u2009+\u2009peritumoral radiomics, an nnU-Net segmentation-based classifier, frozen DINOv3 transfer-learning features, and clinical variables. All 15 combinations were prespecified and stacked using a linear meta-learner trained on development out-of-fold predictions. Frozen models were externally evaluated without modification.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      WHO 2\u20133 tumors comprised 24.6% overall (112\/455; development 20.5%; external 37.6%). External AUCs were 0.732 for nnU-Net, 0.703 for DINOv3, 0.645 for clinical, 0.628 for radiomics. The multimodal ensemble (nnU-Net+DINOv3+Clinical) achieved the highest external AUC (0.742), exceeding radiomics (\u0394AUC +0.114,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.008) but not nnU-Net (\u0394AUC +0.011,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.737). Compared with the clinical model, discrimination was higher but nonsignificant at the prespecified threshold (\u0394AUC +0.098,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009=\u20090.050), while reclassification improved (IDI +0.109,\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.001). Radiomics-containing combinations performed strongly in development but lost most discrimination externally. The multimodal ensemble had an external calibration slope of 1.32 (Brier 0.190) and net benefit above default strategies at thresholds\u2009\u2265\u20090.23.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The multimodal ensemble achieved the highest external AUC but did not significantly exceed nnU-Net, which was the most transportable single predictor of meningioma grade. Clinical variables did not improve discrimination but enabled interpretable feature-level attribution. Handcrafted radiomics lacked external transportability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11060-026-05827-6","type":"journal-article","created":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T04:56:34Z","timestamp":1792040194000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["MRI-based preoperative WHO grade prediction in meningioma: development and comparison of deep learning, radiomics, clinical, and combined models in multicenter data"],"prefix":"10.1007","volume":"180","author":[{"given":"Cagin","family":"Huryol","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umid","family":"Sulaimanov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Murat","family":"Yuce","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nafiye","family":"Sanlier","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fikret","family":"Ozturk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oyku","family":"Ozturk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Irem","family":"Altun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gular","family":"Ismayilova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huseyin","family":"Bozkurt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taha Yasin","family":"Altunkaya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ferdi","family":"Afsin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abdullah Soydan","family":"Mahmutoglu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amine","family":"Durmus","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Burak","family":"Karaaslan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilhan","family":"Aydin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehmet Sabri","family":"Gurbuz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmet Tan","family":"Cimilli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Okan","family":"Turk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ufuk","family":"Erginoglu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mustafa K.","family":"Baskaya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,14]]},"reference":[{"issue":"21","key":"5827_CR1","doi-asserted-by":"publisher","first-page":"2161","DOI":"10.2217\/fon-2018-0006","volume":"14","author":"RA Buerki","year":"2018","unstructured":"Buerki RA, Horbinski CM, Kruser T et al. 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Approval was granted by the Clinical Research Ethics Committee of the University of Health Sciences, Istanbul Training and Research Hospital, Istanbul, T\u00fcrkiye (Date: August 7, 2026; No.: 267). 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