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Radiomics features were extracted from contrast-enhanced T1-weighted MRI (CE-T1WI) using the\u00a0pyradiomics\u00a0package, followed by feature selection via\u00a0LASSO regression. Predictive models (logistic regression, decision tree, support vector machine [SVM], adaptive boosting) were evaluated. Clinical variables (peritumoral edema index and monocyte count) were integrated to try to improve the predictive performance. Model efficacy was assessed using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Four key radiomics features were identified as significant discriminators of tumor grade. The logistic regression model demonstrated superior predictive performance over decision trees, SVMs, and adaptive boosting methods. The inclusion of the peritumoral edema index and monocyte count increased the AUC to 0.801 (95% CI 0.753\u20130.869) in the training set. However, in the validation set, the radiomics model achieved the best performance, with an AUC of 0.770 (95% CI 0.670\u20130.869).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The radiomics-based model effectively predicts high-grade meningioma and demonstrates superior performance compared to the clinical and combined models. This study advances the precision of meningioma grading, offering significant implications for treatment planning and patient management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11596-026-00199-4","type":"journal-article","created":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T05:47:22Z","timestamp":1777873642000},"page":"704-712","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Nomogram for Predicting Meningioma Grade Based on Radiomics Features and Clinical Characteristics"],"prefix":"10.1007","volume":"46","author":[{"given":"Peng-fei","family":"Yan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bao-ping","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhen","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao-jun","family":"Shi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3798-7393","authenticated-orcid":false,"given":"Dong-xiao","family":"Yao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,5,4]]},"reference":[{"issue":"5","key":"199_CR1","first-page":"381","volume":"71","author":"KD Miller","year":"2021","unstructured":"Miller KD, Ostrom QT, Kruchko C, et al. 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