MRI radiomics-based machine learning for preoperative prediction of Simpson grade in intracranial meningiomas: a pilot study

Background: meningiomas are the most frequent primary central nervous system tumors, and extent of resection is a key determinant of long-term tumor control. Quantitative preoperative MRI radiomics may support surgical planning by estimating the likelihood of achieving Simpson grade I versus II resection. This study evaluated the feasibility of an MRI radiomics-based machine learning approach for this purpose. Methods: a retrospective pilot study (2018–2024) included adults with intracranial meningioma and adequate preoperative contrast-enhanced T1-weighted MRI. Tumors were segmented in 3D Slicer and radiomic features were extracted using PyRadiomics under IBSI-compliant settings. The endpoint was binary (Simpson grade I vs II). Model selection used a selector–classifier benchmark with internal validation via nested leave-one-out cross-validation to mitigate data leakage. Performance was assessed using balanced accuracy, AUROC, AUPRC, and calibration (intercept, slope, Brier score). Uncertainty was estimated using bootstrap on out-of-fold predictions. Results: out of approximately 70 screened cases, 12 were included for modeling (Simpson I=4; Simpson II=8) with 126 candidate predictors. The best-performing configuration was LASSO (C=0.2) combined with linear discriminant analysis, achieving balanced accuracy 0.812, AUROC 0.844, AUPRC 0.867, and Brier score 0.129. Calibration was close to ideal (intercept ≈0.00; slope ≈0.97). Bootstrap 95% confidence intervals reflected uncertainty consistent with the small sample size. Conclusions: this pilot study supports the feasibility of a standardized MRI radiomics-based model for preoperative estimation of surgical resectability (Simpson I vs II) in intracranial meningiomas. Multicenter external validation is required to confirm generalizability and clinical utility.