Five predictors of meningioma surgery: an AI analysis

BACKGROUND: Meningioma surgery aims to preserve neurological function, usually measured by the Karnofsky Performance Scale (KPS), but predicting postoperative KPS change remains challenging. METHODS: We retrospectively analyzed 397 meningioma resections from 14 Italian centers. Preoperative clinical and MRI features (age, tumor size, eloquent-area location, vessel and cranial-nerve involvement, prior surgery, baseline KPS, etc.) were used to train classifiers (logistic GAM, weighted logistic regression, linear/RBF SVM, random forest) with cross-validation. We compared performance to the Staartjes et al. GAM_MCS model (predicting KPS drop ≥10). RESULTS: All models achieved only moderate discrimination for new deficits: AUC ≈0.65-0.70 (Staartjes2 GAM_MCS 0.692; logistic regression 0.694). Predicting major changes was more accurate: for KPS drops ≥20 or ≥30 points, AUC ≈0.76-0.77, and for any postoperative improvement AUC ≈0.81, whereas 10-point declines were hardest (AUC ≈0.70). For example, weighted logistic regression yielded AUCs of 0.697, 0.765, 0.762, and 0.811 for predicting KPS drops ≥10, ≥20, ≥30, and any improvement, respectively. The most important predictors were anatomical complexity factors: eloquent-area involvement, major vessel manipulation, and tumor size (consistent with the "Big Five" risk factors). CONCLUSIONS: Preoperative anatomical complexity remains the dominant predictor of postoperative functional outcome. Logistic-based ML classifiers had moderate overall accuracy but better performance for extreme outcomes (major decline or clear improvement). Using a higher threshold (e.g. ≥20-point KPS drop) improved discrimination and aligns with clinically meaningful outcome definitions. These findings support emphasizing high-impact anatomical features and outcome thresholds to improve prognostic models.