Therapeutic hybrid intelligence with neural and knowledge-based expert reasoning for SRS (THINKERS): an AI model for meningioma.

BACKGROUND: Prescription dose selection for intracranial meningiomas treated with stereotactic radiosurgery remains guided by tumor volume, anatomical constraints, proximity to critical structures, and institutional practice rather than individualized estimates of long-term local progression. We developed THINKERS-Meningioma, a mixture-of-experts artificial intelligence framework for personalized dose evaluation after stereotactic radiosurgery. METHODS: We performed a retrospective single-center study of 800 patients with intracranial meningiomas treated with stereotactic radiosurgery. Variables available before or at treatment were used to train a mixture-of-experts neural network with discrete-time survival modeling. Margin dose was incorporated as a queryable input to enable repeated candidate dose evaluation. Internal validation used stratified 5-fold cross-validation and a stratified holdout test split. Performance was assessed using area under the receiver operating characteristic curve (AUC) for 5-year local tumor progression, mean absolute error (MAE) for expected time to local progression, Brier score, and calibration metrics. RESULTS: In stratified cross-validation, THINKERS-Meningioma achieved a raw mean AUC of 0.854 ± 0.077 and calibrated mean AUC of 0.861 ± 0.088 for 5-year local tumor progression. Raw and calibrated Brier scores were 0.0021 ± 0.0020 and 0.0016 ± 0.0024, respectively. The cross-validation MAE was 1.67 ± 0.20 months. In the stratified holdout set, raw AUC was 0.841 and calibrated AUC was 0.861 (95% CI, 0.829-0.915). MAE for expected time to local progression was 1.71 months (95% CI, 1.00-2.01). CONCLUSIONS: THINKERS-Meningioma provides an internally validated framework for individualized 5-year local progression prediction and dose-policy evaluation after stereotactic radiosurgery for intracranial meningiomas. CLINICAL TRIAL NUMBER: Not applicable.