Therapeutic hybrid intelligence with neural and knowledge-based expert reasoning for SRS: an AI model for breast cancer brain metastases.

Background: Prescription dose selection for breast cancer brain metastases treated with stereotactic radiosurgery remains largely guided by tumor size, anatomical constraints, and institutional practice rather than individualized tumor-specific estimates of local failure. We developed THINKERS-Breast, a mixture-of-experts artificial intelligence framework for personalized dose evaluation after Gamma Knife radiosurgery. Methods: We performed a retrospective single-center tumor-level study of breast cancer brain metastases treated with Gamma Knife 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 grouped 5-fold cross-validation and a grouped holdout test split by patient. Performance was assessed using area under the receiver operating characteristic curve (AUC) for 12-month local failure, mean absolute error (MAE) for expected time to local failure, Brier score, and calibration metrics. Results: The cohort included 3,098 tumors from 504 patients. In grouped cross-validation, THINKERS-Breast achieved raw mean AUC >0.807 and calibrated mean AUC of 0.864 for 12-month local failure. Raw and calibrated Brier scores were <0.14 and <0.16, respectively, with calibration intercepts ranging from -0.31 to +0.27. In the grouped holdout set, AUC was 0.781 (95% CI, 0.704-0.857), and MAE for expected time to local failure was 1.55 months (95% CI, 0.78-3.42). Conclusions: THINKERS-Breast provides an internally validated framework for tumor-specific local failure prediction and dose-policy evaluation after Gamma Knife radiosurgery for breast cancer brain metastases. External validation is required before clinical deployment.