Clinical GBM hybrid artificial intelligence for prescription dose recommendation and outcome prediction after gamma knife radiosurgery treatment: a proof-of-concept

Background: Outcomes after Gamma Knife radiosurgery (GKRS) for recurrent glioblastoma (GBM) are heterogeneous, and prescription dose selection remains challenging in previously irradiated brain. A data-driven approach that individualizes dose while providing quantitative, patient-specific outcome predictions could improve counseling and follow-up planning. Methods: We performed a retrospective single-center study of recurrent GBM treated with GKRS at the University of Pittsburgh Medical Center (2014-2024) to develop CGH-AI (Clinical GBM Hybrid Artificial Intelligence). The patient-related clinical variables, biopsy-derived markers, and dosimetric parameters were extracted from the medical records. The primary endpoint was local tumor control, modeled as time to local failure. A lesion-level survival model was developed using a Random Survival Forest and internally validated using patient-level grouped cross-validation. Discrimination and calibration were assessed using Harrell's concordance index (C-index) and integrated Brier score (IBS). A connected dose recommendation engine evaluated clinically feasible candidate doses per case and selected the dose associated with the most favorable predicted local control profile. Results: The local control survival model achieved strong patient-level internal validation performance (C-index 0.80; IBS 0.14). Sensitivity analyses incorporating biopsy-derived markers yielded similar performance, supporting robustness of the clinical-tumor-dosimetric feature set. The integrated dose recommendation engine consistently identified prescription doses associated with improved predicted local control while providing interpretable, case-specific local control probabilities and expected local control duration. Conclusions: CGH-AI integrates survival modeling and dose decision support within the GKRS workflow to recommend individualized prescription dose and to predict local control for recurrent GBM, supporting personalized decision-making and surveillance planning.