ArticleFrontiers in oncology2026
Clinical GBM hybrid artificial intelligence for prescription dose recommendation and outcome prediction after gamma knife radiosurgery treatment: a proof-of-concept.
Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI for prognosis and treatment stratification in glioblastoma neurosurgery: a systematic review.Journal of neuro-oncology · 2026Pooled it
- Predicting time to local failure after gamma knife radiosurgery for melanoma brain metastases using survival machine learning.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Article
- Therapeutic hybrid intelligence with neural and knowledge-based expert reasoning for SRS: an AI model for breast cancer brain metastases.Frontiers in oncology · 2026Article
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5 authors.
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Abstract
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.
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