Personalized prediction of local control after stereotactic radiosurgery for craniopharyngioma: a multicenter machine learning survival model
Journal of Neuro-Oncology, cilt.179, sa.1, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 179 Sayı: 1
- Basım Tarihi: 2026
- Doi Numarası: 10.1007/s11060-026-05736-8
- Dergi Adı: Journal of Neuro-Oncology
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, EMBASE, MEDLINE, Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest), Pharma Collection (ProQuest)
- Anahtar Kelimeler: Craniopharyngioma, Local control, Machine learning, Stereotactic radiosurgery, Survival prediction
- İstanbul Medipol Üniversitesi Adresli: Evet
Özet
Background: Stereotactic radiosurgery (SRS) is used in selected patients with craniopharyngioma, yet counseling and follow-up planning often rely on population-level local control rates rather than individualized expectations over time. Objective: To develop and internally validate a multicenter survival model to predict imaging-defined time to progression after SRS for craniopharyngioma. Methods: We analyzed a multicenter IRRF registry of SRS-treated craniopharyngioma patients. Imaging progression was defined by the overall last imaging response (PD vs. non-PD), with censoring at last imaging follow-up when progression was not observed. A Random Survival Forest (RSF) model was evaluated using 5-fold out-of-fold cross-validation. Performance was assessed using the concordance index, time-dependent AUC at 12, 24, and 60 months with bootstrap 95% confidence intervals, integrated Brier score (IBS) over 0–60 months, and risk-stratified calibration. Benchmarks included a penalized Cox model and a Kaplan–Meier baseline. Results: Among 277 patients (event rate 13.0%; median imaging follow-up 57.0 months by reverse Kaplan–Meier), RSF achieved an out-of-fold C-index of 0.905. Time-dependent AUC was 0.895 (95% CI 0.828–0.959) at 12 months, 0.897 (95% CI 0.833–0.952) at 24 months, and 0.934 (95% CI 0.889–0.969) at 60 months. IBS (0–60 months) was 0.050 with favorable calibration. Conclusions: A multicenter machine learning survival model can provide individualized, well-calibrated estimates of local control over time after SRS for craniopharyngioma to support non-prescriptive decision support. Clinical trial number: Not applicable.