Dual AI models for gamma knife radiosurgery in craniopharyngioma: prescription dose modeling and outcome risk prediction.

Authors

Jheremy S Reyes, Alexandros Bouras, Constantinos G Hadjipanayis, L Dade Lunsford, Ajay Niranjan

Published in

Journal of neuro-oncology. Volume 178. Issue 3. Jul 15, 2026. Epub Jul 15, 2026.

Abstract

Gamma Knife radiosurgery (GKRS) is an established adjunct for residual or recurrent craniopharyngioma, yet prescription dose selection remains experience-driven and long-term failure risk is difficult to individualize. Machine learning may support more consistent planning and risk-informed follow-up.
To develop and internally validate two complementary AI models for craniopharyngioma GKRS: (1) prescription dose prediction and (2) treated-lesion progression risk prediction.
In this retrospective single-center cohort, we trained a random forest regressor to predict delivered single-fraction margin dose (Gy) from baseline clinical and tumor features and a random forest classifier to estimate the probability of treated-lesion progression using baseline features and delivered dose. Internal validation used cross-validation with discrimination and calibration metrics.
Seventy-two treated tumors were analyzed. Prescription dose prediction showed clinically tight error, with MAE 1.30 Gy and RMSE 1.65 Gy (R² 0.21), indicating the model approximated physician dosing patterns within ~1-2 Gy for most cases. For outcome modeling, risk prediction achieved ROC-AUC 0.75 and PR-AUC 0.582, with reasonable calibration (Brier score 0.112; recalibration slope 0.86, intercept 0.096). Together, the two models enabled simultaneous estimation of an expected prescription dose and an individualized probability of long-term treated-lesion failure, supporting risk stratification beyond dose alone.
A dual-model AI framework for craniopharyngioma GKRS is feasible and provides both dose estimates and individualized long-horizon failure risk predictions, with potential to standardize prescriptions and tailor surveillance intensity.
Not applicable.

PMID:
42455391
Bibliographic data and abstract were imported from PubMed on 15 Jul 2026.

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