Journal of neuro-oncology · 2026
Machine learning prediction of local control after Gamma Knife radiosurgery to post-resection cavities from brain metastases: a proof-of-concept study.
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Reyes Jheremy S, Bouras Alexandros, Lunsford L Dade, Niranjan Ajay, Hadjipanayis Constantinos G
Аннотация
BACKGROUND: Large symptomatic brain metastases require initial surgical resection. However, local control (LC) after Gamma Knife radiosurgery (GKRS) to resection cavities remains variable. Quantitative risk stratification using routinely available treatment-time variables could inform surveillance and multidisciplinary decision-making.
METHODS: We performed a retrospective study of post-resection cavities treated with GKRS at a single institution (2014-2024). The primary endpoint was LC. The cohort comprised of 401 post-resection cavities. A gradient boosting classifier was trained using eight routine treatment-time features: age, sex, pre-treatment Karnofsky Performance Status, primary tumor category, single vs. multiple metastases, lobe/structure, eloquence, and cavity volume. Performance was estimated using five-fold stratified cross-validation with out-of-fold predictions and compared with a prevalence-only baseline. Discrimination and calibration were assessed using the receiver operating characteristic area under the curve (ROC-AUC), and Brier score; operating characteristics were reported at a prespecified probability threshold of 0.50.
RESULTS: We compared the performance of two different AI models for predicting LC. The prevalence baseline demonstrated chance-level discrimination (ROC-AUC 0.494). The gradient boosting model improved performance with ROC-AUC 0.735 and PR-AUC 0.802 with acceptable calibration (Brier 0.208). At threshold 0.50, accuracy was 0.701 with sensitivity 0.783 and specificity 0.566. A feedforward neural network trained on the same features performed worse (ROC-AUC 0.672; PR-AUC 0.768; Brier 0.219).
CONCLUSIONS: A machine learning model using routine treatment-time variables can meaningfully stratify LC after GKRS to post-resection cavities. The gradient boosting model showed the best performance supporting further external validation and prospective evaluation.
CLINICAL TRIAL NUMBER: Not applicable.
METHODS: We performed a retrospective study of post-resection cavities treated with GKRS at a single institution (2014-2024). The primary endpoint was LC. The cohort comprised of 401 post-resection cavities. A gradient boosting classifier was trained using eight routine treatment-time features: age, sex, pre-treatment Karnofsky Performance Status, primary tumor category, single vs. multiple metastases, lobe/structure, eloquence, and cavity volume. Performance was estimated using five-fold stratified cross-validation with out-of-fold predictions and compared with a prevalence-only baseline. Discrimination and calibration were assessed using the receiver operating characteristic area under the curve (ROC-AUC), and Brier score; operating characteristics were reported at a prespecified probability threshold of 0.50.
RESULTS: We compared the performance of two different AI models for predicting LC. The prevalence baseline demonstrated chance-level discrimination (ROC-AUC 0.494). The gradient boosting model improved performance with ROC-AUC 0.735 and PR-AUC 0.802 with acceptable calibration (Brier 0.208). At threshold 0.50, accuracy was 0.701 with sensitivity 0.783 and specificity 0.566. A feedforward neural network trained on the same features performed worse (ROC-AUC 0.672; PR-AUC 0.768; Brier 0.219).
CONCLUSIONS: A machine learning model using routine treatment-time variables can meaningfully stratify LC after GKRS to post-resection cavities. The gradient boosting model showed the best performance supporting further external validation and prospective evaluation.
CLINICAL TRIAL NUMBER: Not applicable.