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Health·G Space·Model-prefilled gain·Published 2026-07-20

Machine learning prediction of local control after Gamma Knife radiosurgery to post-resection cavities from brain metastases: a proof-of-concept study

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 40…

TRV-2026-0301Peer-reviewedPermanent record — cite & verify
Machine learning prediction of local control after Gamma Knife radiosurgery to post-resection cavities from brain metastases: a proof-of-concept study

"Brain - CT scan - Metastatic pulmonary adenocarcinoma Case 239 (7603361920)" by Yale Rosen from USA is licensed under CC BY-SA 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by-sa/2.0/.

The quick read

Researchers retrospectively evaluated 401 post-resection cavities from brain metastases treated with Gamma Knife radiosurgery at a single institution from 2014 to 2024. They trained a gradient boosting classifier on eight routine treatment-time features to predict local control, estimating performance with five-fold stratified cross-validation.

By the July 2026 publication date, the model had shown improved discrimination over chance with ROC-AUC 0.735 and PR-AUC 0.802 and acceptable calibration, suggesting potential to inform surveillance. What remains uncertain is whether performance holds outside this single-center retrospective cohort, as authors noted need for external validation and prospective evaluation.

Main points
  • Retrospective cohort of 401 post-resection cavities treated with GKRS at a single institution from 2014-2024 with local control as primary endpoint.
  • Model used eight routine features: age, sex, pre-treatment Karnofsky Performance Status, primary tumor category, single vs multiple metastases, lobe/structure, eloquence, and cavity volume.
  • Performance estimated with five-fold stratified cross-validation with out-of-fold predictions; prevalence baseline ROC-AUC 0.494 versus gradient boosting ROC-AUC 0.735.
  • At prespecified threshold 0.50, gradient boosting accuracy 0.701 with sensitivity 0.783 and specificity 0.566; feedforward neural network performed worse at ROC-AUC 0.672.
Gain

A gradient boosting classifier trained on eight routine treatment-time features improved prediction of local control after Gamma Knife radiosurgery to post-resection cavities from brain metastases, achieving ROC-AUC 0.735 compared to chance-level baseline.

The rundown

The study analyzed 401 post-resection cavities treated with Gamma Knife radiosurgery between 2014 and 2024 at one center, using five-fold stratified cross-validation and out-of-fold predictions to estimate performance against a prevalence-only baseline.

Eight routinely available treatment-time variables were used as inputs, and the gradient boosting model outperformed both the baseline (ROC-AUC 0.494) and a feedforward neural network (ROC-AUC 0.672, PR-AUC 0.768, Brier 0.219) with reported operating characteristics at threshold 0.50.

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