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TRUVACE RECORD VERSION
record: TRV-2026-0301
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-20T08:46:26.896885Z
status: published
lens: g_space
sector: health
headline: Machine learning prediction of local control after Gamma Knife radiosurgery to post-resection cavities from brain metastases: a proof-of-concept study
dek: 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…
gain_title: 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.
problem_title: (none)
trace_subject: (none)
gain_reading: 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.
gain_evidence: The gradient boosting model improved performance with ROC-AUC 0.735 and PR-AUC 0.802 with acceptable calibration (Brier 0.208) | A machine learning model using routine treatment-time variables can meaningfully stratify LC after GKRS to post-resection cavities
problem_reading: (none)
problem_evidence: (none)
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.
limitation: Retrospective single-institution design with 401 cavities limits generalizability and requires external validation before clinical use.
tag: Model-prefilled gain
key_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.
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.
sources:
- peer_reviewed | Journal of Neuro-Oncology | https://doi.org/10.1007/s11060-026-05703-3 | 2026-07-10
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