aceMachine 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…
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.
Retrospective single-institution design with 401 cavities limits generalizability and requires external validation before clinical use.
Evidence
- Peer-reviewedJournal of Neuro-Oncology2026-07-10
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Truvace Impact Record TRV-2026-0301, v1: “Machine learning prediction of local control after Gamma Knife radiosurgery to post-resection cavities from brain metastases: a proof-of-concept study.” Truvace, 2026-07-20. /record/TRV-2026-0301 (accessed at citation time). sha256 2e3b6df6a673a271…
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