acePre-treatment machine learning prediction of local failure and time to local failure after Gamma Knife radiosurgery for radioresistant brain metastases
Background Local control after Gamma Knife radiosurgery (GKRS) for radioresistant brain metastases remains variable. Melanoma and renal cell carcinoma brain metastases are relatively radioresistant, yet pre-treatment prediction of local failure and its timing remains limited. We developed and internally validated a survival machine learning model to predict lesion-specific local failure and time to local failure using only pre-GKRS variables. Methods We performed a retrospective single-center study of melanoma a…
In a single-center study of 893 brain metastases, a random survival forest using only pre-treatment data predicted local failure after Gamma Knife radiosurgery with a C-index of 0.842 on a 179-lesion holdout set.
The model was developed and validated internally in a retrospective study at a single center, so its performance on other patients and hospitals is untested.
Sources
- Peer-reviewedJournal of Neuro-Oncology2026-11-02
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Truvace Impact Record TRV-2026-2430, v1: “Pre-treatment machine learning prediction of local failure and time to local failure after Gamma Knife radiosurgery for radioresistant brain metastases.” Truvace, 2026-11-08. /record/TRV-2026-2430 (accessed on the day you copy it). sha256 17a31cc1caeb213f…
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