Leveraging machine learning for preoperative prediction of supramaximal ablation in laser interstitial thermal therapy for brain tumors.
Journal:
Neurosurgical focus
PMID:
39486059
Abstract
OBJECTIVE: Maximizing safe resection in neuro-oncology has become paramount to improving patient survival and outcomes. Laser interstitial thermal therapy (LITT) offers similar survival benefits to traditional resection, alongside shorter hospital stays and faster recovery times. The extent of ablation (EOA) achieved using LITT is linked to patient outcomes, with greater EOA correlating with improved outcomes. However, the preoperative predictors for achieving supramaximal ablation (EOA ≥ 100%) are not well understood. By leveraging machine learning (ML) techniques, this study aimed to identify these predictors to enhance patient selection and therefore outcomes. The objective was to explore preoperative predictors for supramaximal EOA using ML in patients with glioblastoma.