Machine learning-based models for predicting glioma-associated epilepsy: a systematic review and meta-analysis

BACKGROUND: Glioma-associated epilepsy (GAE) is a common and disabling complication in glioma patients. Predicting seizures in this population is challenging due to complex tumor-host interactions. With recent advancements in machine learning (ML) models, these models can incorporate high-dimensional datasets and detect subtle patterns. This systematic review and meta-analysis aimed to evaluate the predictive performance of ML-based models for predicting GAE. METHODS: A comprehensive review was performed following PRISMA guidelines in four databases (PubMed, Embase, Scopus, and Web of Science) on May 23, 2025. Studies developing ML-based models for GAE prediction were included. Pooled estimates for area under the curve (AUC), accuracy (ACC), sensitivity (SEN), specificity (SPE), and diagnostic odds ratio (DOR) were calculated. RESULTS: Thirteen studies with 3,253 patients were included. Pooled AUC was 0.87 (95% CI: 0.83-0.91), and ACC was 0.82 (95% CI: 0.76-0.88). The pooled SEN was 0.77 (95% CI: 0.64-0.87), SPE was 0.93 (95% CI: 0.86-0.96), and DOR was 40.1 (95% CI: 17.1-94.0). The Summary Receiver Operating Characteristic (SROC) curve demonstrated a false positive rate of 0.09. CONCLUSION: ML-based models demonstrate encouraging diagnostic performance in predicting GAE. Incorporating these models into daily clinical practice can help physicians with risk stratification and the identification of high-risk individuals, thereby optimizing therapeutic strategies and enhancing patient outcomes. Before implementing these models in real-time clinical practice, several limitations, including a lack of standardized protocols, considerable heterogeneity among models, and a lack of external validation, should be addressed.