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

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.

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.

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.

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.