Integrating multisequence radiomics and clinical features to predict seizure recurrence after gross total resection of pediatric low-grade epilepsy-associated brain tumors.
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OBJECTIVE: This study aimed to develop a predictive model integrating clinical features and multisequence MRI radiomics to forecast postoperative seizure outcomes in pediatric patients with low-grade epilepsy-associated tumors (LEATs) who underwent gross total resection (GTR). METHODS: In this study, we propose a novel radiomics-based approach to predict seizure recurrence. The model was further optimized by integrating clinical features, and its performance was compared with traditional radiomics models and deep learning-derived radiomics models. RESULTS: For traditional radiomics models, multi-sequence combination (Combined) outperformed single sequences, with XGBOOST achieving the highest AUC (0.889) and accuracy (0.816). Integrating preoperative epilepsy duration significantly improved model efficacy. CONCLUSION: The combined model of multimodal MRI radiomics and clinical features demonstrates potential for predicting postoperative seizure outcomes in pediatric LEAT patients after GTR.