ArticleNeuroradiology2026
Integrating multisequence radiomics and clinical features to predict seizure recurrence after gross total resection of pediatric low-grade epilepsy-associated brain tumors.
Article in Neuroradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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10 authors.
Funding
Abstract
objectiveThis 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).
methodsIn 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.
resultsFor 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.
conclusionThe combined model of multimodal MRI radiomics and clinical features demonstrates potential for predicting postoperative seizure outcomes in pediatric LEAT patients after GTR.
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