Integrating multisequence radiomics and clinical features to predict seizure recurrence after gross total resection of pediatric low-grade epilepsy-associated brain tumors.
Authors
Affiliations (5)
Affiliations (5)
- Chongqing Medical University, 61, University Road, Shapingba District, Chongqing, China. [email protected].
- Chongqing Medical University, 61, University Road, Shapingba District, Chongqing, China.
- Jiangxi Provincial Children's Hospital, Nanchang, China.
- Ulumuqi Children's Hospital, Ürümqi, China. [email protected].
- Chongqing Medical University, 61, University Road, Shapingba District, Chongqing, China. [email protected].
Abstract
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). 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. 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. The combined model of multimodal MRI radiomics and clinical features demonstrates potential for predicting postoperative seizure outcomes in pediatric LEAT patients after GTR.