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Objective <p>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).</p> Methods <p>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.</p> Results <p>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.</p> Conclusion <p>The combined model of multimodal MRI radiomics and clinical features demonstrates potential for predicting postoperative seizure outcomes in pediatric LEAT patients after GTR.</p>

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Integrating multisequence radiomics and clinical features to predict seizure recurrence after gross total resection of pediatric low-grade epilepsy-associated brain tumors

  • 期刊论文

  • 发表日期：

    2026年6月3日

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  • Tianyou Tang,
  • Corlina Juliette Princess Matthew,
  • Yuxin Wu,
  • Hanli Qiu,
  • Xinyu Dong,
  • Yasi Yang,
  • Zi Yang,
  • Shihang Chen,
  • Wu Yan,
  • Xuan Zhai

摘要

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.