Tang Tianyou, Wu Yuxin, Dong Xinyu, Zhai Xuan
Department of Neurosurgery Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China.
Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders, Chongqing, China.
J Neurooncol. 2025 Sep;174(2):431-437. doi: 10.1007/s11060-025-05073-2. Epub 2025 May 22.
Determining whether pediatric patients with low-grade gliomas (pLGGs) have tumor-related epilepsy (GAE) is a crucial aspect of preoperative evaluation. Therefore, we aim to propose an innovative, machine learning- and deep learning-based framework for the rapid and non-invasive preoperative assessment of GAE in pediatric patients using magnetic resonance imaging (MRI).
In this study, we propose a novel radiomics-based approach that integrates tumor and peritumoral features extracted from preoperative multiparametric MRI scans to accurately and non-invasively predict the occurrence of tumor-related epilepsy in pediatric patients.
Our study developed a multimodal MRI radiomics model to predict epilepsy in pLGGs patients, achieving an AUC of 0.969. The integration of multi-sequence MRI data significantly improved predictive performance, with Stochastic Gradient Descent (SGD) classifier showing robust results (sensitivity: 0.882, specificity: 0.956).
Our model can accurately predict whether pLGGs patients have tumor-related epilepsy, which could guide surgical decision-making. Future studies should focus on similarly standardized preoperative evaluations in pediatric epilepsy centers to increase training data and enhance the generalizability of the model.
确定患有低级别胶质瘤（pLGGs）的儿科患者是否患有肿瘤相关性癫痫（GAE）是术前评估的关键环节。因此，我们旨在提出一种基于机器学习和深度学习的创新框架，用于使用磁共振成像（MRI）对儿科患者的GAE进行快速、非侵入性的术前评估。
在本研究中，我们提出了一种基于影像组学的新方法，该方法整合了从术前多参数MRI扫描中提取的肿瘤和瘤周特征，以准确、非侵入性地预测儿科患者肿瘤相关性癫痫的发生。
我们的研究开发了一种多模态MRI影像组学模型来预测pLGGs患者的癫痫，AUC达到0.969。多序列MRI数据的整合显著提高了预测性能，随机梯度下降（SGD）分类器显示出稳健的结果（敏感性：0.882，特异性：0.956）。
我们的模型可以准确预测pLGGs患者是否患有肿瘤相关性癫痫，这可以指导手术决策。未来的研究应集中在儿科癫痫中心进行类似标准化的术前评估，以增加训练数据并提高模型的通用性。
