Zhong Zian, Yu Hong-Fei, Tong Yanfei, Li Jie
Department of Neurology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, Hubei, People's Republic of China.
Int J Gen Med. 2025 Feb 26;18:1111-1125. doi: 10.2147/IJGM.S512814. eCollection 2025.
Glioma-associated epilepsy (GAE) is a common neurological symptom in glioma patients, which can worsen the condition and increase the risk of death on the basis of primary injury. Given this, accurate prediction of GAE is crucial, and this study aims to develop and validate a GAE warning recognition prediction model.
We retrospectively collected MRI scan imaging data and urine samples from 566 glioma patients at the Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science from August 2016 to December 2023. Least Absolute Shrinkage and Selection Operator (LASSO) regression and multivariate logistic regression analysis are used to determine independent risk factors for GAE. The nomogram and decision tree GAE visualization prediction model were constructed based on independent risk factors. The discrimination, calibration, and clinical usefulness of GAE prediction models were evaluated through receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), respectively.
In the training and validation datasets, the incidence of GAE was 34.50% and 33.00%, respectively. Nomogram and decision tree were composed of five independent radiomic predictors and three differential protein molecules derived from urine. The discrimination rate of area under the curve (AUC) was 0.897 (95% CI: 0.840-0.954), slightly decreased in the validation data set, reaching 0.874 (95% CI: 8.817-0.931). The calibration curve showed a high degree of consistency between the predicted GAE probability and the actual probability. In addition, DCA analysis showed that in machine learning prediction models, decision trees have higher overall net returns within the threshold probability range.
We have introduced a machine learning prediction model for GAE detection in glioma patients based on multiomics data. This model can improve the prognosis of GAE by providing early warnings and actionable feedback and prevent or reduce pathological damage and neurobiochemical changes by implementing early interventions.
胶质瘤相关性癫痫（GAE）是胶质瘤患者常见的神经症状，在原发性损伤的基础上可使病情恶化并增加死亡风险。鉴于此，准确预测GAE至关重要，本研究旨在开发并验证一种GAE预警识别预测模型。
我们回顾性收集了2016年8月至2023年12月在湖北文理学院附属襄阳中心医院的566例胶质瘤患者的MRI扫描影像数据和尿液样本。采用最小绝对收缩和选择算子（LASSO）回归及多变量逻辑回归分析来确定GAE的独立危险因素。基于独立危险因素构建列线图和决策树GAE可视化预测模型。分别通过受试者操作特征（ROC）曲线、校准曲线和决策曲线分析（DCA）评估GAE预测模型的辨别力、校准度和临床实用性。
在训练集和验证集中，GAE的发生率分别为34.50%和33.00%。列线图和决策树由五个独立的影像组学预测因子和三个源自尿液的差异蛋白分子组成。曲线下面积（AUC）的辨别率为0.897（95%CI：0.840 - 0.954），在验证数据集中略有下降，达到0.874（95%CI：8.817 - 0.931）。校准曲线显示预测的GAE概率与实际概率之间具有高度一致性。此外，DCA分析表明，在机器学习预测模型中，决策树在阈值概率范围内具有更高的总体净收益。
我们基于多组学数据引入了一种用于胶质瘤患者GAE检测的机器学习预测模型。该模型可通过提供早期预警和可操作的反馈来改善GAE的预后，并通过实施早期干预预防或减少病理损伤和神经生化变化。
