Seizure risk prediction using machine learning following glioma resection surgery in seizure-naïve patients

IF 1.8 4区医学 Q3 CLINICAL NEUROLOGY

Journal of Clinical Neuroscience Pub Date : 2026-04-01 Epub Date: 2026-01-28 DOI:10.1016/j.jocn.2026.111869

Hua Yang , Hao Wen , Jiadan Ye , Li Yang , Zhigang Zhao

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引用次数: 0

Abstract

Background:

Despite the ongoing controversy around the prophylactic use of antiseizure medications (ASMs) in seizure-naïve patients undergoing brain tumor surgery, this practice has persisted for years. This study aims to develop and validate a machine-learning framework for stratifying postoperative seizure risk.

Methods:

Demographic, tumor topographic, surgery-related details, and biomarkers were collected from a retrospective study involving patients undergoing glioma resection. The dataset was split in a stratified manner into an 80/20 ratio for training and testing purposes. Machine learning (ML) models, including random forest (RF), XGBoost, gradient boosting decision tree (GBDT), multi-layer perceptron (MLP), bootstrap-aggregation ensemble classifier with decision tree classifier (Bagging), and logistic regression (LR), were developed and evaluated. The SHAP method was applied to interpret the attribution values of the top features.

Results:

Among the 786 eligible patients, with a median age of 42.0 years (interquartile range [IQR] = 25.3-54.0), 154 (19.6%) experienced postoperative seizures. The multi-layer perceptron model demonstrated the best predictive performance, incorporating demographic, topographic, surgery-related, and biomarker variables (Test: AUC: 0.74, Accuracy: 0.70, Sensitivity: 0.56, Specificity: 0.73). Notably, a simpler model relying solely on demographic and topographic features also yielded comparable performance.

Conclusions:

This study underscores the effectiveness of the multi-layer perceptron model, which integrates demographic, topographic, surgery-related, and biomarker variables. This integration successfully developed a personalized prediction model for postoperative seizure risk. Such a model holds the potential to aid physicians in optimizing postoperative management, particularly in guiding decisions regarding the duration and discontinuation of prophylactic antiseizure medications.

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查看原文本刊更多论文

神经胶质瘤切除手术后机器学习癫痫发作风险预测seizure-naïve患者

背景：尽管对seizure-naïve脑肿瘤手术患者预防性使用抗癫痫药物（asm）存在争议，但这种做法已持续多年。本研究旨在开发和验证一种用于分层术后癫痫发作风险的机器学习框架。方法：从一项涉及胶质瘤切除术患者的回顾性研究中收集人口统计学、肿瘤地形图、手术相关细节和生物标志物。为了训练和测试的目的，数据集以分层的方式分成80/20的比例。开发并评估了机器学习（ML）模型，包括随机森林（RF）、XGBoost、梯度增强决策树（GBDT）、多层感知器（MLP）、带决策树分类器的自举聚合集成分类器（Bagging）和逻辑回归（LR）。采用SHAP方法对top feature的属性值进行解释。结果：786例符合条件的患者中位年龄为42.0岁（四分位数间距[IQR] = 25.3-54.0）， 154例（19.6%）出现术后癫痫发作。多层感知器模型结合了人口统计学、地形、手术相关和生物标志物变量，显示出最佳的预测性能（检验：AUC: 0.74，准确性：0.70，灵敏度：0.56，特异性：0.73）。值得注意的是，一个简单的仅依赖于人口和地形特征的模型也产生了类似的性能。结论：本研究强调了多层感知器模型的有效性，该模型集成了人口统计、地形、手术相关和生物标志物变量。这种整合成功地开发了一种个性化的术后癫痫发作风险预测模型。这种模型具有帮助医生优化术后管理的潜力，特别是在指导有关预防性抗癫痫药物持续时间和停药的决策方面。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Clinical Neuroscience 医学-临床神经学

CiteScore

4.50

自引率

0.00%

发文量

402

审稿时长

40 days

期刊介绍： This International journal, Journal of Clinical Neuroscience, publishes articles on clinical neurosurgery and neurology and the related neurosciences such as neuro-pathology, neuro-radiology, neuro-ophthalmology and neuro-physiology. The journal has a broad International perspective, and emphasises the advances occurring in Asia, the Pacific Rim region, Europe and North America. The Journal acts as a focus for publication of major clinical and laboratory research, as well as publishing solicited manuscripts on specific subjects from experts, case reports and other information of interest to clinicians working in the clinical neurosciences.