Hu Xu, Yao Yuan, Zhao Baotian, Wang Xiu, Li Zilin, Hu Wenhan, Zhang Chao, Zhang Kai
Department of Neurosurgery, Beijing TianTan Hospital, Capital Medical University, Beijing, China.
Department of Neurosurgery, No. 904 Hospital of the PLA Joint Logistics Support Force, Wuxi, China.
CNS Neurosci Ther. 2025 Aug;31(8):e70563. doi: 10.1111/cns.70563.
Temporal lobe epilepsy (TLE), the most common type of drug-resistant epilepsy (DRE), has a postoperative seizure-free rate of ~70%. Furthermore, precisely localizing the epileptogenic zone and determining the surgical resection area have been established as the key factors influencing surgical outcomes. Herein, we innovatively coupled the surgical resection area with characteristics of effective connectivity via intracranial electroencephalography (iEEG) to predict patients' surgical prognosis.
This study involved 56 patients who underwent TLE surgery and were followed up for over 1 year. All patients underwent stereo-electroencephalography (SEEG) electrode implantation and single-pulse electrical stimulation (SPES) tests. After comparing patients' RMS value of N1/N2 (Z-score standardized) from cortico-cortical evoked potentials (CCEP) with different surgical outcomes, an interpretable machine learning (ML) model based on support vector machine (SVM) for predicting patients' surgical prognosis was constructed.
Patients with various surgical outcomes exhibited differences in effective connectivity. Furthermore, compared to the seizure-free group (Engel I), patients in the nonseizure-free group (Engel II-IV) exhibited stronger connectivity between the seizure onset zone (SOZ) and regions outside the surgical resection area. The nonseizure-free group also exhibited stronger connectivity between the surgical resection area and regions outside the resection area. Our prediction model demonstrated high-accuracy performance, with accuracy and area under the curve (AUC) values of 0.800 and 0.893, respectively.
This study confirmed the potential value of integrating the surgical resection area and effective connectivity characteristics in predicting patients' surgical outcomes; offering a novel approach that could be leveraged to precisely determine the surgical resection area and improve TLE patients' surgical prognosis.
颞叶癫痫（TLE）是最常见的耐药性癫痫（DRE）类型，术后无癫痫发作率约为70%。此外，精确确定致痫区并确定手术切除范围已被确立为影响手术效果的关键因素。在此，我们创新性地将手术切除范围与通过颅内脑电图（iEEG）获得的有效连接特征相结合，以预测患者的手术预后。
本研究纳入了56例行TLE手术并随访1年以上的患者。所有患者均接受了立体定向脑电图（SEEG）电极植入和单脉冲电刺激（SPES）测试。在将患者皮质-皮质诱发电位（CCEP）的N1/N2均方根值（Z分数标准化）与不同手术结果进行比较后，构建了基于支持向量机（SVM）的可解释机器学习（ML）模型来预测患者的手术预后。
不同手术结果的患者在有效连接方面存在差异。此外，与无癫痫发作组（Engel I级）相比，非无癫痫发作组（Engel II-IV级）患者的癫痫发作起始区（SOZ）与手术切除范围以外区域之间的连接更强。非无癫痫发作组在手术切除范围与切除范围以外区域之间也表现出更强的连接。我们的预测模型表现出高精度，准确率和曲线下面积（AUC）值分别为0.800和0.893。
本研究证实了整合手术切除范围和有效连接特征在预测患者手术结果方面的潜在价值；提供了一种可用于精确确定手术切除范围并改善TLE患者手术预后的新方法。
