Chifaou Abdallah MD, MSc, John Thomas PhD, Olivier Aron MD, Tamir Avigdor MSc, Kassem Jaber MSc, Irena Doležalová MD, PhD, Daniel Mansilla MD, Päivi Nevalainen MD, PhD, Prachi Parikh MD, Jaysingh Singh MD, Sandor Beniczky MD, PhD, Philippe Kahane MD, PhD, Lorella Minotti MD, Stephan Chabardes MD, PhD, Sophie Colnat-Coulbois MD, PhD, Louis Maillard MD, PhD, Jeff Hall MD, PhD, Francois Dubeau MD, Jean Gotman PhD, Christophe Grova PhD, Birgit Frauscher MD, PD
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We aimed to identify and validate visually stereo-electroencephalography (SEEG) features with the highest predictive value for surgical outcome, and assess the reliability of their visual extraction.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We included 177 patients with drug-resistant epilepsy who underwent SEEG-guided surgery at 4 epilepsy centers. We assessed the predictive performance of 10 SEEG features from various SEEG periods for surgical outcome, using the area under the receiver operating characteristic curve, and considering resected channels and surgical outcome as the gold standard. Findings were validated externally using balanced accuracy. Six experts, blinded to outcome, evaluated the visual reliability of the optimal feature using interrater reliability, percentage agreement (standard deviation ± SD) and Gwet's kappa (κ ± SD).</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>The derivation cohort comprised 100 consecutive patients, each with at least 1-year of postoperative follow up (40% temporal lobe epilepsy; 42% Engel Ia). Spatial co-occurrence of gamma spikes and preictal spikes emerged as the optimal predictive feature of surgical outcome (area under the receiver operating characteristic curve 0.82). Applying the optimized threshold from the derivation cohort, external validation in 2 datasets showed similar performances (balanced accuracy 69.2% and 73.2%). Expert interrater reliability for gamma spikes (percentage agreement, 96% ± 2%; κ, 0.63 ± 0.16) and preictal spikes (percentage agreement, 92% ± 2%; κ, 0.65 ± 0.18) were substantial.</p>\n </section>\n \n <section>\n \n <h3> Interpretation</h3>\n \n <p>Spatial co-occurrence of gamma spikes and preictal spikes predicts surgical outcome. These visually identifiable features may reduce the burden of SEEG analysis by reducing analysis time, and improve outcome by guiding surgical resection margins. 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引用次数: 0
摘要
目的：癫痫手术需要易于在临床实践中实施的预测特征。以往的研究受到样本量小、缺乏外部验证和复杂计算方法的限制。我们的目的是识别和验证视觉立体脑电图（SEEG）特征对手术结果的最高预测价值，并评估其视觉提取的可靠性。方法：我们纳入了177例在4个癫痫中心接受seeg引导手术的耐药癫痫患者。我们评估了来自不同SEEG时期的10个SEEG特征对手术结果的预测性能，使用接受者工作特征曲线下的面积，并将切除的通道和手术结果作为金标准。研究结果在外部使用平衡准确性进行验证。对结果不知情的6位专家，使用互信度、一致性百分比（标准差±SD）和Gwet kappa （κ±SD）来评估最优特征的视觉可靠性。结果：衍生队列包括100例连续患者，每位患者术后随访至少1年(40%颞叶癫痫；42% Engel Ia)。伽玛峰和前侧峰的空间共现是预测手术结果的最佳特征（受者工作特征曲线下面积0.82）。应用衍生队列的优化阈值，在2个数据集上的外部验证显示出相似的性能（平衡准确率分别为69.2%和73.2%）。伽马峰值的专家互译可靠性(一致性百分比，96%±2%；κ, 0.63±0.16)和预测峰值(一致性百分比，92%±2%；κ为0.65±0.18)。解释：伽玛峰和前导峰的空间共存预测手术结果。这些视觉上可识别的特征可以通过减少分析时间来减轻SEEG分析的负担，并通过指导手术切除边缘来改善结果。Ann neurol 2025。
Visual Features in Stereo-Electroencephalography to Predict Surgical Outcome: A Multicenter Study
Objective
Epilepsy surgery needs predictive features that are easily implemented in clinical practice. Previous studies are limited by small sample sizes, lack of external validation, and complex computational approaches. We aimed to identify and validate visually stereo-electroencephalography (SEEG) features with the highest predictive value for surgical outcome, and assess the reliability of their visual extraction.
Methods
We included 177 patients with drug-resistant epilepsy who underwent SEEG-guided surgery at 4 epilepsy centers. We assessed the predictive performance of 10 SEEG features from various SEEG periods for surgical outcome, using the area under the receiver operating characteristic curve, and considering resected channels and surgical outcome as the gold standard. Findings were validated externally using balanced accuracy. Six experts, blinded to outcome, evaluated the visual reliability of the optimal feature using interrater reliability, percentage agreement (standard deviation ± SD) and Gwet's kappa (κ ± SD).
Results
The derivation cohort comprised 100 consecutive patients, each with at least 1-year of postoperative follow up (40% temporal lobe epilepsy; 42% Engel Ia). Spatial co-occurrence of gamma spikes and preictal spikes emerged as the optimal predictive feature of surgical outcome (area under the receiver operating characteristic curve 0.82). Applying the optimized threshold from the derivation cohort, external validation in 2 datasets showed similar performances (balanced accuracy 69.2% and 73.2%). Expert interrater reliability for gamma spikes (percentage agreement, 96% ± 2%; κ, 0.63 ± 0.16) and preictal spikes (percentage agreement, 92% ± 2%; κ, 0.65 ± 0.18) were substantial.
Interpretation
Spatial co-occurrence of gamma spikes and preictal spikes predicts surgical outcome. These visually identifiable features may reduce the burden of SEEG analysis by reducing analysis time, and improve outcome by guiding surgical resection margins. ANN NEUROL 2025;98:547–560
期刊介绍：
Annals of Neurology publishes original articles with potential for high impact in understanding the pathogenesis, clinical and laboratory features, diagnosis, treatment, outcomes and science underlying diseases of the human nervous system. Articles should ideally be of broad interest to the academic neurological community rather than solely to subspecialists in a particular field. Studies involving experimental model system, including those in cell and organ cultures and animals, of direct translational relevance to the understanding of neurological disease are also encouraged.
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