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.

Brno Epilepsy Center 1st Department of Neurology St Anne's University Hospital Faculty of Medicine Masaryk University Brno Czech Republic

CHU Grenoble Alpes Univ Grenoble Alpes Inserm U1216 Grenoble Institut Neurosciences Grenoble France

Department of Biomedical Engineering Duke Pratt School of Engineering Durham North Carolina USA

Department of Clinical Neurophysiology Aarhus University Hospital Aarhus Denmark

Department of Clinical Neurophysiology Danish Epilepsy Center Dianalund Denmark

Department of Neurology Duke University Medical Center Durham North Carolina USA

Department of Neurology The Ohio State University Wexner Medical Center Columbus Ohio USA

Department of Neurology University Hospital of Nancy Lorraine University Nancy France

Epilepsia Helsinki Full member of ERN EpiCare Department of Clinical Neurophysiology HUS Diagnostic Center University of Helsinki and Helsinki University Hospital Helsinki Finland

Montreal Neurological Institute and Hospital McGill University Montréal Québec Canada

Multimodal Functional Imaging Lab Biomedical Engineering Department McGill University Montréal Québec Canada

Multimodal Functional Imaging Lab Department of Physics Concordia School of Health Concordia University Montréal Québec Canada

Neurophysiology Unit Institute of Neurosurgery Dr Asenjo Santiago Chile

Research Center for Automatic Control of Nancy Lorraine University CNRS UMR Nancy France

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