Visual Features in Stereo-Electroencephalography to Predict Surgical Outcome: A Multicenter Study.

Area of Science:

• Neurology
• Neurosurgery
• Epileptology

Background:

• Epilepsy surgery requires easily applicable predictive markers.
• Previous research is limited by small sample sizes, lack of external validation, and complex computational methods.
• Stereo-EEG (SEEG) is a crucial tool for localizing seizure onset zones.

Purpose of the Study:

• To identify and validate visually detectable SEEG features for predicting epilepsy surgery outcomes.
• To assess the reliability of visual extraction of these predictive SEEG features.
• To develop a more practical approach for SEEG analysis in clinical settings.

Main Methods:

• 177 patients with drug-resistant epilepsy undergoing SEEG-guided surgery were analyzed.
• 10 SEEG features were evaluated for predictive performance using receiver operating characteristic curves.
• External validation and interrater reliability assessments were performed on visual feature extraction.

Main Results:

• The spatial co-occurrence of gamma spikes and preictal spikes was the optimal predictive feature (AUC 0.82).
• External validation demonstrated consistent performance (balanced accuracy 69.2% and 73.2%).
• Substantial interrater reliability was achieved for visual identification of gamma and preictal spikes (kappa > 0.63).

Conclusions:

• Spatial co-occurrence of gamma and preictal spikes is a reliable predictor of epilepsy surgery success.
• These visually identifiable SEEG features can streamline analysis and improve surgical guidance.
• This approach offers a practical method to enhance surgical decision-making and patient outcomes in epilepsy.