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

dc.contributor.author

Abdallah, Chifaou

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Thomas, John

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Aron, Olivier

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Avigdor, Tamir

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Jaber, Kassem

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Doležalová, Irena

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Mansilla, Daniel

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Nevalainen, Päivi

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Parikh, Prachi

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Singh, Jaysingh

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Beniczky, Sandor

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Kahane, Philippe

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Minotti, Lorella

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Chabardes, Stephan

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Colnat-Coulbois, Sophie

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Maillard, Louis

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Hall, Jeff

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Dubeau, Francois

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Gotman, Jean

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Grova, Christophe

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Frauscher, Birgit

dc.date.accessioned

2025-07-23T14:10:40Z

dc.date.available

2025-07-23T14:10:40Z

dc.date.issued

2025-06

dc.description.abstract

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.
dc.identifier.issn

0364-5134

dc.identifier.issn

1531-8249

dc.identifier.uri

https://hdl.handle.net/10161/33038

dc.language

eng

dc.publisher

Wiley

dc.relation.ispartof

Annals of neurology

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10.1002/ana.27278

dc.rights.uri

https://creativecommons.org/licenses/by-nc/4.0

dc.title

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

dc.type

Journal article

pubs.organisational-group

Duke

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Pratt School of Engineering

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School of Medicine

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Clinical Science Departments

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Biomedical Engineering

pubs.organisational-group

Neurology

pubs.organisational-group

Neurology, Epilepsy and Sleep

pubs.publication-status

Published

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