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Visual Features in Stereo-Electroencephalography to Predict Surgical Outcome: A Multicenter Study
C. Abdallah, J. Thomas, O. Aron, T. Avigdor, K. Jaber, I. Doležalová, D. Mansilla, P. Nevalainen, P. Parikh, J. Singh, S. Beniczky, P. Kahane, L. Minotti, S. Chabardes, S. Colnat-Coulbois, L. Maillard, J. Hall, F. Dubeau, J. Gotman, C. Grova, B. Frauscher
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, multicentrická studie
Grantová podpora
Doctoral Award
Fonds de Recherche du Québec - Santé
Salary Award
Fonds de Recherche du Québec - Santé
CIHR, PJT-175056
CIHR - Canada
Doctoral Award
CIHR - Canada
//doi.org/10.69777/320890
CIHR - Canada
Doctoral Award
Savoy Foundation
CIHR, PJT-175056
CIHR - Canada
Doctoral Award
CIHR - Canada
//doi.org/10.69777/320890
CIHR - Canada
PubMed
40519108
DOI
10.1002/ana.27278
Knihovny.cz E-zdroje
- MeSH
- dospělí MeSH
- elektroencefalografie * metody MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- prediktivní hodnota testů MeSH
- refrakterní epilepsie * chirurgie patofyziologie MeSH
- reprodukovatelnost výsledků MeSH
- stereotaktické techniky MeSH
- výsledek terapie MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladiství MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- multicentrická studie MeSH
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.
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
Montreal Neurological Institute and Hospital McGill 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
Citace poskytuje Crossref.org
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