Planning stereoelectroencephalography using automated lesion detection: Retrospective feasibility study.

Epilepsia
Konrad WagstylMartin Tisdall

Abstract

This retrospective, cross-sectional study evaluated the feasibility and potential benefits of incorporating deep-learning on structural magnetic resonance imaging (MRI) into planning stereoelectroencephalography (sEEG) implantation in pediatric patients with diagnostically complex drug-resistant epilepsy. This study aimed to assess the degree of colocalization between automated lesion detection and the seizure onset zone (SOZ) as assessed by sEEG. A neural network classifier was applied to cortical features from MRI data from three cohorts. (1) The network was trained and cross-validated using 34 patients with visible focal cortical dysplasias (FCDs). (2) Specificity was assessed in 20 pediatric healthy controls. (3) Feasibility of incorporation into sEEG implantation plans was evaluated in 34 sEEG patients. Coordinates of sEEG contacts were coregistered with classifier-predicted lesions. sEEG contacts in seizure onset and irritative tissue were identified by clinical neurophysiologists. A distance of <10 mm between SOZ contacts and classifier-predicted lesions was considered colocalization. In patients with radiologically defined lesions, classifier sensitivity was 74% (25/34 lesions detected). No clusters were detected in the...Continue Reading

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Citations

Nov 26, 2020·Epilepsia·Sara LarivièreBoris C Bernhardt
Jun 16, 2021·Seizure : the Journal of the British Epilepsy Association·Iván Sánchez FernándezJurriaan M Peters
Jul 10, 2021·Frontiers in Neuroscience·Daichi Sone, Iman Beheshti

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