Robot-assisted stereoencephalography vs subdural electrodes in the evaluation of temporal lobe epilepsy

机器人辅助立体脑电图与硬膜下电极在颞叶癫痫评估中的比较

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Abstract

OBJECTIVE: Invasive video-electroencephalography (iVEEG) is the gold standard for evaluation of refractory temporal lobe epilepsy before second stage resective surgery (SSRS). Traditionally, the presumed seizure onset zone (SOZ) has been covered with subdural electrodes (SDE), a very invasive procedure prone to complications. Temporal stereoelectroencephalography (SEEG) with conventional frame-based stereotaxy is time-consuming and impeded by the geometry of the frame. The introduction of robotic assistance promised a simplification of temporal SEEG implantation. However, the efficacy of temporal SEEG in iVEEG remains unclear. The aim of this study was therefore to describe the efficiency and efficacy of SEEG in iVEEG of temporal lobe epilepsy. METHODS: This retrospective study enrolled 60 consecutive patients with medically intractable epilepsy who underwent iVEEG of a potential temporal SOZ by SDE (n = 40) or SEEG (n = 20). Surgical time efficiency was analyzed by the skin-to-skin time (STS) and the total procedure time (TPT) and compared between groups (SDE vs SEEG). Surgical risk was depicted by the 90-day complication rate. Temporal SOZ were treated by SSRS. Favorable outcome (Engel°1) was assessed after 1 year of follow-up. RESULTS: Robot-assisted SEEG significantly reduced the duration of surgery (STS and TPT) compared to SDE implantations. There was no significant difference in complication rates. Notably, all surgical revisions in this study were attributed to SDE. Unilateral temporal SOZ was detected in 34/60 cases. Of the 34 patients, 30 underwent second stage SSRS. Both SDE and SEEG had a good predictive value for the outcome of temporal SSRS with no significant group difference. SIGNIFICANCE: Robot-assisted SEEG improves the accessibility of the temporal lobe for iVEEG by increasing surgical time efficiency and by simplifying trajectory selection without losing its predictive value for SSRS.

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