Lesion detection in epilepsy surgery: Lessons from a prospective evaluation of a machine learning algorithm.

AIM: To evaluate a lesion detection algorithm designed to detect focal cortical dysplasia (FCD) in children undergoing stereoelectroencephalography (SEEG) as part of their presurgical evaluation for drug-resistant epilepsy. METHOD: This was a prospective, single-arm, interventional study (Idea, Development, Exploration, Assessment, and Long-Term Follow-Up phase 1/2a). After routine SEEG planning, structural magnetic resonance imaging sequences were run through an FCD lesion detection algorithm to identify putative clusters. If the top three clusters were not already sampled, up to three additional SEEG electrodes were added. The primary outcome measure was the proportion of patients who had additional electrode contacts in the SEEG-defined seizure-onset zone (SOZ). RESULTS: Twenty patients (median age 12 years, range 4-18 years) were enrolled, one of whom did not undergo SEEG. Additional electrode contacts were part of the SOZ in 1 out of 19 patients while 3 out of 19 patients had clusters that were part of the SOZ but they were already implanted. A total of 16 additional electrodes were implanted in nine patients and there were no adverse events from the additional electrodes. INTERPRETATION: We demonstrate early-stage prospective clinical validation of a machine learning lesion detection algorithm used to aid the identification of the SOZ in children undergoing SEEG. We share key lessons learnt from this evaluation and emphasize the importance of robust prospective evaluation before routine clinical adoption of such algorithms. WHAT THIS PAPER ADDS: The focal cortical dysplasia detection algorithm collocated with the seizure-onset zone (SOZ) in 4 out of 19 patients. The algorithm changed the resection boundaries in 1 of 19 patients undergoing stereoelectroencephalography for drug-resistant epilepsy. The patient with an altered resection due to the algorithm was seizure-free 1 year after resective surgery. Overall, the algorithm did not increase the proportion of patients in whom SOZ was identified.

Electroencephalographic characteristics of epileptic seizures in preterm neonates.

OBJECTIVE: Although seizures are more common in the neonatal period than in any other stage of childhood, those in preterm neonates are still poorly described. The aim of this study was to assess electro-clinical characteristics of seizures occurring before a corrected age of 40weeks in neonates born prematurely. METHOD: Retrospective analysis of EEG-documented seizures in neonates born prematurely. Seizures in a group of term neonates served as controls. RESULTS: Fifty-six prematurely born and 46 term born neonates were included. Median duration of seizures was 52s in preterm and 96s in term neonates. Seizures were focal or multifocal. In least mature neonates, they involved smaller regions of onset and remained localised. With increasing corrected age, propagation became more frequent. The electrographic pattern - maximal frequency of oscillation and the onset pattern also evolved with age. Electro-clinical seizures were observed in 25% of preterm versus 50% of term neonates; almost all electro-clinical seizures involved the central (motor) regions. CONCLUSION: Ictal EEG features undergo changes depending on corrected age. Most seizures are subclinical, thus EEG is essential for diagnosis. SIGNIFICANCE: Relating ictal EEG pattern to corrected age can improve diagnosis and ultimately management.