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

Autor:	Chari A; Department of Neurosurgery, Great Ormond Street Hospital, London, UK.; Developmental Neuroscience, Institute of Child Health, University College London, London, UK., Adler S; Developmental Neuroscience, Institute of Child Health, University College London, London, UK., Wagstyl K; Developmental Neuroscience, Institute of Child Health, University College London, London, UK.; Wellcome Centre for Human Neuroimaging, University College London, London, UK., Seunarine K; Developmental Neuroscience, Institute of Child Health, University College London, London, UK., Tahir MZ; Department of Neurosurgery, Great Ormond Street Hospital, London, UK., Moeller F; Department of Neurophysiology, Great Ormond Street Hospital, London, UK., Thornton R; Department of Neurophysiology, Addenbrooke's Hospital, Cambridge, UK., Boyd S; Department of Neurophysiology, Great Ormond Street Hospital, London, UK., Das K; Department of Neurophysiology, Great Ormond Street Hospital, London, UK.; Department of Neurology, Great Ormond Street Hospital, London, UK., Cooray G; Department of Neurophysiology, Great Ormond Street Hospital, London, UK., Smith S; Department of Neurophysiology, Great Ormond Street Hospital, London, UK., D'Arco F; Department of Neuroradiology, Great Ormond Street Hospital, London, UK., Baldeweg T; Developmental Neuroscience, Institute of Child Health, University College London, London, UK., Eltze C; Department of Neurology, Great Ormond Street Hospital, London, UK., Cross JH; Developmental Neuroscience, Institute of Child Health, University College London, London, UK.; Department of Neurology, Great Ormond Street Hospital, London, UK., Tisdall MM; Department of Neurosurgery, Great Ormond Street Hospital, London, UK.; Developmental Neuroscience, Institute of Child Health, University College London, London, UK.
Jazyk:	angličtina
Zdroj:	Developmental medicine and child neurology [Dev Med Child Neurol] 2024 Feb; Vol. 66 (2), pp. 216-225. Date of Electronic Publication: 2023 Aug 09.
DOI:	10.1111/dmcn.15727
Abstrakt:	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. (© 2023 The Authors. Developmental Medicine & Child Neurology published by John Wiley & Sons Ltd on behalf of Mac Keith Press.)
Databáze:	MEDLINE
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