Automated and Interpretable Detection of Hippocampal Sclerosis in Temporal Lobe Epilepsy: AID-HS

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Annals of Neurology - 2024 - Ripart - Automated and Interpretable Detection of Hippocampal Sclerosis in Temporal Lobe-4.pdf (2.35 MB)
Fecha
2024-11-14
Nota de Acceso
Fecha de embargo
Autores
Profe guía
Perfil ORCID
https://orcid.org/0000-0002-6228-2678
Título de la revista
ISSN de la revista
Título del volumen
Editor
Wiley Periodicals LLC
ISBN
ISSN
0364-5134
1531-8249
ISSNe
DOI
https://doi.org/10.1002/ana.27089
URI
https://hdl.handle.net/20.500.12254/7689
Resumen
Objective: Hippocampal sclerosis (HS), the most common pathology associated with temporal lobe epilepsy (TLE), is not always visible on magnetic resonance imaging (MRI), causing surgical delays and reduced postsurgical seizure-freedom. We developed an open-source software to characterize and localize HS to aid the presurgical evaluation of children and adults with suspected TLE. Methods: We included a multicenter cohort of 365 participants (154 HS; 90 disease controls; 121 healthy controls). HippUnfold was used to extract morphological surface-based features and volumes of the hippocampus from T1-weighted MRI scans. We characterized pathological hippocampi in patients by comparing them to normative growth charts and analyzing within-subject feature asymmetries. Feature asymmetry scores were used to train a logistic regression classifier to detect and lateralize HS. The classifier was validated on an independent multicenter cohort of 275 patients with HS and 161 healthy and disease controls. Results: HS was characterized by decreased volume, thickness, and gyrification alongside increased mean and intrinsic curvature. The classifier detected 90.1% of unilateral HS patients and lateralized lesions in 97.4%. In patients with MRI-negative histopathologically-confirmed HS, the classifier detected 79.2% (19/24) and lateralized 91.7% (22/24). The model achieved similar performances on the independent cohort, demonstrating its ability to generalize to new data. Individual patient reports contextualize a patient's hippocampal features in relation to normative growth trajectories, visualise feature asymmetries, and report classifier predictions. Interpretation: Automated and Interpretable Detection of Hippocampal Sclerosis (AID-HS) is an open-source pipeline for detecting and lateralizing HS and outputting clinically-relevant reports.
Descripción
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Estados Unidos
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Citación
Annals of Neurology, Vol. 97, N°. 1 (2024) pp. 62-75
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Atribución-NoComercial-CompartirIgual 3.0 Chile (CC BY-NC-SA 3.0 CL)
Licencia URL
http://creativecommons.org/licenses/by-nc-sa/3.0/cl/
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