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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© 2024 The Author(s). Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.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. ANN NEUROL 2024.
Author(s): Ripart M, DeKraker J, Eriksson MH, Piper RJ, Gopinath S, Parasuram H, Mo J, Likeman M, Ciobotaru G, Sequeiros-Peggs P, Hamandi K, Xie H, Cohen NT, Su T-Y, Kochi R, Wang I, Rojas-Costa GM, Galvez M, Parodi C, Riva A, D'Arco F, Mankad K, Clark CA, Carbo AV, Toledano R, Taylor P, Napolitano A, Rossi-Espagnet MC, Willard A, Sinclair B, Pepper J, Seri S, Devinsky O, Pardoe HR, Winston GP, Duncan JS, Yasuda CL, Scardua-Silva L, Walger L, Ruber T, Khan AR, Baldeweg T, Adler S, Wagstyl K, Zhang K, Bari SMS, Galea J, Illapani VSP, Gaillard WD, Ibanez A, Faure E, Campos M, Severino M, Tortora D, Nobile G, Consales A, Chari A, Tisdall M, Cross JH, Simpson CM, Wang Y, De Palma L, De Benedictis A, Vivash L, O'Brien TJ, De Tisdi J, Alvim MKM, Cendes F
Publication type: Article
Publication status: Published
Journal: Annals of Neurology
Year: 2024
Pages: epub ahead of print
Online publication date: 14/11/2024
Acceptance date: 23/09/2024
Date deposited: 26/11/2024
ISSN (print): 0364-5134
ISSN (electronic): 1531-8249
Publisher: John Wiley and Sons Inc
URL: https://doi.org/0.1002/ana.27089
DOI: 10.1002/ana.27089
Data Access Statement: All data analysis in this study was conducted using Python. The AID-HS software is openly available to download (https://github.com/MELDProject/AID-HS)
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