1. Automated and Interpretable Detection of Hippocampal Sclerosis in Temporal Lobe Epilepsy: AID-HS.
- Author
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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 TY, Kochi R, Wang I, Rojas-Costa GM, Gálvez M, Parodi C, Riva A, D'Arco F, Mankad K, Clark CA, Carbó 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, Scárdua-Silva L, Walger L, Rüber T, Khan AR, Baldeweg T, Adler S, and Wagstyl K
- Abstract
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., (© 2024 The Author(s). Annals of Neurology published by Wiley Periodicals LLC on behalf of American Neurological Association.)
- Published
- 2024
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