Automated and Interpretable Detection of Hippocampal Sclerosis in Temporal Lobe Epilepsy: AID-HS
- Autores
- Ripart, Mathilde
- DeKraker, Jordan
- Eriksson, Maria H
- Piper, Rory J
- Gopinath, Siby
- Parasuram, Harilal
- Mo, Jiajie
- Likeman, Marcus
- Ciobotaru, Georgian
- Sequeiros-Peggs, Philip
- Hamandi, Khalid
- Xie, Hua
- Cohen, Nathan T
- Su, Ting-Yu
- Kochi, Ryuzaburo
- Wang, Irene
- Rojas-Costa, Gonzalo M
- Gálvez, Marcelo
- Parodi, Constanza
- Riva, Antonella
- D'Arco, Felipe
- Mankad, Kshitij
- Clark, Chris A
- Carbó, Adrián Valls
- Toledano, Rafael
- Taylor, Peter
- Napolitano, Antonio
- Rossi-Espagnet, Maria Camilla
- Willard, Anna
- Sinclair, Benjamin
- Pepper, Joshua
- Seri, Stefano
- Devinsky, Orrin
- Pardoe, Heath R
- Winston, Gavin P
- Duncan, John S
- Yasuda, Clarissa L
- Scárdua-Silva, Lucas
- Walger, Lennart
- Rüber, Theodor
- Khan, Aali R
- Baldeweg, Torsten
- Adler, Sophie
- Wagstyl, Konrad
- MELD HS study group
- Editorial
- Wiley Periodicals LLC
- Palabras clave
- 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.