Examinando por Autor "Eriksson, Maria H"

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    Automated and Interpretable Detection of Hippocampal Sclerosis in Temporal Lobe Epilepsy: AID-HS
    (Wiley Periodicals LLC, 2024-11-14) 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
    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.