| Title: |
Automated and Interpretable Detection of Hippocampal Sclerosis in Temporal Lobe Epilepsy: AID-HS |
| Authors: |
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 |
| Source: |
Annals of Neurology, 2024 |
| Publisher Information: |
John Wiley and Sons Inc |
| Publication Year: |
2024 |
| Collection: |
Newcastle University Library ePrints Service |
| Description: |
© 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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
https://eprints.ncl.ac.uk/302618; https://eprints.ncl.ac.uk/fulltext.aspx?url=302618/88B092AD-13C3-47A7-8286-97941A079794.pdf&pub_id=302618 |
| Availability: |
https://eprints.ncl.ac.uk/302618 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.2B62308A |
| Database: |
BASE |