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 Costanza, Riva Antonella, D'Arco Felice, 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 Ali R, Baldeweg Torsten, Adler Sophie, Wagstyl Konrad
UCL Great Ormond Street Institute of Child Health, London, UK.
McGill University, Montreal, Canada.
Ann Neurol. 2024 Nov 14;97(1):62-75. doi: 10.1002/ana.27089.
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.
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.
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.
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.
海马硬化（HS）是与颞叶癫痫（TLE）相关的最常见病理改变，但在磁共振成像（MRI）上并不总是可见，这会导致手术延迟并降低术后无癫痫发作率。我们开发了一种开源软件来对HS进行特征描述和定位，以辅助对疑似TLE的儿童和成人进行术前评估。
我们纳入了一个多中心队列，共365名参与者（154名HS患者；90名疾病对照者；121名健康对照者）。使用HippUnfold从T1加权MRI扫描中提取基于形态表面的特征和海马体积。我们通过将患者的病理性海马与正常生长图表进行比较并分析个体特征不对称性来对其进行特征描述。特征不对称分数用于训练逻辑回归分类器以检测HS并确定其侧别。该分类器在一个独立的多中心队列中进行了验证，该队列包括275名HS患者以及161名健康和疾病对照者。
HS的特征为体积减小、厚度变薄、脑回化减少，同时平均曲率和固有曲率增加。该分类器检测出90.1%的单侧HS患者，并在97.4%的患者中确定了病变的侧别。在MRI阴性但组织病理学证实为HS的患者中，该分类器检测出79.2%（19/24），并确定侧别为91.7%（22/24）。该模型在独立队列中表现出相似的性能，证明了其对新数据的泛化能力。个体患者报告将患者的海马特征与正常生长轨迹相关联，可视化特征不对称性，并报告分类器预测结果。
海马硬化的自动且可解释检测（AID-HS）是一种用于检测HS并确定其侧别以及输出临床相关报告的开源流程。《神经病学纪事》2024年。
