Detection of Epileptogenic Focal Cortical Dysplasia Using Graph Neural Networks

作者：Mathilde Ripart, Hannah Spitzer, Logan Z. J. Williams, Lennart Walger, Andrew A. Chen, Antonio Napolitano, Maria Camilla Rossi‐Espagnet, Stephen T. Foldes, Wenhan Hu, Jiajie Mo, Marcus Likeman, Theodor Rüber, Maria Eugenia Caligiuri, Antonio Gambardella, Christopher Güttler, Anna Tietze, Matteo Lenge, Renzo Guerrini, Nathan T. Cohen, Irène Wang, Ane Kloster, Lars H. Pinborg, Khalid Hamandi, Graeme D. Jackson, Domenico Tortora, Martin Tisdall, Estefanía Conde‐Blanco, José C. Pariente, Carmen Pérez‐Enríquez, Sofía González‐Ortiz, Nandini Mullatti, Katy Vecchiato, Yawu Liu, Reetta Kälviäinen, Drahoslav Sokol, Jay Shetty, Benjamin Sinclair, Lucy Vivash, Anna Willard, Gavin P. Winston, Clarissa Lin Yasuda, Fernando Cendes, Russell T. Shinohara, John S. Duncan, J. Helen Cross, Torsten Baldeweg, Emma C. Robinson, Juan Eugenio Iglesias, Sophie Adler, Konrad Wagstyl, MELD FCD writing group, Abdulah Fawaz, Alessandro De Benedictis, Luca De Palma, Kai Zhang, Angelo Labate, Carmen Barba, Xiaozhen You, William D. Gaillard, Yingying Tang, Shan Wang, Shirin Davies, Mira Semmelroch, Mariasavina Severino, Pasquale Striano, Ajai Chari, Felice D’Arco, Kshitij Mankad, Núria Bargalló, Saül Pascual‐Diaz, Ignacio Delgado, Jonathan O’Muircheartaigh, Eugenio Abela, Jothy Kandasamy, Ailsa McLellan, Patricia Desmond, Elaine Lui, Terence J. O’Brien, Kirstie Whitaker · 发表于：JAMA Neurology · 年份：2025 · DOI：10.1001/jamaneurol.2024.5406 · 被引用次数：49 · 研究领域：Epilepsy research and treatment、Functional Brain Connectivity Studies、EEG and Brain-Computer Interfaces

Importance: A leading cause of surgically remediable, drug-resistant focal epilepsy is focal cortical dysplasia (FCD). FCD is challenging to visualize and often considered magnetic resonance imaging (MRI) negative. Existing automated methods for FCD detection are limited by high numbers of false-positive predictions, hampering their clinical utility. Objective: To evaluate the efficacy and interpretability of graph neural networks in automatically detecting FCD lesions on MRI scans. Design, Setting, and Participants: In this multicenter diagnostic study, retrospective MRI data were collated from 23 epilepsy centers worldwide between 2018 and 2022, as part of the Multicenter Epilepsy Lesion Detection (MELD) Project, and analyzed in 2023. Data from 20 centers were split equally into training and testing cohorts, with data from 3 centers withheld for site-independent testing. A graph neural network (MELD Graph) was trained to identify FCD on surface-based features. Network performance was compared with an existing algorithm. Feature analysis, saliencies, and confidence scores were used to interpret network predictions. In total, 34 surface-based MRI features and manual lesion masks were collated from participants, 703 patients with FCD-related epilepsy and 482 controls, and 57 participants were excluded during MRI quality control. Main Outcomes and Measures: Sensitivity, specificity, and positive predictive value (PPV) of automatically identified lesions. Results: In the test da...