Detection of Epileptogenic Focal Cortical Dysplasia Using Graph Neural Networks
Authors: Mathilde Ripart, Hannah Spitzer, Logan Z. J. Williams, Lennart Walger, Andrew Chen, Antonio Napolitano, Camilla Rossi-Espagnet, Stephen T. Foldes, Wenhan Hu, Jiajie Mo, Marcus Likeman, Theodor Rüber, Maria Eugenia Caligiuri, Antonio Gambardella, Christopher Guttler, Anna Tietze, Matteo Lenge, Renzo Guerrini, Nathan T. Cohen, Irene Wang, Ane Kloster, Lars H. Pinborg, Khalid Hamandi, Graeme Jackson, Domenico Tortora, Martin Tisdall, Estefania Conde-Blanco, Jose C. Pariente, Carmen Perez-Enriquez, Sofia Gonzalez-Ortiz, Nandini Mullatti, Katy Vecchiato, Yawu Liu, Reetta Kalviainen, Drahoslav Sokol, Jay Shetty, Benjamin Sinclair, Lucy Vivash, Anna Willard, Gavin P. Winston, Clarissa Yasuda, Fernando Cendes, Russell T. Shinohara, John S. Duncan, J. Helen Cross, Torsten Baldeweg, Emma C. Robinson, Juan Eugenio Iglesias, Sophie Adler, Konrad Wagstyl, 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, Aswin Chari, Felice D’Arco, Kshitij Mankad, Nuria Bargallo, Saul Pascual-Diaz, Ignacio Delgado-Martinez, Jonathan O’Muircheartaigh, Eugenio Abela, Jothy Kandasamy, Ailsa McLellan, Patricia Desmond, Elaine Lui, Terence J. O’Brien, Kirstie Whitaker
Journal: JAMA Neurology (2025)

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 dataset, the MELD Graph had a sensitivity of 81.6% in histopathologically confirmed patients seizure-free 1 year after surgery and 63.7% in MRI–negative patients with FCD. The PPV of putative lesions from the 260 patients in the test dataset (125 female [48%] and 135 male [52%]; mean age, 18.0 [IQR, 11.0-29.0] years) was 67% (70% sensitivity; 60% specificity), compared with 39% (67% sensitivity; 54% specificity) using an existing baseline algorithm. In the independent test cohort (116 patients; 62 female [53%] and 54 male [47%]; mean age, 22.5 [IQR, 13.5-27.5] years), the PPV was 76% (72% sensitivity; 56% specificity), compared with 46% (77% sensitivity; 47% specificity) using the baseline algorithm. Interpretable reports characterize lesion location, size, confidence, and salient features. Conclusions and Relevance In this study, the MELD Graph represented a state-of-the-art, openly available, and interpretable tool for FCD detection on MRI scans with significant improvements in PPV. Its clinical implementation holds promise for early diagnosis and improved management of focal epilepsy, potentially leading to better patient outcomes.