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