Interpretable surface-based detection of focal cortical dysplasias: a Multi-centre Epilepsy Lesion Detection study
Authors: Spitzer Hannah , Ripart Mathilde , Whitaker Kirstie , D’Arco Felice , Mankad Kshitij , Chen Andrew A , Napolitano Antonio , De Palma Luca , De Benedictis Alessandro , Foldes Stephen , Humphreys Zachary , Zhang Kai , Hu Wenhan , Mo Jiajie , Likeman Marcus , Davies Shirin , Güttler Christopher , Lenge Matteo , Cohen Nathan T , Tang Yingying , Wang Shan , Chari Aswin , Tisdall Martin , Bargallo Nuria , Conde-Blanco Estefanía , Pariente Jose Carlos , Pascual-Diaz Saül , Delgado-Martínez Ignacio , Pérez-Enríquez Carmen , Lagorio Ilaria , Abela Eugenio , Mullatti Nandini , O’Muircheartaigh Jonathan , Vecchiato Katy , Liu Yawu , Caligiuri Maria Eugenia , Sinclair Ben , Vivash Lucy , Willard Anna , Kandasamy Jothy , McLellan Ailsa , Sokol Drahoslav , Semmelroch Mira , Kloster Ane G , Opheim Giske , Ribeiro Letícia , Yasuda Clarissa , Rossi-Espagnet Camilla , Hamandi Khalid , Tietze Anna , Barba Carmen , Guerrini Renzo , Gaillard William Davis , You Xiaozhen , Wang Irene , González-Ortiz Sofía , Severino Mariasavina , Striano Pasquale , Tortora Domenico , Kälviäinen Reetta , Gambardella Antonio , Labate Angelo , Desmond Patricia , Lui Elaine , O’Brien Terence , Shetty Jay , Jackson Graeme , Duncan John S , Winston Gavin P , Pinborg Lars H , Cendes Fernando , Theis Fabian J , Shinohara Russell T , Cross J Helen , Baldeweg Torsten , Adler Sophie , Wagstyl Konrad 
Published: August 2022

One outstanding challenge for machine learning in diagnostic biomedical imaging is algorithm interpretability. A key application is the identification of subtle epileptogenic focal cortical dysplasias (FCDs) from structural MRI. FCDs are difficult to visualize on structural MRI but are often amenable to surgical resection. We aimed to develop an open-source, interpretable, surface-based machine-learning algorithm to automatically identify FCDs on heterogeneous structural MRI data from epilepsy surgery centres worldwide.

The Multi-centre Epilepsy Lesion Detection (MELD) Project collated and harmonized a retrospective MRI cohort of 1015 participants, 618 patients with focal FCD-related epilepsy and 397 controls, from 22 epilepsy centres worldwide. We created a neural network for FCD detection based on 33 surface-based features. The network was trained and cross-validated on 50% of the total cohort and tested on the remaining 50% as well as on 2 independent test sites. Multidimensional feature analysis and integrated gradient saliencies were used to interrogate network performance.

Our pipeline outputs individual patient reports, which identify the location of predicted lesions, alongside their imaging features and relative saliency to the classifier. On a restricted ‘gold-standard’ subcohort of seizure-free patients with FCD type IIB who had T1 and fluid-attenuated inversion recovery MRI data, the MELD FCD surface-based algorithm had a sensitivity of 85%. Across the entire withheld test cohort the sensitivity was 59% and specificity was 54%. After including a border zone around lesions, to account for uncertainty around the borders of manually delineated lesion masks, the sensitivity was 67%.

This multicentre, multinational study with open access protocols and code has developed a robust and interpretable machine-learning algorithm for automated detection of focal cortical dysplasias, giving physicians greater confidence in the identification of subtle MRI lesions in individuals with epilepsy.