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Interpretable surface-based detection of focal cortical dysplasias: A Multi-centre Epilepsy Lesion Detection study.
Brain 145, 3859-3871 (2022)
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Forschungsdaten
DOI
PMC
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.
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Focal Cortical Dysplasia ; Epilepsy ; Structural Mri ; Machine Learning
ISSN (print) / ISBN
0006-8950
e-ISSN
1460-2156
Zeitschrift
Brain: A Journal of Neurology
Quellenangaben
Band: 145,
Heft: 11,
Seiten: 3859-3871
Verlag
Oxford University Press
Nichtpatentliteratur
Publikationen
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute of Computational Biology (ICB)
Förderungen
Welsh Government
King's College London
Medical Research Council Centre
Sir Henry Dale Fellowship
São Paulo Research Foundation
Hess Foundation and Children's National IDDRC
CNRI Chief Research Officer Award
CNF/PERF Shields Award
NIHR GOSH BRC
DINOGMI Department of Excellence
BRAIN Unit Infrastructure
Saastamoinen Foundation
GOSH Children's Charity Surgeon-Scientist Fellowship
NHMRC Investigator
NIHR
Tuscany Region Call for Health
NIH HHS
Alan Turing Institute
MRC
Rosetrees Trust
King's College London
Medical Research Council Centre
Sir Henry Dale Fellowship
São Paulo Research Foundation
Hess Foundation and Children's National IDDRC
CNRI Chief Research Officer Award
CNF/PERF Shields Award
NIHR GOSH BRC
DINOGMI Department of Excellence
BRAIN Unit Infrastructure
Saastamoinen Foundation
GOSH Children's Charity Surgeon-Scientist Fellowship
NHMRC Investigator
NIHR
Tuscany Region Call for Health
NIH HHS
Alan Turing Institute
MRC
Rosetrees Trust