Global and Regional Deep Learning Models for Multiple Sclerosis Stratification From MRI

Background The combination of anatomical MRI and deep learning‐based methods such as convolutional neural networks (CNNs) is a promising strategy to build predictive models of multiple sclerosis (MS) prognosis. However, studies assessing the effect of different input strategies on model's perfo...

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Bibliographic Details
Published in:Journal of magnetic resonance imaging 2024-07, Vol.60 (1), p.258-267
Main Authors: Coll, Llucia, Pareto, Deborah, Carbonell‐Mirabent, Pere, Cobo‐Calvo, Álvaro, Arrambide, Georgina, Vidal‐Jordana, Ángela, Comabella, Manuel, Castilló, Joaquín, Rodrı́guez‐Acevedo, Breogán, Zabalza, Ana, Galán, Ingrid, Midaglia, Luciana, Nos, Carlos, Auger, Cristina, Alberich, Manel, Río, Jordi, Sastre‐Garriga, Jaume, Oliver, Arnau, Montalban, Xavier, Rovira, Àlex, Tintoré, Mar, Lladó, Xavier, Tur, Carmen
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
Brain
Brain stem
classification
Datasets
Deep learning
Field strength
input sampling
Machine learning
Magnetic resonance imaging
Multiple sclerosis
Neural networks
Prediction models
Statistical analysis
Statistical tests
structural MRI
Substantia alba
Substantia grisea
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Summary:Background The combination of anatomical MRI and deep learning‐based methods such as convolutional neural networks (CNNs) is a promising strategy to build predictive models of multiple sclerosis (MS) prognosis. However, studies assessing the effect of different input strategies on model's performance are lacking. Purpose To compare whole‐brain input sampling strategies and regional/specific‐tissue strategies, which focus on a priori known relevant areas for disability accrual, to stratify MS patients based on their disability level. Study Type Retrospective. Subjects Three hundred nineteen MS patients (382 brain MRI scans) with clinical assessment of disability level performed within the following 6 months (~70% training/~15% validation/~15% inference in‐house dataset) and 440 MS patients from multiple centers (independent external validation cohort). Field Strength/Sequence Single vendor 1.5 T or 3.0 T. Magnetization‐Prepared Rapid Gradient‐Echo and Fluid‐Attenuated Inversion Recovery sequences. Assessment A 7‐fold patient cross validation strategy was used to train a 3D‐CNN to classify patients into two groups, Expanded Disability Status Scale score (EDSS) ≥ 3.0 or EDSS 
ISSN:1053-1807
1522-2586
DOI:10.1002/jmri.29046