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- Title
Global and Regional Deep Learning Models for Multiple Sclerosis Stratification From MRI.
- 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
- Abstract
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 < 3.0. Two strategies were investigated: 1) a global approach, taking the whole brain volume as input and 2) regional approaches using five different regions‐of‐interest: white matter, gray matter, subcortical gray matter, ventricles, and brainstem structures. The performance of the models was assessed in the in‐house and the independent external cohorts. Statistical Tests: Balanced accuracy, sensitivity, specificity, area under receiver operating characteristic (ROC) curve (AUC). Results: With the in‐house dataset, the gray matter regional model showed the highest stratification accuracy (81%), followed by the global approach (79%). In the external dataset, without any further retraining, an accuracy of 72% was achieved for the white matter model and 71% for the global approach. Data Conclusion: The global approach offered the best trade‐off between internal performance and external validation to stratify MS patients based on accumulated disability. Evidence Level: 4 Technical Efficacy: Stage 2
- Subjects
DEEP learning; MULTIPLE sclerosis; GRAY matter (Nerve tissue); CONVOLUTIONAL neural networks; RECEIVER operating characteristic curves
- Publication
Journal of Magnetic Resonance Imaging, 2024, Vol 60, Issue 1, p258
- ISSN
1053-1807
- Publication type
Article
- DOI
10.1002/jmri.29046