Global and Regional Deep Learning Models for Multiple Sclerosis Stratification From MRI
Coll, Llucia (Hospital Universitari Vall d'Hebron)
Pareto, Deborah (Hospital Universitari Vall d'Hebron)
Carbonell-Mirabent, Pere (Hospital Universitari Vall d'Hebron)
Cobo-Calvo, Álvaro (Hospital Universitari Vall d'Hebron)
Arrambide, Georgina (Hospital Universitari Vall d'Hebron)
Vidal-Jordana, Angela (Hospital Universitari Vall d'Hebron)
Comabella López, Manuel (Hospital Universitari Vall d'Hebron)
Castilló, Joaquín (Hospital Universitari Vall d'Hebron)
Rodríguez-Acevedo, Breogán (Hospital Universitari Vall d'Hebron)
Zabalza, Ana (Hospital Universitari Vall d'Hebron)
Galan, Ingrid (Hospital Universitari Vall d'Hebron)
Midaglia, Luciana (Hospital Universitari Vall d'Hebron)
Nos, Carlos (Hospital Universitari Vall d'Hebron)
Auger, Cristina (Universitat Autònoma de Barcelona. Departament de Medicina)
Alberich, Manel (Hospital Universitari Vall d'Hebron)
Río, Jordi (Hospital Universitari Vall d'Hebron)
Sastre-Garriga, Jaume (Hospital Universitari Vall d'Hebron)
Oliver, Arnau (Universitat de Girona)
Montalban, Xavier (Universitat Autònoma de Barcelona. Departament de Medicina)
Rovira, Alex (Hospital Universitari Vall d'Hebron)
Tintoré, Mar (Hospital Universitari Vall d'Hebron)
Lladó, Xavier (Universitat de Girona)
Tur, Carmen (Universitat Autònoma de Barcelona. Departament de Medicina)

Data:	2023
Descripció:	10 pàg.
Resum:	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.
Nota:	Altres ajuts: acords transformatius de la UAB
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Publicat a:	Journal of Magnetic Resonance Imaging, ISSN 1522-2586

DOI: 10.1002/jmri.29046
PMID: 37803817

10 p, 2.3 MB

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