ArticleJournal of neurology2022
Diagnostic magnetic resonance imaging biomarkers for facioscapulohumeral muscular dystrophy identified by machine learning.
Article in Journal of neurology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Whole-Body Pattern of Muscle Degeneration and Progression in Sarcoglycanopathies.Annals of clinical and translational neurology · 2026Article
- RNA cargo profiling of muscle extracellular vesicles identifies candidate biomarkers of disease activity and muscle degeneration in FSHD.Journal of translational medicine · 2026Article
- Muscle Imaging in Inclusion Body Myositis: Refinement of MRI Criteria and Insights Into Upper Body Involvement.Journal of cachexia, sarcopenia and muscle · 2026Article
- Biallelic PAX7 variants cause a novel Satellite Cell-opathy with progressive muscle involvement resembling facioscapulohumeral muscular dystrophy.Cell death & disease · 2026Article
- A neuromuscular clinician's primer on machine learning.Journal of neuromuscular diseases · 2026Review
- Camptocormia as a Phenotypic Variant of FSHD in the Elderly: Clinical, Genetic, and Imaging Features.European journal of neurology · 2025Article
- Myo-Guide: A Machine Learning-Based Web Application for Neuromuscular Disease Diagnosis With MRI.Journal of cachexia, sarcopenia and muscle · 2025Article
- Natural history of facioscapulohumeral muscular dystrophy evaluated by multiparametric quantitative MRI: a prospective cohort study.Journal of neurology · 2025Article
- Deciphering Facioscapulohumeral Dystrophy in the clinical trials era: where are we now?Acta myologica : myopathies and cardiomyopathies : official journal of the Mediterranean Society of Myology · 2025Review
- Deciphering the Complexity of FSHD: A Multimodal Approach as a Model for Rare Disorders.International journal of molecular sciences · 2024Review
- French National Protocol for diagnosis and care of facioscapulohumeral muscular dystrophy (FSHD).Journal of neurology · 2024Review
- Whole exome sequencing highlights rare variants inFrontiers in genetics · 2023Article
- Article
- Non-myogenic mesenchymal cells contribute to muscle degeneration in facioscapulohumeral muscular dystrophy patients.Cell death & disease · 2022Article
- Update on the Molecular Aspects and Methods Underlying the Complex Architecture of FSHD.Cells · 2022Review
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe diagnosis of facioscapulohumeral muscular dystrophy (FSHD) can be challenging in patients not displaying the classical phenotype or with atypical clinical features. Despite the identification by magnetic resonance imaging (MRI) of selective patterns of muscle involvement, their specificity and added diagnostic value are unknown.
methodsWe aimed to identify the radiological features more useful to distinguish FSHD from other myopathies and test the diagnostic accuracy of MRI. A retrospective cohort of 295 patients (187 FSHD, 108 non-FSHD) studied by upper and lower-limb muscle MRI was analyzed. Scans were evaluated for the presence of 15 radiological features. A random forest machine learning algorithm was used to identify the most relevant for FSHD diagnosis. Different patterns were created by their combination and diagnostic accuracy of each of them was tested.
resultsThe combination of trapezius involvement and bilateral subscapularis muscle sparing achieved the best diagnostic accuracy (0.89, 95% Confidence Interval [0.85-0.92]) with 0.90 [0.85-0.94] sensitivity and 0.88 [0.80-0.93] specificity. This pattern correctly identified 91% atypical FSHD patients of our cohort. The combination of trapezius involvement, bilateral subscapularis and iliopsoas sparing and asymmetric involvement of upper and lower-limb muscles was pathognomonic for FSHD, yielding a specificity of 0.99 [0.95-1.00].
conclusionsWe identified MRI patterns that showed a high diagnostic power in promptly discriminating FSHD from other muscle disorders, with comparable performance irrespective of typical or atypical clinical features. Upper girdle in addition to lower-limb muscle imaging should be extensively implemented in the diagnostic workup to support or exclude a diagnosis of FSHD.
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