Background: The 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. Methods: We 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. Results: The 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]. Conclusions: We 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.
|Rivista||Journal of Neurology|
|Stato di pubblicazione||Pubblicato - 2021|
- Facioscapulohumeral muscular dystrophy
- Machine learning
- Muscle MRI