Utilization of Multi-Parametric Quantitative Magnetic Resonance Imaging in the Early Diagnosis of Duchenne Muscular Dystrophy

Multi-parameter quantitative magnetic resonance imaging for early detecting skeletal muscle involvement and predicting functional decline in children with Becker muscular dystrophy

BACKGROUND

The extreme clinical heterogeneity of children with Becker muscular dystrophy significantly poses a great challenge to accurately assess their disease status.

OBJECTIVE

To detect skeletal muscle involvement in children with Becker muscular dystrophy using multiple-parameter quantitative magnetic resonance imaging (qMRI), and to determine the preferred muscle site and qMRI biomarker.

MATERIALS AND METHODS

Fat fraction, T1, and T2 measurements were conducted in Becker muscular dystrophy (n=29) and healthy controls (n=23). North Star Ambulatory Assessment (NSAA) was performed in Becker muscular dystrophy. Group differences were compared by using the Mann-Whitney or Kruskal-Wallis test or a linear mixed-effect model. Receiver operating characteristic analysis with area under curve (AUC) was used to compare the diagnostic performance. Logistic regression was used to identify the predictor of functional decline.

RESULTS

Both fat fraction and T2 were effective in detecting muscle involvement across different functional stages that were categorized by NSAA, with fat fraction in gluteus maximus demonstrating the most superior diagnostic performance (AUC range, 0.85-0.98). The combination of T2 and T1 enables a good diagnosis of no abnormal fat-infiltrated muscles (AUC=0.82). Overall, fat fraction in gluteus maximus exhibited the strongest negative correlation with the NSAA score (r=-0.69, P<0.01) and emerged as an independent risk factor for functional decline (odds ratio=1.12, P=0.02).

CONCLUSION

Multi-parametric qMRI demonstrate effective capabilities for early detection of muscle involvement, with gluteus maximus being the preferred muscle site. Fat fraction in gluteus maximus may serve as a potential biomarker for predicting functional decline.