MRI-derived 3D lower limb muscle shape as a biomarker for disease severity in Duchenne muscular dystrophy
Researchers analyzed MRI data from children with Duchenne muscular dystrophy and healthy controls to assess muscle shape patterns. Shape descriptors showed high accuracy in distinguishing groups and predicting time-to-am
Duchenne muscular dystrophy (DMD) is characterized by progressive muscle degeneration leading to loss of ambulation. Identification of predictive biomarkers for this clinical milestone is crucial, yet analysis of muscle shape remains underexplored compared to traditional volumetric measures.
The study utilized MRI data from 17 children with DMD, including 10 who had subsequently lost ambulation, alongside 10 healthy controls. Researchers analyzed the three-dimensional shapes and volumes of lower limb muscles to evaluate correlations with functional metrics such as gait tests and dynamometric forces.
Key findings revealed that individuals with DMD exhibited distinct shape patterns in key muscle groups, particularly the triceps surae. These included increased thickness and reduced extensibility without consistent differences in absolute volume. Cross-validated analyses demonstrated that models based on shape descriptors achieved high accuracy in distinguishing controls from patients and yielded a mean absolute error of approximately 110 days for time-to-ambulation-loss prediction.
These findings suggest that geometric descriptors may provide complementary, acquisition-agnostic information to MRI fat-related measures. However, the authors caution that these results require validation in larger, independent cohorts before any clinical use is considered. This research highlights a potential avenue for non-invasive monitoring of disease progression in metabolic and neuromuscular contexts.