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<p>Duchenne muscular dystrophy (DMD) is characterized by progressive muscle degeneration leading to loss of ambulation. Identification of predictive biomarkers of loss of ambulation is crucial, yet analysis of muscle shape remains underexplored. Using MRI data from 17 children with DMD (10 followed by loss of ambulation) and 10 healthy controls, we analyzed the muscle shapes and volumes of the lower limbs. Correlations with functional metrics (gait tests, dynamometric forces) were evaluated, and exploratory predictive modeling tasks (classification and regression) were performed via random forests with cross-validation. Compared with controls, key muscle groups, particularly the triceps surae, in individuals with DMD presented distinct shape patterns, including increased thickness and reduced extensibility, without consistent differences in absolute volume. In cross-validated analyses and within the limits of this small cohort, models based on shape descriptors showed high accuracy for distinguishing controls from DMD 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, but require validation in larger, independent cohorts before any clinical use is considered.</p>

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MRI-derived 3D lower limb muscle shape as a biomarker for disease severity in Duchenne muscular dystrophy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月30日

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  • Nathan Decaux,
  • François Rousseau,
  • Pierre-Henri Conze,
  • Christelle Pons,
  • Douraied Ben Salem,
  • Sylvain Brochard,
  • Juliette Ropars

摘要

Duchenne muscular dystrophy (DMD) is characterized by progressive muscle degeneration leading to loss of ambulation. Identification of predictive biomarkers of loss of ambulation is crucial, yet analysis of muscle shape remains underexplored. Using MRI data from 17 children with DMD (10 followed by loss of ambulation) and 10 healthy controls, we analyzed the muscle shapes and volumes of the lower limbs. Correlations with functional metrics (gait tests, dynamometric forces) were evaluated, and exploratory predictive modeling tasks (classification and regression) were performed via random forests with cross-validation. Compared with controls, key muscle groups, particularly the triceps surae, in individuals with DMD presented distinct shape patterns, including increased thickness and reduced extensibility, without consistent differences in absolute volume. In cross-validated analyses and within the limits of this small cohort, models based on shape descriptors showed high accuracy for distinguishing controls from DMD 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, but require validation in larger, independent cohorts before any clinical use is considered.