Multi-scale machine learning model predicts muscle and functional disease progression.
Authors
Affiliations (10)
Affiliations (10)
- Springbok Analytics, 110 Old Preston Ave, Charlottesville, VA, 22902, USA. [email protected].
- University of Virginia, Charlottesville, VA, USA. [email protected].
- Springbok Analytics, 110 Old Preston Ave, Charlottesville, VA, 22902, USA.
- FSHD Global Research Foundation, Sydney, AU, Australia.
- University of Kansas Medical Center, Kansas City, KS, USA.
- Fred Hutchinson Cancer Center, Seattle, WA, USA.
- University of Washington, Seattle, WA, USA.
- Seattle Children's Hospital, Seattle, WA, USA.
- University of Missouri, Columbia, MO, USA.
- Kennedy Krieger Institute, Baltimore, MD, USA.
Abstract
Facioscapulohumeral muscular dystrophy (FSHD) is a genetic neuromuscular disorder characterized by progressive muscle degeneration with substantial variability in severity and progression patterns. FSHD is a highly heterogeneous disease; however, current clinical metrics used for tracking disease progression lack sensitivity for personalized assessment, which greatly limits the design and execution of clinical trials. This study introduces a multi-scale machine learning framework leveraging whole-body magnetic resonance imaging (MRI) and clinical data to predict regional, muscle, joint, and functional progression in FSHD. The goal this work is to create a 'digital twin' of individual FSHD patients that can be leveraged in clinical trials. Using a combined dataset of over 100 patients from seven studies, MRI-derived metrics-including fat fraction, lean muscle volume, and fat spatial heterogeneity at baseline-were integrated with clinical and functional measures. A three-stage random forest model was developed to predict annualized changes in muscle composition and a functional outcome (timed up-and-go (TUG)). All model stages revealed strong predictive performance in separate holdout datasets. After training, the models predicted fat fraction change with a root mean square error (RMSE) of 2.16% and lean volume change with a RMSE of 8.1 ml in a holdout testing dataset. Feature analysis revealed that metrics of fat heterogeneity within muscle predicts muscle-level progression. The stage 3 model, which combined functional muscle groups, predicted change in TUG with a RMSE of 0.6 s in the holdout testing dataset. This study demonstrates the machine learning models incorporating individual muscle and performance data can effectively predict MRI disease progression and functional performance of complex tasks, addressing the heterogeneity and nonlinearity inherent in FSHD. Further studies incorporating larger longitudinal cohorts, as well as comprehensive clinical and functional measures, will allow for expanding and refining this model. As many neuromuscular diseases are characterized by variability and heterogeneity similar to FSHD, such approaches have broad applicability.