Multi-scale machine learning model predicts muscle and functional disease progression
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 d...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-09516-8 |
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| author | Silvia S. Blemker Lara Riem Olivia DuCharme Megan Pinette Kathryn Eve Costanzo Emma Weatherley Jeff Statland Stephen J. Tapscott Leo H. Wang Dennis W. W. Shaw Xing Song Doris Leung Seth D. Friedman |
| author_facet | Silvia S. Blemker Lara Riem Olivia DuCharme Megan Pinette Kathryn Eve Costanzo Emma Weatherley Jeff Statland Stephen J. Tapscott Leo H. Wang Dennis W. W. Shaw Xing Song Doris Leung Seth D. Friedman |
| author_sort | Silvia S. Blemker |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-89917543fc324e15addf9782ac8b8a6e |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-89917543fc324e15addf9782ac8b8a6e2025-08-20T03:45:55ZengNature PortfolioScientific Reports2045-23222025-07-0115111410.1038/s41598-025-09516-8Multi-scale machine learning model predicts muscle and functional disease progressionSilvia S. Blemker0Lara Riem1Olivia DuCharme2Megan Pinette3Kathryn Eve Costanzo4Emma Weatherley5Jeff Statland6Stephen J. Tapscott7Leo H. Wang8Dennis W. W. Shaw9Xing Song10Doris Leung11Seth D. Friedman12Springbok AnalyticsSpringbok AnalyticsSpringbok AnalyticsSpringbok AnalyticsSpringbok AnalyticsFSHD Global Research FoundationUniversity of Kansas Medical CenterFred Hutchinson Cancer CenterUniversity of WashingtonUniversity of WashingtonUniversity of MissouriKennedy Krieger InstituteSeattle Children’s HospitalAbstract 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.https://doi.org/10.1038/s41598-025-09516-8 |
| spellingShingle | Silvia S. Blemker Lara Riem Olivia DuCharme Megan Pinette Kathryn Eve Costanzo Emma Weatherley Jeff Statland Stephen J. Tapscott Leo H. Wang Dennis W. W. Shaw Xing Song Doris Leung Seth D. Friedman Multi-scale machine learning model predicts muscle and functional disease progression Scientific Reports |
| title | Multi-scale machine learning model predicts muscle and functional disease progression |
| title_full | Multi-scale machine learning model predicts muscle and functional disease progression |
| title_fullStr | Multi-scale machine learning model predicts muscle and functional disease progression |
| title_full_unstemmed | Multi-scale machine learning model predicts muscle and functional disease progression |
| title_short | Multi-scale machine learning model predicts muscle and functional disease progression |
| title_sort | multi scale machine learning model predicts muscle and functional disease progression |
| url | https://doi.org/10.1038/s41598-025-09516-8 |
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