Denny, A. P. & Heather, A. K. Are Antioxidants a Potential Therapy for FSHD? A Review of the Literature. Oxid Med Cell Longev 7020295 (2017). (2017).
Andersen, G. et al. MRI as outcome measure in facioscapulohumeral muscular dystrophy: 1-year follow-up of 45 patients. J. Neurol. 264, 438–447 (2017).
Google Scholar
Dahlqvist, J. R. et al. Relationship between muscle inflammation and fat replacement assessed by MRI in facioscapulohumeral muscular dystrophy. J. Neurol. 266, 1127–1135 (2019).
Google Scholar
Dahlqvist, J. R. et al. Evaluation of inflammatory lesions over 2 years in facioscapulohumeral muscular dystrophy. Neurology 95, e1211–e1221 (2020).
Google Scholar
Fatehi, F. et al. Long-term follow-up of MRI changes in thigh muscles of patients with facioscapulohumeral dystrophy: A quantitative study. PLoS One. 12, e0183825 (2017).
Google Scholar
Ferguson, M. R. et al. MRI change metrics of facioscapulohumeral muscular dystrophy: stir and T1. Muscle Nerve. 57, 905–912 (2018).
Google Scholar
Friedman, S. D. et al. Longitudinal features of STIR bright signal in FSHD. Muscle Nerve. 49, 257–260 (2014).
Google Scholar
Janssen, B. H. et al. Distinct disease phases in muscles of facioscapulohumeral dystrophy patients identified by MR detected fat infiltration. PLoS One. 9, e85416 (2014).
Google Scholar
Monforte, M. et al. Tracking muscle wasting and disease activity in facioscapulohumeral muscular dystrophy by qualitative longitudinal imaging. J. Cachexia Sarcopenia Muscle. 10, 1258–1265 (2019).
Google Scholar
Wang, L. H. et al. Longitudinal study of MRI and functional outcome measures in facioscapulohumeral muscular dystrophy. BMC Musculoskelet. Disord. 22, 262 (2021).
Google Scholar
Han, J. J. et al. Reachable workspace in facioscapulohumeral muscular dystrophy (FSHD) by kinect. Muscle Nerve. 51, 168–175 (2015).
Google Scholar
Olsen, D. B., Gideon, P., Jeppesen, T. D. & Vissing, J. Leg muscle involvement in facioscapulohumeral muscular dystrophy assessed by MRI. J. Neurol. 253, 1437–1441 (2006).
Google Scholar
Kan, H. E. et al. Quantitative MR imaging of individual muscle involvement in facioscapulohumeral muscular dystrophy. Neuromuscul. Disord. 19, 357–362 (2009).
Google Scholar
Kan, H. E. et al. Only fat infiltrated muscles in resting lower leg of FSHD patients show disturbed energy metabolism. NMR Biomed. 23, 563–568 (2010).
Google Scholar
Friedman, S. D. et al. The magnetic resonance imaging spectrum of facioscapulohumeral muscular dystrophy. Muscle Nerve. 45, 500–506 (2012).
Google Scholar
Tasca, G. et al. Different molecular signatures in magnetic resonance imaging-staged facioscapulohumeral muscular dystrophy muscles. PLoS One. 7, e38779 (2012).
Google Scholar
Lareau-Trudel, E. et al. Muscle quantitative MR imaging and clustering analysis in patients with facioscapulohumeral muscular dystrophy type 1. PLoS One. 10, e0132717 (2015).
Google Scholar
Leung, D. G., Carrino, J. A., Wagner, K. R. & Jacobs, M. A. Whole-body magnetic resonance imaging evaluation of facioscapulohumeral muscular dystrophy. Muscle Nerve. 52, 512–520 (2015).
Google Scholar
Mul, K. et al. Quantitative muscle MRI and ultrasound for facioscapulohumeral muscular dystrophy: complementary imaging biomarkers. J. Neurol. 265, 2646–2655 (2018).
Google Scholar
Wang, L. H. et al. MRI-informed muscle biopsies correlate MRI with pathology and DUX4 target gene expression in FSHD. Hum. Mol. Genet. 28, 476–486 (2019).
Google Scholar
Heskamp, L., Ogier, A., Bendahan, D. & Heerschap, A. Whole-muscle fat analysis identifies distal muscle end as disease initiation site in facioscapulohumeral muscular dystrophy. Commun. Med. (Lond). 2, 155 (2022).
Google Scholar
Mellion, M. L. et al. Quantitative muscle analysis in FSHD using Whole-Body Fat-Referenced MRI: composite scores for longitudinal and Cross-sectional analysis. Neurology 99, e877–e889 (2022).
Google Scholar
Widholm, P. et al., Quantitative muscle analysis in facioscapulohumeral muscular dystrophy using whole-body fat-referenced MRI: Protocol development, multicenter feasibility, and repeatability. Muscle Nerve (2022).
Riem, L. et al. AI driven analysis of MRI to measure health and disease progression in FSHD. Sci. Rep. 14, 15462 (2024).
Google Scholar
Dijkstra, J. N. et al. Natural history of facioscapulohumeral dystrophy in children: A 2-Year Follow-up. Neurology 97, e2103–e2113 (2021).
Google Scholar
Woodcock, I. R., de Valle, K., Varma, N., Kean, M. & Ryan, M. M. Correlation between whole body muscle MRI and functional measures in paediatric patients with facioscapulohumeral muscular dystrophy. Neuromuscul. Disord. 33, 15–23 (2023).
Google Scholar
Willcocks, R. J. et al. Multicenter prospective longitudinal study of magnetic resonance biomarkers in a large Duchenne muscular dystrophy cohort. Ann. Neurol. 79, 535–547 (2016).
Google Scholar
Sampson, J. H. et al., MDNA55 survival in recurrent glioblastoma (rGBM) patients expressing the interleukin-4 receptor (IL4R) as compared to a matched synthetic control. J Clin. Oncol 38 (2020).
Zhou, N. & Manser, P. Does including machine learning predictions in ALS clinical trial analysis improve statistical power? Ann. Clin. Transl Neurol. 7, 1756–1765 (2020).
Google Scholar
Bordukova, M., Makarov, N., Rodriguez-Esteban, R., Schmich, F. & Menden, M. P. Generative artificial intelligence empowers digital twins in drug discovery and clinical trials. Expert Opin. Drug Discov. 19, 33–42 (2024).
Google Scholar
Wong, C. J. et al., Regional and bilateral MRI and gene signatures in facioscapulohumeral dystrophy: implications for clinical trial design and mechanisms of disease progression. Hum Mol. Genet (2024).
Ni, R., Meyer, C. H., Blemker, S. S., Hart, J. M. & Feng, X. Automatic segmentation of all lower limb muscles from high-resolution magnetic resonance imaging using a cascaded three-dimensional deep convolutional neural network. J. Med. Imaging (Bellingham). 6, 044009 (2019).
Google Scholar
Santago, A. C. 2 Quantitative analysis of Three-Dimensional distribution and clustering of intramuscular fat in muscles of the rotator cuff. Ann. Biomed. Eng. 44, 2158–2167 (2016)., et al.
Google Scholar
Chang, R. et al. Percentage fat fraction in magnetic resonance imaging: upgrading the osteoporosis-detecting parameter. BMC Med. Imaging. 20, 30 (2020).
Google Scholar
Hothorn, T. & Jung, H. H. RandomForest4Life: a random forest for predicting ALS disease progression. Amyotroph. Lateral Scler. Frontotemporal Degener. 15, 444–452 (2014).
Google Scholar
Ko, K. D., El-Ghazawi, T., Kim, D. & Morizono, H. & Pooled Resource Open-Access, A.L.S.C.T.C. Predicting the severity of motor neuron disease progression using electronic health record data with a cloud computing Big Data approach. IEEE Symp Comput Intell Bioinforma Comput Biol Proc 2014 (2014).
Lundberg, S. M. & Lee, S. I. A unified approach to interpreting model predictions. Adv Neur in 30 (2017).
Ponce-Bobadilla, A. V., Schmitt, V., Maier, C. S., Mensing, S. & Stodtmann, S. Practical guide to SHAP analysis: explaining supervised machine learning model predictions in drug development. Clin. Transl Sci. 17, e70056 (2024).
Google Scholar
Wang, L. H., Johnstone, L. M., Bindschadler, M., Tapscott, S. J. & Friedman, S. D. Adapting MRI as a clinical outcome measure for a facioscapulohumeral muscular dystrophy trial of prednisone and tacrolimus: case report. BMC Musculoskelet. Disord. 22, 56 (2021).
Google Scholar
Colelli, G. et al. Radiomics and machine learning applied to STIR sequence for prediction of quantitative parameters in facioscapulohumeral disease. Front. Neurol. 14, 1105276 (2023).
Google Scholar
Fatehi, F., Salort-Campana, E., Le Troter, A., Bendahan, D. & Attarian, S. Muscle MRI of facioscapulohumeral dystrophy (FSHD): A growing demand and a promising approach. Rev. Neurol. (Paris). 172, 566–571 (2016).
Google Scholar
Ferguson, M. R. et al. Quantitative MRI reveals decelerated fatty infiltration in muscles of active FSHD patients. Neurology 87, 1746 (2016).
Google Scholar
Fionda, L. et al., Comparison of quantitative muscle ultrasound and whole-body muscle MRI in facioscapulohumeral muscular dystrophy type 1 patients. Neurol Sci (2023).
Mul, K. et al. Adding quantitative muscle MRI to the FSHD clinical trial toolbox. Neurology 89, 2057–2065 (2017).
Google Scholar
Tasca, G. et al. Upper girdle imaging in facioscapulohumeral muscular dystrophy. PLoS One. 9, e100292 (2014).
Google Scholar
Vincenten, S. C. C. et al. Five-year follow-up study on quantitative muscle magnetic resonance imaging in facioscapulohumeral muscular dystrophy: the link to clinical outcome. J. Cachexia Sarcopenia Muscle. 14, 1695–1706 (2023).
Google Scholar
Wong, C. J. et al. Regional and bilateral MRI and gene signatures in facioscapulohumeral dystrophy: implications for clinical trial design and mechanisms of disease progression. Hum. Mol. Genet. 33, 698–708 (2024).
Google Scholar
Wong, C. J. et al. Longitudinal measures of RNA expression and disease activity in FSHD muscle biopsies. Hum. Mol. Genet. 29, 1030–1043 (2020).
Google Scholar
Frisullo, G. et al. CD8(+) T cells in facioscapulohumeral muscular dystrophy patients with inflammatory features at muscle MRI. J. Clin. Immunol. 31, 155–166 (2011).
Google Scholar
Greco, A. et al. IL-6 and TNF are potential inflammatory biomarkers in facioscapulohumeral muscular dystrophy. J. Neuromuscul. Dis. 11, 327–347 (2024).
Google Scholar
Tasca, G. et al. Muscle Microdialysis to investigate inflammatory biomarkers in facioscapulohumeral muscular dystrophy. Mol. Neurobiol. 55, 2959–2966 (2018).
Google Scholar
Blaszczyk, E. et al. Progressive myocardial injury in myotonic dystrophy type II and facioscapulohumeral muscular dystrophy 1: a cardiovascular magnetic resonance follow-up study. J. Cardiovasc. Magn. Reson. 23, 130 (2021).
Google Scholar
Collins, G. S., Reitsma, J. B., Altman, D. G. & Moons, K. G. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. BMJ 350, g7594 (2015).
Google Scholar
