Harmen Reyngoudt1,2, Benjamin Marty3,4, Jean-Marc Boisserie3,4, Julien Le Louër3,4, Cedi Koumako3,4, Pierre-Yves Baudin5, Brenda Wong6,7, Tanya Stojkovic8, Anthony Béhin8, Teresa Gidaro9, Yves Allenbach10, Olivier Benveniste10, Laurent Servais9,11,12, Pierre G Carlier3,4. 1. NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France. h.reyngoudt@institut-myologie.org. 2. NMR Laboratory, CEA/DRF/IBFJ/MIRCen, Bâtiment Babinski, Groupe Hospitalier Pitié-Salpêtrière, 47-83 boulevard Vincent Auriol, 75651, Paris Cedex 13, France. h.reyngoudt@institut-myologie.org. 3. NMR Laboratory, Neuromuscular Investigation Center, Institute of Myology, Paris, France. 4. NMR Laboratory, CEA/DRF/IBFJ/MIRCen, Bâtiment Babinski, Groupe Hospitalier Pitié-Salpêtrière, 47-83 boulevard Vincent Auriol, 75651, Paris Cedex 13, France. 5. Consultants for Research in Imaging and Spectroscopy, Tournai, Belgium. 6. Department of Neurology, Cincinnati Children's Hospital Medical Center (CCHMC), Cincinnati, OH, USA. 7. Department of Pediatrics, University of Massachusetts Medical School, Worcester, MA, USA. 8. Neuromuscular Reference Center, Institute of Myology, Pitié-Salpêtrière Hospital (AP-HP), Paris, France. 9. I-Motion - Pediatric Clinical Trials Department, Trousseau Hospital (AP-HP), Paris, France. 10. Department of Internal Medicine and Clinical Immunology, University Pierre et Marie Curie, AP-HP, GH Pitié-Salpêtrière, Paris, France. 11. Department of Pediatrics, CHU, University of Liège, Liège, Belgium. 12. MDUK Oxford Neuromuscular Center, Department of Pediatrics, University of Oxford, Oxford, UK.
Abstract
OBJECTIVES: Magnetic resonance imaging (MRI) constitutes a powerful outcome measure in neuromuscular disorders, yet there is a broad diversity of approaches in data acquisition and analysis. Since each neuromuscular disease presents a specific pattern of muscle involvement, the recommended analysis is assumed to be the muscle-by-muscle approach. We, therefore, performed a comparative analysis of different segmentation approaches, including global muscle segmentation, to determine the best strategy for evaluating disease progression. METHODS: In 102 patients (21 immune-mediated necrotizing myopathy/IMNM, 21 inclusion body myositis/IBM, 10 GNE myopathy/GNEM, 19 Duchenne muscular dystrophy/DMD, 12 dysferlinopathy/DYSF, 7 limb-girdle muscular dystrophy/LGMD2I, 7 Pompe disease, 5 spinal muscular atrophy/SMA), two MRI scans were obtained at a 1-year interval in thighs and lower legs. Regions of interest (ROIs) were drawn in individual muscles, muscle groups, and the global muscle segment. Standardized response means (SRMs) were determined to assess sensitivity to change in fat fraction (ΔFat%) in individual muscles, muscle groups, weighted combinations of muscles and muscle groups, and in the global muscle segment. RESULTS: Global muscle segmentation gave high SRMs for ΔFat% in thigh and lower leg for IMNM, DYSF, LGMD2I, DMD, SMA, and Pompe disease, and only in lower leg for GNEM and thigh for IBM. CONCLUSIONS: Global muscle segment Fat% showed to be sensitive to change in most investigated neuromuscular disorders. As compared to individual muscle drawing, it is a faster and an easier approach to assess disease progression. The use of individual muscle ROIs, however, is still of interest for exploring selective muscle involvement. KEY POINTS: • MRI-based evaluation of fatty replacement in muscles is used as an outcome measure in the assessment of 1-year disease progression in 8 different neuromuscular diseases. • Different segmentation approaches, including global muscle segmentation, were evaluated for determining 1-year fat fraction changes in lower limb skeletal muscles. • Global muscle segment fat fraction has shown to be sensitive to change in lower leg and thigh in most of the investigated neuromuscular diseases.
OBJECTIVES: Magnetic resonance imaging (MRI) constitutes a powerful outcome measure in neuromuscular disorders, yet there is a broad diversity of approaches in data acquisition and analysis. Since each neuromuscular disease presents a specific pattern of muscle involvement, the recommended analysis is assumed to be the muscle-by-muscle approach. We, therefore, performed a comparative analysis of different segmentation approaches, including global muscle segmentation, to determine the best strategy for evaluating disease progression. METHODS: In 102 patients (21 immune-mediated necrotizing myopathy/IMNM, 21 inclusion body myositis/IBM, 10 GNEmyopathy/GNEM, 19 Duchenne muscular dystrophy/DMD, 12 dysferlinopathy/DYSF, 7 limb-girdle muscular dystrophy/LGMD2I, 7 Pompe disease, 5 spinal muscular atrophy/SMA), two MRI scans were obtained at a 1-year interval in thighs and lower legs. Regions of interest (ROIs) were drawn in individual muscles, muscle groups, and the global muscle segment. Standardized response means (SRMs) were determined to assess sensitivity to change in fat fraction (ΔFat%) in individual muscles, muscle groups, weighted combinations of muscles and muscle groups, and in the global muscle segment. RESULTS: Global muscle segmentation gave high SRMs for ΔFat% in thigh and lower leg for IMNM, DYSF, LGMD2I, DMD, SMA, and Pompe disease, and only in lower leg for GNEM and thigh for IBM. CONCLUSIONS: Global muscle segment Fat% showed to be sensitive to change in most investigated neuromuscular disorders. As compared to individual muscle drawing, it is a faster and an easier approach to assess disease progression. The use of individual muscle ROIs, however, is still of interest for exploring selective muscle involvement. KEY POINTS: • MRI-based evaluation of fatty replacement in muscles is used as an outcome measure in the assessment of 1-year disease progression in 8 different neuromuscular diseases. • Different segmentation approaches, including global muscle segmentation, were evaluated for determining 1-year fat fraction changes in lower limb skeletal muscles. • Global muscle segment fat fraction has shown to be sensitive to change in lower leg and thigh in most of the investigated neuromuscular diseases.
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