247th ENMC International Workshop: Muscle magnetic resonance imaging - Implementing muscle MRI as a diagnostic tool for rare genetic myopathy cohorts. Hoofddorp, The Netherlands, September 2019

Warman‐Chardon, Jodi; Díaz‐Manera, Jordi; Tasca, Giorgio; Straub, Volker

doi:10.1016/j.nmd.2020.08.360

Cited by 16 publications

(7 citation statements)

References 76 publications

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“…Muscle magnetic resonance imaging (MRI) represents the gold standard technique for a muscle imaging study, providing useful information for diagnostic purposes through the detection of disease‐specific patterns of muscle involvement in various neuromuscular conditions [ 8 , 9 ]. Furthermore, muscle MRI has been considered a biomarker of disease progression [ 10 , 11 , 12 ], and also a potential non‐invasive tool to better understand the pathophysiology of specific muscle diseases [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Muscle magnetic resonance imaging in myotonic dystrophy type 1 (DM1): Refining muscle involvement and implications for clinical trials

Garibaldi

Nicoletti

Bucci

et al. 2021

Euro J of Neurology

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Background Only a few studies have reported muscle imaging data on small cohorts of patients with myotonic dystrophy type 1 (DM1). We aimed to investigate the muscle involvement in a large cohort of patients in order to refine the pattern of muscle involvement, to better understand the pathophysiological mechanisms of muscle weakness, and to identify potential imaging biomarkers for disease activity and severity. Methods One hundred and thirty‐four DM1 patients underwent a cross‐sectional muscle magnetic resonance imaging (MRI) study. Short tau inversion recovery (STIR) and T1 sequences in the lower and upper body were analyzed. Fat replacement, muscle atrophy and STIR positivity were evaluated using three different scales. Correlations between MRI scores, clinical features and genetic background were investigated. Results The most frequent pattern of muscle involvement in T1 consisted of fat replacement of the tongue, sternocleidomastoideus, paraspinalis, gluteus minimus, distal quadriceps and gastrocnemius medialis. Degree of fat replacement at MRI correlated with clinical severity and disease duration, but not with CTG expansion. Fat replacement was also detected in milder/asymptomatic patients. More than 80% of patients had STIR‐positive signals in muscles. Most DM1 patients also showed a variable degree of muscle atrophy regardless of MRI signs of fat replacement. A subset of patients (20%) showed a ‘marbled’ muscle appearance. Conclusions Muscle MRI is a sensitive biomarker of disease severity alsofor the milder spectrum of disease. STIR hyperintensity seems to precede fat replacement in T1. Beyond fat replacement, STIR positivity, muscle atrophy and a ‘marbled’ appearance suggest further mechanisms of muscle wasting and weakness in DM1, representing additional outcome measures and therapeutic targets for forthcoming clinical trials.

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Section: Introductionmentioning

confidence: 99%

Muscle magnetic resonance imaging in myotonic dystrophy type 1 (DM1): Refining muscle involvement and implications for clinical trials

Garibaldi

Nicoletti

Bucci

et al. 2021

Euro J of Neurology

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“…Muscle MRI has been developed as an essential tool for diagnosing myopathies because it can be used either for global assessment of each muscle or for compositional assessment noninvasively to assess the characteristic radiological patterns ( 8 , 10 ). Traditionally, T1-weighted sequence and STIR sequence were used for muscle fatty infiltration and edema in the MRI evaluation of myopathy ( 29 , 30 ). Previous studies on using the Mercuri scale based on the T1-weighted sequence to identify the different patterns of myopathies suggested that this visual scoring could be advantageous in conveniently applied in clinical practice, especially in primary care hospitals ( 8 , 31 ).…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based radiomics to differentiate immune-mediated necrotizing myopathy from limb-girdle muscular dystrophy R2 using MRI

Wei,

Zhong,

Xie

et al. 2023

Front. Neurol.

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ObjectivesThis study aimed to assess the feasibility of a machine learning-based radiomics tools to discriminate between Limb-girdle muscular dystrophy R2 (LGMDR2) and immune-mediated necrotizing myopathy (IMNM) using lower-limb muscle magnetic resonance imaging (MRI) examination.MethodsAfter institutional review board approval, 30 patients with genetically proven LGMDR2 (12 females; age, 34.0 ± 11.3) and 45 patients with IMNM (28 females; age, 49.2 ± 16.6) who underwent lower-limb MRI examination including T1-weighted and interactive decomposition water and fat with echos asymmetric and least-squares estimation (IDEAL) sequences between July 2014 and August 2022 were included. Radiomics features of muscles were obtained, and four machine learning algorithms were conducted to select the optimal radiomics classifier for differential diagnosis. This selected algorithm was performed to construct the T1-weighted (TM), water-only (WM), or the combined model (CM) for calf-only, thigh-only, or the calf and thigh MR images, respectively. And their diagnostic performance was studied using area under the curve (AUC) and compared to the semi-quantitative model constructed by the modified Mercuri scale of calf and thigh muscles scored by two radiologists specialized in musculoskeletal imaging.ResultsThe logistic regression (LR) model was the optimal radiomics model. The performance of the WM and CM for thigh-only images (AUC 0.893, 0.913) was better than those for calf-only images (AUC 0.846, 0.880) except the TM. For “calf + thigh” images, the TM, WM, and CM models always performed best (AUC 0.953, 0.907, 0.953) with excellent accuracy (92.0, 84.0, 88.0%). The AUCs of the Mercuri model of the calf, thigh, and “calf + thigh” images were 0.847, 0.900, and 0.953 with accuracy (84.0, 84.0, 88.0%).ConclusionMachine learning-based radiomics models can differentiate LGMDR2 from IMNM, performing better than visual assessment. The model built by combining calf and thigh images presents excellent diagnostic efficiency.

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“…The high signal intensity in T1‐weighted images suggests an increase of intramuscular adipose tissue in affected muscles, contrasting with normal signal intensity in other muscle groups. Genetic myopathies frequently disclose specific patterns of muscle involvement detected on muscle MRI imaging (Warman‐Chardon et al, 2020)…”

Section: Case Reportmentioning

confidence: 99%

Ehlers‐Danlos/myopathy overlap syndrome caused by a large de novo deletion in COL12A1

Coppens

Desmyter

Koch

et al. 2022

American J of Med Genetics Pt A

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Autosomal dominant and recessive mutations in COL12A1 cause the Ehlers‐Danlos/myopathy overlap syndrome. Here, we describe a boy with fetal hypokinesia, severe neonatal weakness, striking hyperlaxity, high arched palate, retrognathia, club feet, and pectus excavatum. His motor development was initially delayed but muscle strength improved with time while hyperlaxity remained very severe causing recurrent joint dislocations. Using trio exome sequencing and a copy number variation (CNV) analysis tool, we identified an in‐frame de novo heterozygous deletion of the exons 45 to 54 in the COL12A1 gene. Collagen XII immunostaining on cultured skin fibroblasts demonstrated intracellular retention of collagen XII, supporting the pathogenicity of the deletion. The phenotype of our patient is slightly more severe than other cases with dominantly acting mutations, notably with the presence of fetal hypokinesia. This case highlights the importance of CNVs analysis in the COL12A1 gene in patients with a phenotype suggesting Ehlers‐Danlos/myopathy overlap syndrome.

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247th ENMC International Workshop: Muscle magnetic resonance imaging - Implementing muscle MRI as a diagnostic tool for rare genetic myopathy cohorts. Hoofddorp, The Netherlands, September 2019

Cited by 16 publications

References 76 publications

Muscle magnetic resonance imaging in myotonic dystrophy type 1 (DM1): Refining muscle involvement and implications for clinical trials

Muscle magnetic resonance imaging in myotonic dystrophy type 1 (DM1): Refining muscle involvement and implications for clinical trials

Machine learning-based radiomics to differentiate immune-mediated necrotizing myopathy from limb-girdle muscular dystrophy R2 using MRI

Ehlers‐Danlos/myopathy overlap syndrome caused by a large de novo deletion in COL12A1

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