Diagnostic value of muscle MRI in rare congenital myopathies and collagen related muscular dystrophies

Background: Muscle MRI protocols have been developed to assess muscle involvement in a wide variety of muscular dystrophies. Different muscular dystrophies can involve muscle groups in characteristic patterns. These patterns can be identified in muscle MRI in the form of fatty infiltration. Objective: This study was conducted to add the existing knowledge of muscle MRI in GNE myopathy and evaluate the correlation of muscular involvement with different gene mutations. Methods: The MRI scans of the 18 GNE patients were analyzed retrospectively. Cluster analysis was done for grouping the muscles and patients. Results: The four muscles with the highest fat infiltration were adductor magnus, tibialis anterior, semitendinosus, and semimembranosus. Furthermore, three clusters of muscle involvement were found, including cluster 1, typical muscle involvement indicating muscles with the highest infiltration: extensor digitorum longus, gracilis, biceps femoris, soleus, gastrocnemius medial, adductor longus, tibialis anterior, adductor magnus, semimembranosus, semitendinosus; cluster 2, less typical muscle involvement indicating muscles with intermediate fat infiltration, peroneus longus, gastrocnemius lateral, and minimal fat infiltration in most of the patients, i.e., tibialis posterior; and cluster 3, atypical muscle involvement with low-fat infiltration: rectus femoris, sartorius, vastus intermedius, vastus medialis, and vastus lateralis. Conclusions: This study found three clusters of muscle involvement and three groups of patients among GNE patients. Hamstring muscles and the anterior compartment of the lower leg were the muscles with the highest fat infiltration. Moreover, a weak genotype-muscle MRI association was found in which tibialis posterior was more involved in patients with the most frequent mutation, i.e., C.2228T > C (p.M743T) mutation; however, this finding may be related to longer disease duration.

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Early Findings in Neonatal Cases of RYR1–Related Congenital Myopathies

Frontiers in Neurology ◽

10.3389/fneur.2021.664618 ◽

2021 ◽

Vol 12 ◽

Author(s):

Eleonora Mauri ◽

Daniela Piga ◽

Alessandra Govoni ◽

Roberta Brusa ◽

Serena Pagliarani ◽

...

Keyword(s):

Ryanodine Receptor ◽

Brain Mri ◽

Fetal Brain ◽

Congenital Myopathy ◽

Receptor Type ◽

Congenital Myopathies ◽

Muscle Mri ◽

Next Generation Sequencing Ngs ◽

Generation Sequencing

Ryanodine receptor type 1-related congenital myopathies are the most represented subgroup among congenital myopathies (CMs), typically presenting a central core or multiminicore muscle histopathology and high clinical heterogeneity. We evaluated a cohort of patients affected with Ryanodine receptor type 1-related congenital myopathy (RYR1-RCM), focusing on four patients who showed a severe congenital phenotype and underwent a comprehensive characterization at few months of life. To date there are few reports on precocious instrumental assessment. In two out of the four patients, a muscle biopsy was performed in the first days of life (day 5 and 37, respectively) and electron microscopy was carried out in two patients detecting typical features of congenital myopathy. Two patients underwent brain MRI in the first months of life (15 days and 2 months, respectively), one also a fetal brain MRI. In three children electromyography was performed in the first week of life and neurogenic signs were excluded. Muscle MRI obtained within the first years of life showed a typical pattern of RYR1-CM. The diagnosis was confirmed through genetic analysis in three out of four cases using Next Generation Sequencing (NGS) panels. The development of a correct and rapid diagnosis is a priority and may lead to prompt medical management and helps optimize inclusion in future clinical trials.

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Update on congenital myopathies and congenital muscular dystrophies

Journal of the Neurological Sciences ◽

10.1016/j.jns.2021.117965 ◽

2021 ◽

Vol 429 ◽

pp. 117965

Author(s):

Haluk Topaloglu

Keyword(s):

Muscular Dystrophies ◽

Congenital Myopathies

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Congenital muscular dystrophies and unstructured congenital myopathies

Brain and Development ◽

10.1016/s0387-7604(80)80032-5 ◽

1980 ◽

Vol 2 (2) ◽

pp. 120-125 ◽

Cited By ~ 8

Author(s):

Hans-Gerd Lenard ◽

Hans-Hilmar Goebel

Keyword(s):

Muscular Dystrophies ◽

Congenital Myopathies

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117th ENMC Workshop: Ventilatory Support in Congenital Neuromuscular Disorders — Congenital Myopathies, Congenital Muscular Dystrophies, Congenital Myotonic Dystrophy and SMA (II) 4–6 April 2003, Naarden, The Netherlands

Neuromuscular Disorders ◽

10.1016/j.nmd.2003.09.003 ◽

2004 ◽

Vol 14 (1) ◽

pp. 56-69 ◽

Cited By ~ 91

Author(s):

Carina Wallgren-Pettersson ◽

Kate Bushby ◽

Uwe Mellies ◽

Anita Simonds

Keyword(s):

The Netherlands ◽

Myotonic Dystrophy ◽

Neuromuscular Disorders ◽

Ventilatory Support ◽

Muscular Dystrophies ◽

Congenital Myopathies ◽

Congenital Myotonic Dystrophy

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Abnormal Expression of Dysferlin in Blood Monocytes Supports Primary Dysferlinopathy in Patients Confirmed by Genetic Analyses

Frontiers in Neurology ◽

10.3389/fneur.2020.540098 ◽

2021 ◽

Vol 11 ◽

Author(s):

Huili Zhang ◽

Yaqin Li ◽

Qiusheng Cheng ◽

Xi Chen ◽

Qiuxia Yu ◽

...

Keyword(s):

Dna Sequencing ◽

Genomic Dna ◽

Mononuclear Cells ◽

Diagnostic Value ◽

Medial Gastrocnemius ◽

Chinese Patients ◽

Blood Monocytes ◽

Peripheral Blood Mononuclear ◽

Muscle Mri ◽

Mutational Spectrum

Objective: Dysferlin deficiency causes dysferlinopathy. This study aimed to expand the mutational spectrum of dysferlinopathies, to further study one case with diagnostic ambiguity, and to identify the diagnostic value of dysferlin expression in total peripheral blood mononuclear cells (PBMC).Methods: The clinical and molecular profiles of dysferlinopathies in eight Chinese patients were evaluated. We also conducted magnetic resonance imaging (6/8) and determined dysferlin protein expression in muscle (7/8) and PBMC (3/8).Results: Nine of the 13 DYSF mutations identified were novel. One patient was homozygous for the Gln111Ter mutation by genomic DNA sequencing but was found to be heterozygous by sequencing of cDNA from total PBMC. A daughter of this patient did not carry any Gln111Ter mutation. Abnormal muscle MRI with predominant involvement of the medial gastrocnemius and soleus muscle was observed in 5/6 patients. Dysferlin levels were significantly reduced (immunohistochemistry/immunoblot) or absent (immunohistochemistry) in muscle and total PBMC (26–39%) for most patients. Sarcoplasmic accumulation of dysferlin was detected in one patient.Conclusion: Genomic DNA sequencing detects frequent homozygous mutations, while fewer heterozygous mutations in cDNA are detected after posttranscription. Total PBMC may serve as an alternative to confirm diagnosis and to guide further testing in dysferlinopathies. Our results contribute to the mutational spectrum of dysferlinopathies.

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Accuracy of a machine learning muscle MRI-based tool for the diagnosis of muscular dystrophies

Neurology ◽

10.1212/wnl.0000000000009068 ◽

2020 ◽

Vol 94 (10) ◽

pp. e1094-e1102 ◽

Cited By ~ 8

Author(s):

José Verdú-Díaz ◽

Jorge Alonso-Pérez ◽

Claudia Nuñez-Peralta ◽

Giorgio Tasca ◽

John Vissing ◽

...

Keyword(s):

Machine Learning ◽

Correct Diagnosis ◽

Genetic Diagnosis ◽

Lower Limbs ◽

Supervised Machine Learning ◽

Muscular Dystrophies ◽

Muscle Mri ◽

Fatty Replacement ◽

Numeric Data ◽

The One

ObjectiveGenetic diagnosis of muscular dystrophies (MDs) has classically been guided by clinical presentation, muscle biopsy, and muscle MRI data. Muscle MRI suggests diagnosis based on the pattern of muscle fatty replacement. However, patterns overlap between different disorders and knowledge about disease-specific patterns is limited. Our aim was to develop a software-based tool that can recognize muscle MRI patterns and thus aid diagnosis of MDs.MethodsWe collected 976 pelvic and lower limbs T1-weighted muscle MRIs from 10 different MDs. Fatty replacement was quantified using Mercuri score and files containing the numeric data were generated. Random forest supervised machine learning was applied to develop a model useful to identify the correct diagnosis. Two thousand different models were generated and the one with highest accuracy was selected. A new set of 20 MRIs was used to test the accuracy of the model, and the results were compared with diagnoses proposed by 4 specialists in the field.ResultsA total of 976 lower limbs MRIs from 10 different MDs were used. The best model obtained had 95.7% accuracy, with 92.1% sensitivity and 99.4% specificity. When compared with experts on the field, the diagnostic accuracy of the model generated was significantly higher in a new set of 20 MRIs.ConclusionMachine learning can help doctors in the diagnosis of muscle dystrophies by analyzing patterns of muscle fatty replacement in muscle MRI. This tool can be helpful in daily clinics and in the interpretation of the results of next-generation sequencing tests.Classification of evidenceThis study provides Class II evidence that a muscle MRI-based artificial intelligence tool accurately diagnoses muscular dystrophies.

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