MRI of skeletal muscles in patients with idiopathic inflammatory myopathies: characteristic findings and diagnostic performance in dermatomyositis

Ukichi, Taro; Yoshida, Ken; Matsushima, Satoshi; Kawakami, Go; Noda, Kentaro; Furuya, Keizo; Kurosaka, Daitaro

doi:10.1136/rmdopen-2018-000850

Cited by 45 publications

(50 citation statements)

References 33 publications

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“…In patients with established diagnosis MRI can help to determine whether clinical muscle weakness is based on chronic muscle damage or relapse of active disease [157] . The characteristic MRI findings in patients with DM are high signal intensities in subcutaneous tissue and fascial areas, peripheral distribution and honeycomb pattern [158] . There is no standardized protocol for evaluation of MRI in patients with myositis.…”

Section: Treatment In Dmmentioning

confidence: 99%

239th ENMC International Workshop: Classification of dermatomyositis, Amsterdam, the Netherlands, 14–16 December 2018

Mammen¹,

Allenbach

Stenzel

et al. 2020

Neuromuscular Disorders

187

155

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Section: Treatment In Dmmentioning

confidence: 99%

239th ENMC International Workshop: Classification of dermatomyositis, Amsterdam, the Netherlands, 14–16 December 2018

Mammen¹,

Allenbach

Stenzel

et al. 2020

Neuromuscular Disorders

187

155

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“…In the myositis group, highest MRI scores were observed for muscle fatty replacement, followed by edema and CM uptake ( Figure 3 ). Gadolinium enhancement is reported to be mainly present in subcutaneous and fascial areas in inflammatory myopathies 5 as a form of myofasciitis. Our findings support the notion that edema and muscle fatty replacement are key findings in muscle MRI of patients with suspected myopathies.…”

Section: Discussionmentioning

confidence: 99%

“… 5 A specific MRI pattern has already been established for single neuromuscular disorders, for example, idiopathic inflammatory myopathies (IIMs), including dermatomyositis, polymyositis, and inclusion body myositis. 5 , 6 Muscle MRI is also helpful in genetically determined myopathies like facioscapulohumeral dystrophy (FSHD) 7 and other muscular dystrophies, 8 showing a specific pattern of affected muscles based on fat replacement recognizable on MRI. However, many of these patterns are overlapping 9 and critical to interpretation.…”

Section: Introductionmentioning

confidence: 99%

The value of qualitative muscle MRI in the diagnostic procedures of myopathies: a biopsy-controlled study in 191 patients

Urban

Mohamed

Ludolph

et al. 2021

Ther Adv Neurol Disord

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Background and aims: The role of muscle magnetic resonance imaging (MRI) in the diagnostic procedures of myopathies is still controversially discussed. The current study was designed to analyze the status of qualitative muscle MRI, electromyography (EMG), and muscle biopsy in different cases of clinically suspected myopathy. Methods: A total of 191 patients (male: n = 112, female: n = 79) with suspected myopathy who all received muscle MRI, EMG, and muscle biopsy for diagnostic reasons were studied, with the same location of biopsy and muscle MRI (either upper or lower extremities or paravertebral muscles). Muscle MRIs were analyzed using standard rating protocols by two different raters independently. Results: Diagnostic findings according to biopsy results and genetic testing were as follow: non-inflammatory myopathy: n = 65, inflammatory myopathy (myositis): n = 51, neurogenic: n = 18, unspecific: n = 23, and normal: n = 34. The majority of patients showed myopathic changes in the EMG. Edema, atrophy, muscle fatty replacement, and contrast medium enhancement (CM uptake) in MRI were observed across all final diagnostic groups. Only 30% of patients from the myositis group ( n = 15) showed CM uptake. Discussion and conclusion: The study provides guidance in the definition of the impact of muscle MRI in suspected myopathy: despite being an important diagnostic tool, qualitative MRI findings could not distinguish different types of neuromuscular diagnostic groups in comparison with the gold standard histopathologic diagnosis and/or genetic testing. The results suggest that neither muscle edema nor gadolinium enhancement are able to secure a diagnosis of myositis. The current results do not support qualitative MRI as aiding in the diagnostic distinction of various myopathies. Quantitative muscle MRI is, however, useful in the diagnostic procedure of a suspected neuromuscular disease, especially with regard to assessing progression of a chronic myopathy by quantification of the degree of atrophy and fatty replacement and in exploring patterns of muscle group involvements in certain genetic myopathies.

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“…The proximal legs are preferentially examined because thigh muscles are mostly affected in IIM patients 12 . Although previous studies reported characteristic muscle MRI findings in IIM patients 11 – 16 , quantitative or semi-quantitative assessments with MRI have been limited 12 .…”

Section: Introductionmentioning

confidence: 98%

Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies

Nagawa

Suzuki

Yamamoto

et al. 2021

Sci Rep

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To develop a machine learning (ML) model that predicts disease groups or autoantibodies in patients with idiopathic inflammatory myopathies (IIMs) using muscle MRI radiomics features. Twenty-two patients with dermatomyositis (DM), 14 with amyopathic dermatomyositis (ADM), 19 with polymyositis (PM) and 19 with non-IIM were enrolled. Using 2D manual segmentation, 93 original features as well as 93 local binary pattern (LBP) features were extracted from MRI (short-tau inversion recovery [STIR] imaging) of proximal limb muscles. To construct and compare ML models that predict disease groups using each set of features, dimensional reductions were performed using a reproducibility analysis by inter-reader and intra-reader correlation coefficients, collinearity analysis, and the sequential feature selection (SFS) algorithm. Models were created using the linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machine (SVM), k-nearest neighbors (k-NN), random forest (RF) and multi-layer perceptron (MLP) classifiers, and validated using tenfold cross-validation repeated 100 times. We also investigated whether it was possible to construct models predicting autoantibody status. Our ML-based MRI radiomics models showed the potential to distinguish between PM, DM, and ADM. Models using LBP features provided better results, with macro-average AUC values of 0.767 and 0.714, accuracy of 61.2 and 61.4%, and macro-average recall of 61.9 and 59.8%, in the LDA and k-NN classifiers, respectively. In contrast, the accuracies of radiomics models distinguishing between non-IIM and IIM disease groups were low. A subgroup analysis showed that classification models for anti-Jo-1 and anti-ARS antibodies provided AUC values of 0.646–0.853 and 0.692–0.792, with accuracy of 71.5–81.0 and 65.8–78.3%, respectively. ML-based TA of muscle MRI may be used to predict disease groups or the autoantibody status in patients with IIM and is useful in non-invasive assessments of disease mechanisms.

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MRI of skeletal muscles in patients with idiopathic inflammatory myopathies: characteristic findings and diagnostic performance in dermatomyositis

Cited by 45 publications

References 33 publications

239th ENMC International Workshop: Classification of dermatomyositis, Amsterdam, the Netherlands, 14–16 December 2018

239th ENMC International Workshop: Classification of dermatomyositis, Amsterdam, the Netherlands, 14–16 December 2018

The value of qualitative muscle MRI in the diagnostic procedures of myopathies: a biopsy-controlled study in 191 patients

Texture analysis of muscle MRI: machine learning-based classifications in idiopathic inflammatory myopathies

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