Update on muscle imaging in myositis

Tan, Ai Lyn; Di Matteo, Andrea; Wakefield, Richard J.; Biglands, John

doi:10.1097/bor.0000000000000975

Cited by 8 publications

(1 citation statement)

References 69 publications

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“…The signal intensity of MRI scans directly correlates with disease activity, making it a valuable tool for assessing disease activity, guiding treatment decisions, and selecting biopsy sites (12,13). Furthermore, by utilizing conventional sequences such as T1-weighted and FS T2W sequences, muscle MRI technology can qualitatively detect muscle signal changes, specifically those associated with fat infiltration and muscle edema (14). These findings provide insights into the pathological changes in DM/PM (15,16), revealing selective muscle involvement in FS T2W sequences and the characteristic appearance of high signals related to edema/inflammation, which serve as indicators of acute or subacute inflammatory myopathy (17).…”

Section: Introductionmentioning

confidence: 99%

Value of radiomics-based automatic grading of muscle edema in polymyositis/dermatomyositis based on MRI fat-suppressed T2-weighted images

Zhang,

Zou,

Tan

et al. 2024

Acta Radiol

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Background The precise and objective assessment of thigh muscle edema is pivotal in diagnosing and monitoring the treatment of dermatomyositis (DM) and polymyositis (PM). Purpose Radiomic features are extracted from fat-suppressed (FS) T2-weighted (T2W) magnetic resonance imaging (MRI) of thigh muscles to enable automatic grading of muscle edema in cases of polymyositis and dermatomyositis. Material and Methods A total of 241 MR images were analyzed and classified into five levels using the Stramare criteria. The correlation between muscle edema grading and T2-mapping values was assessed using Spearman’s correlation. The dataset was divided into a 7:3 ratio of training (168 samples) and testing (73 samples). Thigh muscle boundaries in FS T2W images were manually delineated with 3D-Slicer. Radiomics features were extracted using Python 3.7, applying Z-score normalization, Pearson correlation analysis, and recursive feature elimination for reduction. A Naive Bayes classifier was trained, and diagnostic performance was evaluated using receiver operating characteristic (ROC) curves and comparing sensitivity and specificity with senior doctors. Results A total of 1198 radiomics parameters were extracted and reduced to 18 features for Naive Bayes modeling. In the testing set, the model achieved an area under the ROC curve of 0.97, sensitivity of 0.85, specificity of 0.98, and accuracy of 0.91. The Naive Bayes classifier demonstrated grading performance comparable to senior doctors. A significant correlation (r = 0.82, P <0.05) was observed between Stramare edema grading and T2-mapping values. Conclusion The Naive Bayes model, utilizing radiomics features extracted from thigh FS T2W images, accurately assesses the severity of muscle edema in cases of PM/DM.

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Section: Introductionmentioning

confidence: 99%

Value of radiomics-based automatic grading of muscle edema in polymyositis/dermatomyositis based on MRI fat-suppressed T2-weighted images

Zhang,

Zou,

Tan

et al. 2024

Acta Radiol

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Non‐negligible ultrasonographic findings in sarcoid myositis: A case series and literature review

Lv,

Xu,

Han

et al. 2024

J of Clinical Ultrasound

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Sarcoid myositis is a rare and often debilitating extrapulmonary manifestation of sarcoidosis that can be difficult to recognize without a prior sarcoidosis diagnosis. Sarcoidosis with muscle nodules or masses as the first symptom is the least common form, occurring in approximately 0.5%–2.3% of cases. This article presents four middle‐aged female patients who initially sought medical attention for a lower limb mass. Ultrasound examinations revealed consistent characteristic changes indicative of myositis. All patients underwent ultrasound‐guided muscle biopsy and were diagnosed with sarcoidosis. Therefore, ultrasonography plays a pivotal role as the primary diagnostic tool for the early detection of sarcoid myositis.

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Can we differentiate patients with dysferlinopathies and inflammatory myopathies by ultrasound? A discriminant analysis study

Solorzano-Flores,

Soto-Fajardo,

Ángeles-Acuña

et al. 2024

Rheumatol Int

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Update on muscle imaging in myositis

Cited by 8 publications

References 69 publications

Value of radiomics-based automatic grading of muscle edema in polymyositis/dermatomyositis based on MRI fat-suppressed T2-weighted images

Value of radiomics-based automatic grading of muscle edema in polymyositis/dermatomyositis based on MRI fat-suppressed T2-weighted images

Non‐negligible ultrasonographic findings in sarcoid myositis: A case series and literature review

Can we differentiate patients with dysferlinopathies and inflammatory myopathies by ultrasound? A discriminant analysis study

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