Quantitative muscle ultrasound in Duchenne muscular dystrophy: A comparison of techniques

Abstract Background: Nowadays, it needs favorable biomarkers to follow up the disease progression and therapeutic responses of Duchenne muscular dystrophy (DMD). This study evaluates which one of Quantitative muscle ultrasound (QMUS) and magnetic resonance imaging (MRI) is suitable for the disease in China. Methods: Thirty-six boys with DMD engaged in the longitudinal observational cohort study, who used prednisone from baseline to 12th month. Muscle thickness (MT) and echo intensity (EI) of QMUS and T1-weighted MRI grading were measured in the right quadriceps femoris of the boys with DMD. Results: The scores of MT and EI of QMUS and T1-weighted MRI grading showed significant correlations with the clinical ones of muscle strength, timed testing, and quality of life. The scores of MT and EI of QMUS showed good correlations with the ones of T1-weighted MRI grading too (P<0.05). But 15 of 36 boys with DMD did not take MRI examinations for different reasons. Conclusions: QMUS and MRI can use as biomarkers for tracking DMD. Nevertheless, QMUS, because of its practical, low cost, and patient-friendly, applies for DMD widely than MRI in China. Keywords: Ultrasonography, Magnetic resonance imaging, Duchenne muscular dystrophy, Child

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Quantitative muscle ultrasound is a promising longitudinal follow-up tool in Duchenne muscular dystrophy

Neuromuscular Disorders ◽

10.1016/j.nmd.2011.10.020 ◽

2012 ◽

Vol 22 (4) ◽

pp. 306-317 ◽

Cited By ~ 85

Author(s):

Merel Jansen ◽

Nens van Alfen ◽

Maria W.G. Nijhuis van der Sanden ◽

Johannes P. van Dijk ◽

Sigrid Pillen ◽

...

Keyword(s):

Duchenne Muscular Dystrophy ◽

Muscular Dystrophy ◽

Muscle Ultrasound

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Muscle ultrasound in Duchenne muscular dystrophy: Useful tool to monitor progression of early disease

Neuromuscular Disorders ◽

10.1016/j.nmd.2015.06.051 ◽

2015 ◽

Vol 25 ◽

pp. S197

Author(s):

P. Karachunski ◽

J. Dalton ◽

J. Marsh

Keyword(s):

Duchenne Muscular Dystrophy ◽

Muscular Dystrophy ◽

Early Disease ◽

Muscle Ultrasound

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Muscle ultrasound elastography and MRI in preschool children with Duchenne muscular dystrophy

Neuromuscular Disorders ◽

10.1016/j.nmd.2018.02.007 ◽

2018 ◽

Vol 28 (6) ◽

pp. 476-483 ◽

Cited By ~ 14

Author(s):

Anna Pichiecchio ◽

Francesco Alessandrino ◽

Chandra Bortolotto ◽

Alessandra Cerica ◽

Cristina Rosti ◽

...

Keyword(s):

Preschool Children ◽

Duchenne Muscular Dystrophy ◽

Muscular Dystrophy ◽

Ultrasound Elastography ◽

Muscle Ultrasound

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Qualitative and quantitative muscle ultrasound in patients with Duchenne muscular dystrophy: Where do sonographic changes begin?

European Journal of Paediatric Neurology ◽

10.1016/j.ejpn.2020.06.001 ◽

2020 ◽

Vol 28 ◽

pp. 142-150

Author(s):

K. Vill ◽

M. Sehri ◽

C. Müller ◽

I. Hannibal ◽

V. Huf ◽

...

Keyword(s):

Duchenne Muscular Dystrophy ◽

Muscular Dystrophy ◽

Qualitative And Quantitative ◽

Muscle Ultrasound

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Deep Learning of Ultrasound Imaging for Evaluating Ambulatory Function of Individuals with Duchenne Muscular Dystrophy

Diagnostics ◽

10.3390/diagnostics11060963 ◽

2021 ◽

Vol 11 (6) ◽

pp. 963

Author(s):

Ai-Ho Liao ◽

Jheng-Ru Chen ◽

Shi-Hong Liu ◽

Chun-Hao Lu ◽

Chia-Wei Lin ◽

...

Keyword(s):

Deep Learning ◽

Duchenne Muscular Dystrophy ◽

Muscular Dystrophy ◽

Ultrasound Imaging ◽

Roc Curve ◽

Confusion Matrix ◽

Automatic Generation ◽

Classification Performance ◽

Fine Tuning ◽

Muscle Ultrasound

Duchenne muscular dystrophy (DMD) results in loss of ambulation and premature death. Ultrasound provides real-time, safe, and cost-effective routine examinations. Deep learning allows the automatic generation of useful features for classification. This study utilized deep learning of ultrasound imaging for classifying patients with DMD based on their ambulatory function. A total of 85 individuals (including ambulatory and nonambulatory subjects) underwent ultrasound examinations of the gastrocnemius for deep learning of image data using LeNet, AlexNet, VGG-16, VGG-16TL, VGG-19, and VGG-19TL models (the notation TL indicates fine-tuning pretrained models). Gradient-weighted class activation mapping (Grad-CAM) was used to visualize features recognized by the models. The classification performance was evaluated using the confusion matrix and receiver operating characteristic (ROC) curve analysis. The results show that each deep learning model endows muscle ultrasound imaging with the ability to enable DMD evaluations. The Grad-CAMs indicated that boundary visibility, muscular texture clarity, and posterior shadowing are relevant sonographic features recognized by the models for evaluating ambulatory function. Of the proposed models, VGG-19 provided satisfying classification performance (the area under the ROC curve: 0.98; accuracy: 94.18%) and feature recognition in terms of physical characteristics. Deep learning of muscle ultrasound is a potential strategy for DMD characterization.

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Ambulatory Duchenne Muscular Dystrophy Children: Cross-sectional Correlation between Function, Quantitative Muscle Ultrasound and MRI

10.1101/2021.08.17.21262119 ◽

2021 ◽

Author(s):

Hala Abdulhady ◽

Hossam M. Sakr ◽

Nermine S. Elsayed ◽

Tamer A. El-Sobky ◽

Nagia Fahmy ◽

...

Keyword(s):

Duchenne Muscular Dystrophy ◽

Muscular Dystrophy ◽

Functional Assessment ◽

Significant Positive Correlation ◽

Significant Negative Correlation ◽

Fat Content ◽

Assessment Tools ◽

Muscle Mri ◽

Cross Sectional ◽

Muscle Ultrasound

Introduction/Aims: Duchenne muscular dystrophy (DMD) is a progressive genetic muscle disease. Quantitative muscle ultrasound (MUS), muscle MRI, and functional tools are important to delineate characteristics of muscle involvement. We aimed to establish correlations between clinical/functional and above-named imaging tools respecting their diagnostic and prognostic role in DMD children. Methods: A Prognostic cross-sectional retrospective study of 27 steroid-naive, ambulant male children/adolescents with genetically-confirmed DMD (mean age, 8.8 +/- 3.3 years). Functional performance was assessed using motor function measure (MFM) which assess standing/transfer (D1), proximal (D2) and distal (D3) motor function. And six-minute-walk test (6MWT). Imaging evaluation included quantitative muscle MRI which measured muscle fat content in a specific location of right rectus femoris by mDixon sequence. Quantitative MUS measured muscle brightness in standardized US image as an indicator of muscle fat content. Results: We found a highly significant positive correlation between the mean MFM total score and 6MWT (R=0.537, P=0.007). And a highly significant negative correlation between fat content by MUS and MFM total score (R=-0.603, P=0.006) and its D1 subscore (R=-0.712, P=0.001). And a significant negative correlation between fat content by US and 6MWT (R=-0.529, P=0.02). And a significant positive correlation between muscle fat content by mDixon MRI and patient's age (R=0.617, P=0.01). Discussion: Quantitative MUS correlates significantly with clinical/functional assessment tools as MFM and 6MWT, and augments their role in disease-tracking of DMD. Quantitative MUS has the potential to act as a substitute to functional assessment tools. The role for quantitative muscle MRI in disease-tracking should be further explored after elimination of confounding factors.

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