Dataset Information

Quantitative ultrasound of trapezius muscle involvement in myofascial pain: comparison of clinical and healthy population using texture analysis.

ABSTRACT:

Purpose

Ultrasound is a non-invasive quantitative method to characterize sonographic textures of skeletal muscles. To date, there is no information available on the trapezius muscle. This study assessed the trapezius muscles of patients with myofascial pain compared with normal healthy participants.

Methods

The trapezius muscles of 15 healthy and 17 myofascial pain participants were assessed using B-mode ultrasound to obtain 120 images for healthy and 162 images from myofascial pain participants. Texture features such as blob area, count and local binary patterns (LBP) were calculated. Multi-feature classification and analysis were performed using principal component analysis (PCA) and MANOVA to determine whether there were statistical differences.

Results

We demonstrate the two principal components composed of a combination of LBP and blob parameters which explain 92.55% of the cumulative variance of our data set. In addition, blob characteristics were significantly different between healthy and myofascial pain participants.

Conclusion

Our study provides evidence that texture analysis techniques can differentiate between healthy and myofascial pain affected trapezius muscles. Further research is necessary to evaluate the nature of these differences.

SUBMITTER: Kumbhare D

PROVIDER: S-EPMC7010874 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

Quantitative ultrasound of trapezius muscle involvement in myofascial pain: comparison of clinical and healthy population using texture analysis.

Kumbhare Dinesh D Shaw Saurabh S Ahmed Sara S Noseworthy Michael D MD

Journal of ultrasound 20181109 1

<h4>Purpose</h4>Ultrasound is a non-invasive quantitative method to characterize sonographic textures of skeletal muscles. To date, there is no information available on the trapezius muscle. This study assessed the trapezius muscles of patients with myofascial pain compared with normal healthy participants.<h4>Methods</h4>The trapezius muscles of 15 healthy and 17 myofascial pain participants were assessed using B-mode ultrasound to obtain 120 images for healthy and 162 images from myofascial pa ...[more]

PMID: 30414083

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Project description:Myofascial pain syndrome, thought to be the main cause of neck pain and shoulder muscle tenderness in the working population, is characterized by myofascial trigger points (MTrPs). This study aimed to examine the immediate and short-term effect of the combination of two therapeutic techniques for improving neck pain and muscle tenderness in male patients with upper trapezius active MTrPs. This study was a pretest-posttest single-blinded randomized controlled trial. Sixty male subjects with mechanical neck pain due to upper trapezius active MTrPs were recruited and randomly allocated into group A, which received muscle energy technique (MET) and ischemic compression technique (ICT) along with conventional intervention; group B, which received all the interventions of group A except ICT; and group C, which received conventional treatment only. Baseline (Pr), immediate postintervention (Po), and 2-week follow-up (Fo) measurements were made for all variables. Pain intensity and pressure pain threshold (PPT) were assessed by a visual analog scale (VAS) and pressure threshold meter, respectively. All the three groups received their defined intervention plans only. Repeated-measures analysis of variance was used to perform intra- and intergroup analyses. Cohen's d test was used to assess the effect size of the applied interventions within the groups. The intergroup analysis revealed significant differences among groups A, B, and C in VAS and PPT at Po (VAS-Po: F = 13.88, p=0.0001; PPT-Po: F = 17.17, p=0.0001) and even after 2 weeks of follow-up (VAS-Fo: F = 222.35, p=0.0001; PPT-Fo: F = 147.70, p=0.0001). Cohen's d revealed a significant treatment effect size within all groups except group C (only significant for VAS-Po-VAS-Pr: mean difference = 1.33, p < 0.05, d = 1.09); however, it showed a maximum effect size in group A for its variables (VAS-Fo-VAS-Pr: mean difference = 5.27, p=0.01, d = 4.04; PPT-Fo-PPT-Pr: mean difference = 2.14, p < 0.01, d = 3.89). Combination therapies (MET plus ICT) showed immediate and short-term (2-week follow-up) improvements in neck pain and muscle tenderness in male patients with upper trapezius active MTrPs.

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Project description:AimCancer treatments with radiation present a challenging physical toll for patients, which can be justified by the potential reduction in cancerous tissue with treatment. However, there remain patients for whom treatments do not yield desired outcomes. Radiomics involves using biomedical images to determine imaging features which, when used in tandem with retrospective treatment outcomes, can train machine learning (ML) classifiers to create predictive models. In this study we investigated whether pre-treatment imaging features from index lymph node (LN) quantitative ultrasound (QUS) scans parametric maps of head & neck (H&N) cancer patients can provide predictive information about treatment outcomes.Methods72 H&N cancer patients with bulky metastatic LN involvement were recruited for study. Involved bulky neck nodes were scanned with ultrasound prior to the start of treatment for each patient. QUS parametric maps and related radiomics texture-based features were determined and used to train two ML classifiers (support vector machines (SVM) and k-nearest neighbour (k-NN)) for predictive modeling using retrospectively labelled binary treatment outcomes, as determined clinically 3-months after completion of treatment. Additionally, novel higher-order texture-of-texture (TOT) features were incorporated and evaluated in regards to improved predictive model performance.ResultsIt was found that a 7-feature multivariable model of QUS texture features using a support vector machine (SVM) classifier demonstrated 81% sensitivity, 76% specificity, 79% accuracy, 86% precision and an area under the curve (AUC) of 0.82 in separating responding from non-responding patients. All performance metrics improved after implementation of TOT features to 85% sensitivity, 80% specificity, 83% accuracy, 89% precision and AUC of 0.85. Similar trends were found with k-NN classifier.ConclusionBinary H&N cancer treatment outcomes can be predicted with QUS texture features acquired from index LNs. Prediction efficacy improved by implementing TOT features following methodology outlined in this work.

Dataset Information

Quantitative ultrasound of trapezius muscle involvement in myofascial pain: comparison of clinical and healthy population using texture analysis.

Purpose

Methods

Results

Conclusion

Publications

Quantitative ultrasound of trapezius muscle involvement in myofascial pain: comparison of clinical and healthy population using texture analysis.

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