Quantitative Sonographic Assessment of the Quadriceps Femoris Muscle in Healthy Japanese Adults

Watanabe, Tsuneo; Murakami, Hiroki; Fukuoka, Daisuke; Terabayashi, Nobuo; Shin, Sohee; Yabumoto, Tamotsu; Ito, Hiroyasu; Fujita, Hiroshi; Matsuoka, Takeshi; Seishima, Mitsuru

doi:10.7863/ultra.16.07054

Cited by 18 publications

(28 citation statements)

References 29 publications

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“…To reflect both the quantitative and qualitative changes, skeletal muscles in the elderly show characteristic imaging features. Recently, Watanabe and colleagues reported correlations between physical characteristics (age, height, weight, body mass index, muscle strength) and several texture features (mean, skewness, kurtosis, inverse difference moment, angular second moment, and sum of entropy) in healthy adults (12). Our study is in line with the study by Watanabe and colleagues to show age-dependency in some of the texture features.…”

Section: Texture Features and Agesupporting

confidence: 92%

“…However, before reliably using muscle texture features in any clinical settings, these should be carefully assessed in the confounding factors, such as gender and aging. Watanabe and colleagues recently showed that there was indeed aging effects in the muscle texture features, although they analyzed only 6 texture features (12). Our study is in agreement with the study by Watanabe and colleagues and further extended the classes of texture features.…”

Section: Clinical Indicationsupporting

confidence: 92%

“…Given the recent advent of radiomics that utilizes quantitative imaging features including texture features in oncology and related fields (5,13), there would be little doubt that this methodology of quantitative imaging analysis can be applicable to neuromuscular ultrasound. Indeed, there have been several studies to assess the texture characteristics of skeletal muscles ( 6,12,14,8,(15)(16)(17). Among these, texture analysis of muscle ultrasound was reported to be useful in identifying the underlying etiology (i.e., myogenic vs. neurogenic in nature) (6) and in being potentially utilized as a disease biomarker, such as in amyotrophic lateral sclerosis (17).…”

Section: Clinical Indicationmentioning

confidence: 99%

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Age‐dependent texture features in skeletal muscle ultrasonography

et al. 2018

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Texture analysis characterizes regions in an image by their texture content and has been utilized to infer the underlying structures of medical images such as skeletal muscles. Although potentially useful in tissue diagnosis and assessing disease progression of neuromuscular diseases, the use of texture analysis in such purposes are limited, due to lack of information such as effects of aging. Thus, we performed texture analysis of medial gastrocnemius in healthy individuals form their 20s to late 80s. Among the 283 texture features in 6 classes, the features related to histogram, co-occurrence matrix, absolute gradient, and wavelet were correlated to age in 17-40% % of the parameters, while none of the features related to run-length matrix and autoregressive model had significant correlation to age. This study showed that age-dependency in many texture features are present and need to be taken into account in elucidating the clinical significance. By contrast, the features related to runlength matrix and autoregressive model could have clinical utility. J. Med. Invest. 65 : 274-279, August, 2018

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Section: Texture Features and Agesupporting

confidence: 92%

Section: Clinical Indicationsupporting

confidence: 92%

Section: Clinical Indicationmentioning

confidence: 99%

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Age‐dependent texture features in skeletal muscle ultrasonography

et al. 2018

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“…Another concern is that EI is affected by the thickness of tissues and by the US scanner settings. 64 Contrary to a conventional first-order statistical feature like EI (histogram analysis), second order or higher order texture analyses have little effect on change by gain or frequency and intensity invariant. 64,65 Texture analysis provides an objective quantitative assessment of tissue heterogeneity by analyzing the distribution and relationship of pixel gray levels in the image, 66 so its application can effectively overcome these limitations.…”

Section: Echo Intensitymentioning

confidence: 99%

“…64 Contrary to a conventional first-order statistical feature like EI (histogram analysis), second order or higher order texture analyses have little effect on change by gain or frequency and intensity invariant. 64,65 Texture analysis provides an objective quantitative assessment of tissue heterogeneity by analyzing the distribution and relationship of pixel gray levels in the image, 66 so its application can effectively overcome these limitations. Texture analysis of muscle recently enabled differentiation between normal and pathologic muscles, suggesting that texture analysis may be a promising tool for differentiation of various muscle diseases.…”

Section: Echo Intensitymentioning

confidence: 99%

Ultrasound Biomarkers for Sarcopenia: What Can We Tell So Far?

Mombiela

Vucetic²,

Rossi

et al. 2020

Semin Musculoskelet Radiol

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Sarcopenia is a disease characterized by decreased skeletal muscle mass, strength, and loss of function that can impair quality of life and increase physical disability, adverse metabolic effects, and mortality. This review familiarizes the reader with ultrasound (US)-based biomarkers for sarcopenia with a discussion of these topics: why assessing sarcopenia is relevant for radiologists, how to evaluate muscle structure with US, the current challenges or pitfalls of these biomarkers, and the implications of all the available evidence. It summarizes the most up-to-date literature on the pathophysiology behind the use of these US-based biomarkers, the biomarkers themselves, and a protocol used for their assessments. The article also describes representative examples that reflect modern practice in the field. Imaging is routinely used in clinical practice, and radiologists can play an important role in the evaluation of geriatric patients, providing an unprecedented opportunity to improve decision support in sarcopenia prevention and treatment.

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Altered features of body composition in older adults with type 2 diabetes and prediabetes compared with matched controls

Bell

Paris

Avrutin

et al. 2022

J cachexia sarcopenia muscle

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Background Ageing is accompanied by muscle loss and fat gain, which may elevate the risk of type 2 diabetes (T2D). However, there is a paucity of data on the distribution of regional lean and fat tissue in older adults with T2D or prediabetes compared with healthy controls. The objective of this study was to compare regional body composition [by dual‐energy x‐ray absorptiometry (DXA)], muscle and subcutaneous adipose tissue (SAT) thicknesses (by ultrasound), and ultrasound‐based muscle texture features in older adults with T2D or prediabetes compared with normoglycaemic controls. Methods Eighteen adults > 60 years with T2D or prediabetes (T2D group) were individually matched to normoglycaemic participants [healthy matched (HM) group] for age (±5 years), sex, and body fat (±2.5%). In a single study visit, all participants received a whole‐body DXA scan and ultrasound assessment of the abdomen and anterior thigh. At these two landmarks, we used ultrasound to measure muscle and SAT thickness, as well as texture features of the rectus femoris and rectus abdominis. We also conducted an exploratory subanalysis on a subset of participants (n = 14/18 in the T2D group and n = 10/18 in the HM group) who underwent additional assessments including strength testing of the knee extensors (using a Biodex dynamometer), and a fasting blood sample for the measurement of circulating markers of glucose metabolism [glucose, insulin, c‐peptide, and the homoeostatic model assessment of insulin resistance (HOMA‐IR)]. Results The T2D group was 72 ± 8 years old (mean ± SD), predominantly male (n = 15/18; 83%), and overweight (BMI: 27.8 ± 4.2 kg/m2, 33.2 ± 5.3% body fat). DXA‐derived upper arm lean mass was 0.4 kg greater (P = 0.034), and leg fat mass was 1.4 kg lower (P = 0.048), in the T2D vs. HM group. Ultrasound‐based texture features were distinct between the groups [rectus abdominis blob size: 0.07 ± 0.06 vs. 0.30 ± 0.43 cm2, P = 0.045; rectus femoris local binary pattern (LBP) entropy: 4.65 ± 0.05 vs. 4.59 ± 0.08 A.U., P = 0.007]. When all participants who underwent additional assessments were pooled (n = 24), we observed that certain ultrasound‐based muscle texture features correlated significantly with muscle strength (rectus abdominis histogram skew vs. power during an isokinetic contraction at 60°/s: r = 0.601, P = 0.003) and insulin resistance (rectus femoris LBP entropy vs. HOMA‐IR: r = 0.419, P = 0.042). Conclusions Our findings suggest a novel body composition phenotype specific to older adults with T2D or prediabetes. We are also the first to report that ultrasound‐based texture features correspond with functional outcomes. Future larger scale studies are needed to uncover the mechanisms underpinning these regional body composition differences.

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Quantitative Sonographic Assessment of the Quadriceps Femoris Muscle in Healthy Japanese Adults

Abstract: Sonography has the capacity to quantitatively assess muscular morphologic changes due to aging and could be a valuable tool for early detection of musculoskeletal disorders.

Cited by 18 publications

References 29 publications

Age‐dependent texture features in skeletal muscle ultrasonography

Age‐dependent texture features in skeletal muscle ultrasonography

Ultrasound Biomarkers for Sarcopenia: What Can We Tell So Far?

Altered features of body composition in older adults with type 2 diabetes and prediabetes compared with matched controls

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