Evidence map›Paper›PMID 41121368›Full record

ArticleJournal of orthopaedic surgery and research2025

Quantitative evaluation of muscle quality and quantity using an ultrasound radio frequency signal.

Lan Zeng, Li Zhang, Jinhua Shao, Jianwen Luo, Qiong He, Yang Sun

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Article in Journal of orthopaedic surgery and research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Lan Zeng *Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Haidian District, Beijing, 100191, China.
Li Zhang *Department of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Haidian District, Beijing, 100191, China.
Jinhua ShaoWuxi Hisky Medical Technologies Co., Ltd, Beijing, China.
Jianwen LuoSchool of Biomedical Engineering, Tsinghua University, Beijing, China.
Qiong HeWuxi Hisky Medical Technologies Co., Ltd, Beijing, China. heqiong@hiskymedical.com.
Yang SunDepartment of Ultrasound, Peking University Third Hospital, 49 North Garden Rd., Haidian District, Beijing, 100191, China. sunyang_bysy@126.com.

Funding

National Natural Science Foundation of China 82102071
6 · The paper itself

Abstract

backgroundWe aimed to investigate the utility of quantitative ultrasonographic (QUS) parameters derived from radio frequency (RF) signals for the assessment of muscle quality and quantity.

methodsA total of 200 hospitalized patients (102 men, 98 women; mean age: 59.11 ± 27.58 years) were prospectively enrolled in the study. Their skeletal muscle density (SMD) and skeletal muscle index (SMI) were calculated from computed tomography images, as the gold-standard methods of evaluating muscle quality and quantity, respectively. At the L3 vertebral level, the paraspinal muscle (PSM) thickness was measured by ultrasound, and the RF signals of the PSM were recorded, from which QUS parameters were obtained. Univariate and multivariate logistic regression analyses were performed to identify independent predictors of SMD and SMI. Receiver operating characteristic curves were used to evaluate the predictive performance of combination of parameters for low SMD and low SMI.

resultsThe RF signals-based QUS parameters MusQBOX.NNM(C), MusQBOX.NNM(L), MusQBOX.Nα(C), and MusQBOX.NK(L) were identified as independent predictors of low SMD (all P < 0.05). The PSM thickness and the parameter MUSQBOX.NMI(L) were independent predictors of low SMI (both P < 0.05). The combination of MusQBOX.NNM(C), MusQBOX.NNM(L), MusQBOX.Nα(C), and MusQBOX.NK(L) had high diagnostic efficiency in the prediction of low SMD [area under the curve (AUC): 0.899 [95% confidence interval (CI): 0.852-0.945), P < 0.001]. In addition, the combination of PSM thickness and MUSQBOX.NMI(L) showed moderate efficacy for the prediction of low SMI [AUC: 0.779 (95% CI 0.714-0.836), P < 0.001].

conclusionsThe RF signals-based QUS parameters derived from PSM are effective means of assessing both low SMD and low SMI and provide an alternative noninvasive method of evaluating muscle quality and quantity.

Indexed as

Muscle, SkeletalParaspinal MusclesRadio WavesAdultAgedFemaleHumansMaleMiddle AgedProspective StudiesUltrasonographyQuantitative evaluationSarcopeniaSkeletal muscleUltrasound

Identifiers

PMID41121368
PMCPMC12538758

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.