Jan Yih-Kuen, Hung Isabella Yu-Ju, Cheung W Catherine
Department of Health and Kinesiology, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
Department of Nursing, Chung Hwa University of Medical Technology, Tainan 717, Taiwan.
Diagnostics (Basel). 2025 Feb 21;15(5):524. doi: 10.3390/diagnostics15050524.
The objective of this systematic review was to summarize the findings of texture analyses of musculoskeletal ultrasound images and synthesize the information to facilitate the use of texture analysis on assessing skeletal muscle quality in various pathophysiological conditions. Medline, PubMed, Scopus, Web of Science, and Cochrane databases were searched from their inception until January 2025 using the PRISMA Diagnostic Test Accuracy and was registered at PROSPERO CRD42025636613. Information related to patients, interventions, ultrasound settings, texture analyses, muscles, and findings were extracted. The quality of evidence was evaluated using QUADAS-2. A total of 38 studies using second-order and higher-order texture analysis met the criteria. The results indicated that no studies used an established reference standard (histopathology) to evaluate the accuracy of ultrasound texture analysis in diagnosing muscle quality. Alternative reference standards were compared, including various physiological, pathological, and pre-post intervention comparisons using over 200+ texture features of various muscles on diverse pathophysiological conditions. The findings of these included studies demonstrating that ultrasound texture analysis was able to discriminate changes in muscle quality using texture analysis between patients with pathological conditions and healthy conditions, including popular gray-level co-occurrence matrix (GLCM)-based contrast, correlation, energy, entropy, and homogeneity. Studies also demonstrated that texture analysis can discriminate muscle quality in various muscles under pathophysiological conditions although evidence is low because of bias in subject recruitment and lack of comparison with the established reference standard. This is the first systematic review of the use of texture analysis of musculoskeletal ultrasonography in assessing muscle quality in various muscles under diverse pathophysiological conditions.
本系统评价的目的是总结肌肉骨骼超声图像纹理分析的结果,并综合这些信息,以促进在各种病理生理条件下利用纹理分析评估骨骼肌质量。从数据库创建至2025年1月,检索了Medline、PubMed、Scopus、Web of Science和Cochrane数据库,采用PRISMA诊断试验准确性方法,并在PROSPERO注册,注册号为CRD42025636613。提取了与患者、干预措施、超声设置、纹理分析、肌肉和研究结果相关的信息。使用QUADAS-2评估证据质量。共有38项使用二阶及更高阶纹理分析的研究符合纳入标准。结果表明,尚无研究使用既定的参考标准(组织病理学)来评估超声纹理分析诊断肌肉质量的准确性。研究比较了替代参考标准,包括在不同病理生理条件下,对各种肌肉的200多个纹理特征进行各种生理、病理及干预前后的比较。这些研究结果表明,超声纹理分析能够利用纹理分析区分病理状态患者和健康状态患者之间的肌肉质量变化,包括基于灰度共生矩阵(GLCM)的对比度、相关性、能量、熵和同质性。研究还表明,纹理分析可以区分病理生理条件下各种肌肉的质量,不过由于受试者招募存在偏倚且缺乏与既定参考标准的比较,证据强度较低。这是第一项关于在不同病理生理条件下,利用肌肉骨骼超声纹理分析评估各种肌肉质量的系统评价。