Asadi Borhan, Cuenca-Zaldívar Juan Nicolás, Carcasona-Otal Alberto, Herrero Pablo, Lapuente-Hernández Diego
iHealthy Research Group, Instituto de Investigación Sanitaria (IIS) Aragon, University of Zaragoza, 50009 Zaragoza, Spain.
Department of Physiatry and Nursing, Faculty of Health Sciences, University of Zaragoza, 50009 Zaragoza, Spain.
J Clin Med. 2025 Apr 23;14(9):2902. doi: 10.3390/jcm14092902.
Ultrasound (US) imaging and echotexture analysis are emerging techniques for assessing muscle tissue quality in the post-stroke population. Clinical studies suggest that echovariation (EV) and echointensity (EI) serve as objective indicators of muscle impairment, although methodological limitations hinder their clinical translation. This secondary analysis aimed to refine the assessment of echotexture by using robust statistical techniques. A total of 130 regions of interest (ROIs) extracted from the gastrocnemius medialis of 22 post-stroke individuals were analyzed. First, inter-examiner reliability between two physiotherapists was assessed by using Cohen's kappa for muscle impairment classification (low/high) for each echotexture feature. For each examiner, the correlation between the classification of the degree of impairment and the modified Heckmatt scale for each feature was analyzed. The dataset was then reduced to 44 ROIs (one image per leg per patient) and assessed by three physiotherapists to analyze inter-examiner reliability by using Light´s kappa and correlation between both assessment methods globally. Statistical differences in 21 echotexture features were evaluated according to the degree of muscle impairment. A binary logistic regression model was developed by using features with a Cohen's kappa value greater than 0.9 as predictors. A strong and significant degree of agreement was observed among the three examiners regarding the degree of muscle impairment (Kappa = 0.85, < 0.001), with nine of the 21 features showing excellent inter-examiner reliability. The correlation between muscle impairment classification with the modified Heckmatt scale was very high and significant both globally and for each echotexture feature. Significant differences (<0.05) were found for EV, EI, dissimilarity, energy, contrast, maximum likelihood, skewness, and the modified Heckmatt scale. Logistic regression highlighted dissimilarity, entropy, EV, Gray-Level Uniformity (GLU), and EI as the main predictors of muscle tissue impairment. The EV and EI models showed high explanatory power (Nagelkerke's pseudo-R = 0.74 and 0.76) and robust classification performance (AUC = 94.20% and 95.45%). This secondary analysis confirms echotexture analysis as a reliable tool for post-stroke muscle assessment, validating EV and EI as key indicators while identifying dissimilarity, entropy, and GLU as additional relevant features.
超声（US）成像和回声纹理分析是评估中风后人群肌肉组织质量的新兴技术。临床研究表明，回声变化（EV）和回声强度（EI）可作为肌肉损伤的客观指标，尽管方法学上的局限性阻碍了它们在临床上的应用。这项二次分析旨在通过使用稳健的统计技术来完善对回声纹理的评估。对22名中风后个体的腓肠肌内侧提取的总共130个感兴趣区域（ROI）进行了分析。首先，使用科恩kappa系数评估两名物理治疗师之间在每个回声纹理特征的肌肉损伤分类（低/高）方面的检查者间可靠性。对于每位检查者，分析每个特征的损伤程度分类与改良的赫克马特量表之间的相关性。然后将数据集缩减至44个ROI（每位患者每条腿一张图像），并由三名物理治疗师进行评估，以使用莱特kappa系数分析检查者间可靠性以及两种评估方法之间的整体相关性。根据肌肉损伤程度评估21个回声纹理特征的统计学差异。使用科恩kappa值大于0.9的特征作为预测因子建立二元逻辑回归模型。三名检查者在肌肉损伤程度方面观察到高度且显著的一致性（kappa系数 = 0.85，<0.001），21个特征中有9个显示出出色的检查者间可靠性。肌肉损伤分类与改良的赫克马特量表之间的相关性在整体上以及每个回声纹理特征方面都非常高且显著。在EV、EI、差异度、能量、对比度、最大似然性、偏度和改良的赫克马特量表方面发现了显著差异（<0.05）。逻辑回归突出显示差异度、熵、EV、灰度均匀度（GLU）和EI是肌肉组织损伤的主要预测因子。EV和EI模型显示出高解释力（纳格尔克的伪R = 0.74和0.76）以及稳健的分类性能（AUC = 94.20%和95.45%）。这项二次分析证实回声纹理分析是中风后肌肉评估的可靠工具，验证了EV和EI作为关键指标，同时确定差异度、熵和GLU为其他相关特征。
