000153646 001__ 153646
000153646 005__ 20250508112648.0
000153646 0247_ $$2doi$$a10.3390/jcm14092902
000153646 0248_ $$2sideral$$a143729
000153646 037__ $$aART-2025-143729
000153646 041__ $$aeng
000153646 100__ $$aAsadi, Borhan
000153646 245__ $$aImproving the Reliability of Muscle Tissue Characterization Post-Stroke: A Secondary Statistical Analysis of Echotexture Features
000153646 260__ $$c2025
000153646 5060_ $$aAccess copy available to the general public$$fUnrestricted
000153646 5203_ $$aBackground/Objectives: 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. Methods: 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. Results: A strong and significant degree of agreement was observed among the three examiners regarding the degree of muscle impairment (Kappalight = 0.85, p < 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-R2 = 0.74 and 0.76) and robust classification performance (AUC = 94.20% and 95.45%). Conclusions: 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.
000153646 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000153646 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000153646 700__ $$aCuenca-Zaldívar, Juan Nicolás
000153646 700__ $$aCarcasona-Otal, Alberto$$uUniversidad de Zaragoza
000153646 700__ $$0(orcid)0000-0002-9201-0120$$aHerrero, Pablo$$uUniversidad de Zaragoza
000153646 700__ $$0(orcid)0000-0002-6506-6081$$aLapuente-Hernández, Diego$$uUniversidad de Zaragoza
000153646 7102_ $$11006$$2245$$aUniversidad de Zaragoza$$bDpto. Fisiatría y Enfermería$$cÁrea Educación Física y Depor.
000153646 7102_ $$11006$$2413$$aUniversidad de Zaragoza$$bDpto. Fisiatría y Enfermería$$cÁrea Fisioterapia
000153646 773__ $$g14, 9 (2025), 2902 [12 pp.]$$pJ. clin.med.$$tJournal of Clinical Medicine$$x2077-0383
000153646 8564_ $$s403724$$uhttps://zaguan.unizar.es/record/153646/files/texto_completo.pdf$$yVersión publicada
000153646 8564_ $$s2805386$$uhttps://zaguan.unizar.es/record/153646/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000153646 909CO $$ooai:zaguan.unizar.es:153646$$particulos$$pdriver
000153646 951__ $$a2025-05-08-09:45:57
000153646 980__ $$aARTICLE