Neurogenic and Myogenic Diseases: Quantitative Texture Analysis of Muscle US Data for Differentiation

• Published in: Radiology Vol. 283; no. 2; pp. 492 - 498
• Main Authors: Sogawa, Kazuki, Nodera, Hiroyuki, Takamatsu, Naoko, Mori, Atsuko, Yamazaki, Hiroki, Shimatani, Yoshimitsu, Izumi, Yuishin, Kaji, Ryuji
• Format: Journal Article
• Language: English
• Published: United States 01.05.2017
• Subjects: Adult; Aged; Algorithms; Diagnosis, Differential; Female; Humans; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Male; Middle Aged; Muscle, Skeletal - diagnostic imaging; Muscular Diseases - diagnostic imaging; Neuromuscular Diseases - diagnostic imaging; Reproducibility of Results; Sensitivity and Specificity; Ultrasonography - methods
• ISSN: 0033-8419; 1527-1315; 1527-1315
• DOI: 10.1148/radiol.2016160826

Purpose To assess the multiple texture features of skeletal muscles in neurogenic and myogenic diseases by using ultrasonography (US). Materials and Methods After institutional review board approval, muscle US studies of the medial head of the gastrocnemius were performed prospectively in patients with neurogenic diseases (n = 25 [18 men]; mean age, 66.0 years ± 12.3 [standard deviation]), in patients with myogenic diseases (n = 21 [12 men]; mean age, 68.3 years ± 11.5), and in healthy control subjects (n = 21 [11 men]; mean age, 70.5 years ± 8.4) between January 2013 and May 2016. Written informed consent was obtained. Muscle texture parameters were obtained, and five algorithms were used to classify the groups. Results The neurogenic and myogenic disease groups showed higher echo intensities than the control subjects. The histogram-derived texture parameters had overlaps between the neurogenic and myogenic groups and thus had a low discrimination rate. With assessment of more classes of texture parameters, three groups were correctly classified (100% correct, according to four of five classification algorithms). Tenfold cross validation showed 93.5%-95.7% correct classification between the neurogenic and myogenic groups. The run-length matrix, autoregressive model, and co-occurrence matrix were particularly useful in distinguishing the neurogenic and myogenic groups. Conclusion Texture analysis of muscle US data can enable differentiation between neurogenic and myogenic diseases and is useful in noninvasively assessing underlying disease mechanisms. RSNA, 2017 Online supplemental material is available for this article.