Assessment of Myosteatosis on Computed Tomography by Automatic Generation of a Muscle Quality Map Using a Web-Based Toolkit: Feasibility Study

Dong Wook Kim; Kyung Won Kim; Yousun Ko; Taeyong Park; Seungwoo Khang; Heeryeol Jeong; Kyoyeong Koo; Jeongjin Lee; Hong-Kyu Kim; Jiyeon Ha; Yu Sub Sung; Youngbin Shin

doi:10.2196/23049

Assessment of Myosteatosis on Computed Tomography by Automatic Generation of a Muscle Quality Map Using a Web-Based Toolkit: Feasibility Study

JMIR Med Inform. 2020 Oct 19;8(10):e23049. doi: 10.2196/23049.

Authors

Affiliations

¹ Department of Radiology and Research Institute of Radiology, Asan Medical Center, Seoul, Republic of Korea.
² Biomedical Research Center, Asan Medical Center, Seoul, Republic of Korea.
³ School of Computer Science and Engineering, Soongsil University, Seoul, Republic of Korea.
⁴ Health Screening and Promotion Center, Asan Medical Center, Seoul, Republic of Korea.
⁵ Clinical Research Center, Asan Medical Center, Seoul, Republic of Korea.
⁶ Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, Republic of Korea.

PMID: 33074159
PMCID: PMC7605976
DOI: 10.2196/23049

Abstract

Background: Muscle quality is associated with fatty degeneration or infiltration of the muscle, which may be associated with decreased muscle function and increased disability.

Objective: The aim of this study is to evaluate the feasibility of automated quantitative measurements of the skeletal muscle on computed tomography (CT) images to assess normal-attenuation muscle and myosteatosis.

Methods: We developed a web-based toolkit to generate a muscle quality map by categorizing muscle components. First, automatic segmentation of the total abdominal muscle area (TAMA), visceral fat area, and subcutaneous fat area was performed using a predeveloped deep learning model on a single axial CT image at the L3 vertebral level. Second, the Hounsfield unit of each pixel in the TAMA was measured and categorized into 3 components: normal-attenuation muscle area (NAMA), low-attenuation muscle area (LAMA), and inter/intramuscular adipose tissue (IMAT) area. The myosteatosis area was derived by adding the LAMA and IMAT area. We tested the feasibility of the toolkit using randomly selected healthy participants, comprising 6 different age groups (20 to 79 years). With stratification by sex, these indices were compared between age groups using 1-way analysis of variance (ANOVA). Correlations between the myosteatosis area or muscle densities and fat areas were analyzed using Pearson correlation coefficient r.

Results: A total of 240 healthy participants (135 men and 105 women) with 40 participants per age group were included in the study. In the 1-way ANOVA, the NAMA, LAMA, and IMAT were significantly different between the age groups in both male and female participants (P≤.004), whereas the TAMA showed a significant difference only in male participants (male, P<.001; female, P=.88). The myosteatosis area had a strong negative correlation with muscle densities (r=-0.833 to -0.894), a moderate positive correlation with visceral fat areas (r=0.607 to 0.669), and a weak positive correlation with the subcutaneous fat areas (r=0.305 to 0.441).

Conclusions: The automated web-based toolkit is feasible and enables quantitative CT assessment of myosteatosis, which can be a potential quantitative biomarker for evaluating structural and functional changes brought on by aging in the skeletal muscle.

Keywords: CT; automated; body composition; computed tomography; feasibility; muscle; sarcopenia; scan; skeletal; web-based tool; x-ray.

©Dong Wook Kim, Kyung Won Kim, Yousun Ko, Taeyong Park, Seungwoo Khang, Heeryeol Jeong, Kyoyeong Koo, Jeongjin Lee, Hong-Kyu Kim, Jiyeon Ha, Yu Sub Sung, Youngbin Shin. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 19.10.2020.