Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jul 30, 2020
Date Accepted: Sep 15, 2020
Assessment of myosteatosis on computed tomography by automatic generation of muscle quality map using a web-based toolkit: Feasibility study
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:
To evaluate the feasibility of automated quantitative measurement of the skeletal muscle on computed tomography (CT) for assessing normal-attenuation muscle and myosteatosis.
Methods:
Randomly selected healthy subjects, comprising six different age groups from 20s to 70s, who underwent CT were retrospectively included. Automatic segmentation of total abdominal muscle area (TAMA), visceral fat area, and subcutaneous fat area was performed using a pre-trained deep learning model on a single axial image at the L3 vertebral level. Using an automated analysis function, the Hounsfield unit of each pixel in the TAMA was measured and categorized into three 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. With stratification by sex, these indices were compared between age groups using one-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 subjects (135 men and 105 women) with 40 per age group were finally included. In the one-way ANOVA, the NAMA, LAMA, and IMAT were significantly different between the age groups both in male and female subjects (P≤0.004), whereas TAMA showed a significant difference only in male subjects (male, P<0.001; female, P=0.877). The myosteatosis area had strong negative correlation with muscle densities (r, -0.833 to -0.894), moderate positive correlation with visceral fat areas (r, 0.607 to 0.669) and 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 might be potential quantitative biomarkers for evaluating structural and functional changes in the skeletal muscle with aging.
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