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Assessment of Myosteatosis on Computed Tomography by Automatic Generation of a Muscle Quality Map Using a Web-Based Toolkit: Feasibility Study.
Kim, Dong Wook; Kim, Kyung Won; Ko, Yousun; Park, Taeyong; Khang, Seungwoo; Jeong, Heeryeol; Koo, Kyoyeong; Lee, Jeongjin; Kim, Hong-Kyu; Ha, Jiyeon; Sung, Yu Sub; Shin, Youngbin.
Afiliación
  • Kim DW; Department of Radiology and Research Institute of Radiology, Asan Medical Center, Seoul, Republic of Korea.
  • Kim KW; Department of Radiology and Research Institute of Radiology, Asan Medical Center, Seoul, Republic of Korea.
  • Ko Y; Biomedical Research Center, Asan Medical Center, Seoul, Republic of Korea.
  • Park T; School of Computer Science and Engineering, Soongsil University, Seoul, Republic of Korea.
  • Khang S; School of Computer Science and Engineering, Soongsil University, Seoul, Republic of Korea.
  • Jeong H; School of Computer Science and Engineering, Soongsil University, Seoul, Republic of Korea.
  • Koo K; School of Computer Science and Engineering, Soongsil University, Seoul, Republic of Korea.
  • Lee J; School of Computer Science and Engineering, Soongsil University, Seoul, Republic of Korea.
  • Kim HK; Health Screening and Promotion Center, Asan Medical Center, Seoul, Republic of Korea.
  • Ha J; Department of Radiology and Research Institute of Radiology, Asan Medical Center, Seoul, Republic of Korea.
  • Sung YS; Clinical Research Center, Asan Medical Center, Seoul, Republic of Korea.
  • Shin Y; Department of Convergence Medicine, University of Ulsan College of Medicine, Seoul, Republic of Korea.
JMIR Med Inform ; 8(10): e23049, 2020 Oct 19.
Article en En | MEDLINE | ID: mdl-33074159
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.
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Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: JMIR Med Inform Año: 2020 Tipo del documento: Article

Texto completo: 1 Bases de datos: MEDLINE Idioma: En Revista: JMIR Med Inform Año: 2020 Tipo del documento: Article