J Korean Soc Radiol.  2023 Sep;84(5):1123-1133. 10.3348/jksr.2022.0152.

CT-Derived Deep LearningBased Quantification of Body Composition Associated with Disease Severity in Chronic Obstructive Pulmonary Disease

• Song JE
• Bak SH
• Lim MN
• Lee EJ
• Cha YK
• Yoon HJ
• Kim WJ

• KMID: 2546651
• DOI: http://doi.org/10.3348/jksr.2022.0152

Abstract

Purpose
Our study aimed to evaluate the association between automated quantified body composition on CT and pulmonary function or quantitative lung features in patients with chronic obstructive pulmonary disease (COPD).
Materials and Methods
A total of 290 patients with COPD were enrolled in this study. The volume of muscle and subcutaneous fat, area of muscle and subcutaneous fat at T12, and bone attenuation at T12 were obtained from chest CT using a deep learning-based body segmentation algorithm. Parametric response mapping-derived emphysema (PRM^emph ), PRM-derived functional small airway dis-ease (PRM^fSAD ), and airway wall thickness (AWT)-Pi10 were quantitatively assessed. The association between body composition and outcomes was evaluated using Pearson’s correlation analysis.
Results
The volume and area of muscle and subcutaneous fat were negatively associated with PRM^emph and PRM^fSAD (p < 0.05). Bone density at T12 was negatively associated with PRM^emph (r = -0.1828, p = 0.002). The volume and area of subcutaneous fat and bone density at T12 were positively correlated with AWT-Pi10 (r = 0.1287, p = 0.030; r = 0.1668, p = 0.005; r = 0.1279, p = 0.031). However, muscle volume was negatively correlated with the AWT-Pi10 (r = -0.1966, p = 0.001). Muscle volume was significantly associated with pulmonary function (p < 0.001).
Conclusion
Body composition, automatically assessed using chest CT, is associated with the phenotype and severity of COPD.

Keyword

Multidetector Computed Tomography; Chronic Obstructive Pulmonary Disease; Deep Learning; Muscle