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

J Korean Soc Radiol. 2023 Sep;84(5):1123-1133. doi: 10.3348/jksr.2022.0152. Epub 2023 Sep 22.

Abstract
in English, Korean

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 (PRMemph), PRM-derived functional small airway disease (PRMfSAD), 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 PRMemph and PRMfSAD (p < 0.05). Bone density at T12 was negatively associated with PRMemph (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.

목적: 만성폐쇄성폐질환의 CT에서 자동 정량 측정된 체성분과 폐기능 또는 정량적 변수들 사이의 연관성을 알아보고자 하였다.

대상과 방법: 총 290명의 만성폐쇄성폐질환 환자를 대상으로 연구하였다. 흉부 CT에서 근육 및 피하지방 부피, T12 레벨에서 근육 및 피하지방 면적 및 골 감쇠를 딥러닝 기반 분할 알고리즘을 사용하여 획득하였다. Parametric response mapping-derived emphysema (이하 PRMemph), PRM-derived functional small airway disease (이하 PRMfSAD) 및 기도 벽 두께 (airway wall thickness; 이하 AWT)-Pi10을 정량적으로 평가하였다. Pearson 상관 분석을 사용하여 체성분과 결과 간의 연관성을 평가하였다.

결과: 근육과 피하지방의 부피와 면적은 PRMemph와 PRMfSAD와 음의 상관관계를 보였다(p < 0.05). T12에서의 골밀도는 PRMemph와 음의 상관관계를 보였다(r = -0.1828, p = 0.002). 피하지방의 부피와 면적과 T12에서의 골밀도는 AWT-Pi10과 양의 상관관계를 보였다(r = 0.1287, p = 0.030; r = 0.1668, p = 0.005; r = 0.1279, p = 0.031). 반면에 근육 부피는 AWT-Pi10과 음의 상관관계를 보였다(r = -0.1966, p = 0.001). 근육 부피는 폐기능과 의미 있는 연과성을 보였다(p < 0.001).

결론: 흉부 CT에서 정량적으로 평가된 체성분은 만성폐쇄성폐질환의 표현형 또는 중증도와 연관성을 보인다.

Keywords: Chronic Obstructive Pulmonary Disease; Deep Learning; Multidetector Computed Tomography; Muscle.