BACKGROUND: Computed Tomography (CT) scans allow opportunistic evaluation of body composition. We investigated whether body composition and change through time are associated with lung cancer incidence and all-cause/lung cancer-specific mortality in a lung cancer screening cohort. METHODS: A machine learning segmentation method was used in this retrospective cohort study to measure skeletal muscle area and density, and subcutaneous adipose tissue area (SAT) on repeated chest CTs from the Dutch-Belgian lung cancer screening trial. Hazard ratios by sex adjusted for age, smoking status, and smoking pack-years (aHR) were calculated for each outcome. FINDINGS: During median follow-up of 12.2 (interquartile range, 1.2) years, 4.1% of 6187 subjects (85.5% male, mean age ± SD, 58.6 ± 5.5 years, smoking pack-years 41.2 ± 18.3) developed lung cancer, 12.2% died, and 2.1% died due to lung cancer. For males, SAT loss was associated with lung cancer incidence (aHR 1.19, 95% CI 1.02-1.39) and lung cancer-specific mortality (aHR 1.26, 95% CI 1.03-1.55), and less baseline muscle and muscle loss with all-cause mortality (aHR 1.20, 95% CI 1.10-1.31 and 1.17, 1.07-1.27). For females, less baseline SAT and SAT loss was associated with all-cause mortality (aHR 1.44, 95% CI 1.06-1.97 and 1.48, 1.13-1.94) and lung cancer-specific mortality (aHR 2.85, 95% CI 1.50-5.39 and aHR 1.96, 1.11-3.44). Models improved by including body composition trends for all-cause mortality (males: p < 0.001; females: p = 0.012) and for lung cancer-specific mortality (males: p = 0.102; females: p = 0.005). INTERPRETATION: Body composition trends based on automated analysis of chest CT are associated with worse outcomes in participants screened for lung cancer. FUNDING: Dutch Cancer Society, Health Holland, Siemens Healthineers.
Bunk et al. (Thu,) studied this question.