Quantitative Computed Tomography Measures of Lung Fibrosis and Outcomes in the National Lung Screening Trial.

Incidental features of interstitial lung disease (ILD) are commonly observed on chest computed tomography (CT) scans and are independently associated with poor outcomes. Although most studies to date have relied on qualitative assessments of ILD, quantitative imaging algorithms have the potential to effectively detect ILD and assist in risk stratification for population-based cohorts. To determine whether quantitative measures of ILD are associated with clinically relevant outcomes in the NLST (National Lung Screening Trial). Quantitative measures of ILD were generated using low-dose CT (LDCT) data collected as part of the NLST and processed with Computer-Aided Lung Informatics for Pathology Evaluation and Ratings (CALIPER) and deep learning-based usual interstitial pneumonia (DL-UIP) algorithms (Imbio Inc.). A multivariable Cox proportional hazard regression model was used to test the association between ILD measures (percentage ground-glass opacity, reticular opacity, and honeycombing of total lung volume and binary DL-UIP classification) and all-cause mortality. Secondary outcomes of incident lung cancer and lung cancer mortality were also explored. Quantitative CT data were generated in 11,518 individuals. Mean age was 61.5 years, and 58.7% were male. An increased risk of all-cause mortality was observed for each percentage increase in CALIPER-derived ground-glass opacity (hazard ratio [HR], 1.02; 95% confidence interval [CI], 1.01-1.02), reticular opacity (HR, 1.18; 95% CI, 1.12-1.24), and honeycombing (HR, 6.23; 95% CI, 4.23-9.16). Individuals with a positive DL-UIP classification pattern had a 4.8-fold increased risk of all-cause mortality (HR, 4.75; 95% CI, 2.50-9.04). CALIPER-derived reticular opacity was also associated with increased lung cancer-specific mortality. No quantitative measures of ILD were associated with incident lung cancer. Quantitative measures of ILD on LDCT are associated with clinically relevant endpoints in a large at-risk population of individuals with tobacco use history.