• University of Michigan Michigan Medicine, Pulmonary and Critical Care, Ann Arbor, Michigan, United States; wangjenn@med.umich.edu.
• University of Michigan Michigan Medicine, Biostatistics, Ann Arbor, Michigan, United States.
• University of Michigan Michigan Medicine, Pulmonary and Critical Care, Ann Arbor, Michigan, United States.
• University of Michigan Michigan Medicine, Radiology, Ann Arbor, Michigan, United States.
• UCSD, Radiology, San Diego, United States.
• Imbio LLC, Minneapolis, United States.

RATIONALE: Incidental features of interstitial lung disease (ILD) are commonly observed on chest computed tomography (CT) scans and are independently associated with poor outcomes. While 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.

OBJECTIVES: To determine whether quantitative measures of ILD are associated with clinically relevant outcomes in the National Lung Screening Trial (NLST).

METHODS: 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., Minneapolis, MN). A multivariable Cox proportional hazard regression model was used to test the association between ILD measures (percent 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.

RESULTS: 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 percent 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.

CONCLUSIONS: Quantitative measures of ILD on LDCT are associated with clinically relevant endpoints in a large at-risk population of individuals with tobacco use history. Primary Source of Funding: This work was supported by the National Institutes of Health Grants K24HL138188 (MKH), F32HL175973 (JMW), T32HL007749 (JMW), R01HL169166 (JMO), R01HL166290 (JMO). Word Count: 324/350.