Objectively Measured Chronic Lung Injury on Chest Computed Tomography

Abstract Background Tobacco smoke exposure is associated with emphysema and pulmonary fibrosis, both of which are irreversible. We have developed a new objective computed tomography (CT) analysis tool that combines densitometry with machine learning to detect high attenuation changes in visually normal appearing lung (Norm HA ) that may precede these diseases. Methods We trained the classification tool by placing 34528 training points in chest CT scans from 297 COPDGene participants. The tool was then used to classify lung tissue in 9038 participants as normal, emphysema, fibrotic/interstitial, or Norm HA . Associations between the quartile of Norm HA and plasma-based biomarkers, clinical severity and mortality were evaluated using Jonckheere-Terpstra, pairwise Wilcoxon Rank Sum tests, and multivariable linear and Cox regression. Results A higher percentage of lung occupied by Norm HA was associated with higher c-reactive protein and intercellular adhesion molecule 1 (p for trend for both HA , those in the highest quartile had a 6.50 absolute percent lower percent predicted lower forced expiratory volume in one second (p Conclusions A new class of visually normal appearing tissue (Norm HA ) on CT may represent a unique tissue class associated with adverse outcomes, independent of emphysema and fibrosis.