Deep Learning Integration of Chest CT Imaging and Gene Expression Identifies Novel Aspects of COPD

RationaleChronic obstructive pulmonary disease (COPD) is characterized by pathologic changes in the airways, lung parenchyma, and persistent inflammation, but the links between lung structural changes and patterns of systemic inflammation have not been fully described. ObjectivesTo identify novel relationships between lung structural changes measured by chest computed tomography (CT) and systemic inflammation measured by blood RNA sequencing. MethodsCT scan images and blood RNA-seq gene expression from 1,223 subjects in the COPDGene study were jointly analyzed using deep learning to identify shared aspects of inflammation and lung structural changes that we refer to as Image-Expression Axes (IEAs). We related IEAs to COPD-related measurements and prospective health outcomes through regression and Cox proportional hazards models and tested them for biological pathway enrichment. Measurements and Main ResultsWe identified two distinct IEAs: IEAemph captures an emphysema-predominant process with a strong positive correlation to CT emphysema and a negative correlation to FEV1 and Body Mass Index (BMI); IEAairway captures an airway-predominant process with a positive correlation to BMI and airway wall thickness and a negative correlation to emphysema. Pathway enrichment analysis identified 29 and 13 pathways significantly associated with IEAemph and IEAairway, respectively (adjusted p<0.001). ConclusionsIntegration of CT scans and gene expression data identified two IEAs that capture distinct inflammatory processes associated with emphysema and airway-predominant COPD. At a Glance CommentaryO_ST_ABSScientific Knowledge on the SubjectC_ST_ABSChronic obstructive pulmonary disease (COPD) is characterized by lung structural changes and has a prominent systemic inflammatory component, but the links between lung structural changes and patterns of systemic inflammation in COPD have not been fully described. What This Study Adds to the FieldWe identified novel relationships between lung structural changes and systemic inflammation by simultaneously analyzing CT scans and blood RNA-sequencing gene expression using deep learning models. We identified two distinct Image-Expression Axes (IEAs) that characterize different inflammatory processes associated with emphysema and airway predominant COPD. This article has an online data supplement, which is accessible from this issues table of content online at www.atsjournals.org.