Deep Learning Integration of Chest Computed Tomography Imaging and Gene Expression Identifies Novel Aspects of COPD.

Abstract

Rationale: Chronic 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 blood transcriptome patterns have not been fully described.

Objections: The objective of this study was to identify novel relationships between lung structural changes measured by chest computed tomography (CT) and blood transcriptome patterns measured by blood RNA sequencing (RNA-seq).

Methods: CT scan images and blood RNA-seq gene expression from 1223 participants in the COPD Genetic Epidemiology (COPDGene^®) study were jointly analyzed using deep learning to identify shared aspects of inflammation and lung structural changes that we labeled 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.

Results: We identified 2 distinct IEAs: IEA_emph which captures an emphysema-predominant process with a strong positive correlation to CT emphysema and a negative correlation to forced expiratory volume in 1 second and body mass index (BMI); and IEA_airway which 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 IEA_emph and IEA_airway, respectively (adjusted p<0.001).

Conclusions: Integration of CT scans and blood RNA-seq data identified 2 IEAs that capture distinct inflammatory processes associated with emphysema and airway-predominant COPD.