Airway Tree Subtyping on Large Cohorts of CT Images for COPD Risk

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Project Summary / Abstract: Chronic obstructive pulmonary disease (COPD) defined by irreversible airflow limitation, is the 3rd leading cause of death globally and 4th in the United States. Smoking tobacco is a major extrinsic COPD risk factor, but despite six decades of declining smoking rates in many countries, the corresponding declines in COPD have been modest. Only a minority of lifetime smokers develop COPD, and up to 25% occurs in never smokers. While other factors have been linked to COPD much of the variation in COPD risk remains unexplained. In addition, personalized risk and therapies are lacking for COPD, due to a lack of reliable COPD subphenotypes. Airflow obstruction, or reduced airflow from the lungs, is determined in part by airway tree structure and lung volume, both of which can be imaged with high precision by high resolution computed tomographic (HRCT) scans. Emerging evidence by our group suggests that airway tree structure variation is common in the general population and is a major contributor to this unexplained COPD risk. By manual labeling of the airway tree structure, limited to one airway generation in just 2 of the 5 lung lobes (due to complexity of tree structure), we found that 26% of the general population has major airway branch variants that differ from the classical “textbook” structure, increase COPD risk, and have a strong and biologically plausible genetic basis. We further demonstrated that airway tree caliber variation (dysanapsis) measured on CT was a stronger predictor of COPD risk than all known risk factors including smoking. Yet there is no standardized approach to characterize the full scope airway tree variation, making the exact relationship between COPD and individual airway-structure features unclear. This proposal would apply for the first-time the power of machine learning methods to the entire airway tree structure imaged on HRCT to build logically upon prior high-impact work to discover new COPD subphenotypes for risk stratification and biological pathways of intervention. Also, we will apply sophisticated / rigorous mathematical clustering approaches to airway trees derived from over 18,000 computed tomography (CT) scans in three highly characterized NIH/NHLBI-funded cohorts – the Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study, the Subpopulations and Intermediate Outcome Measures in Chronic Obstructive Pulmonary Disease Study (SPIROMICS), and the Genetic Epidemiology of COPD (COPDGene) Study, in addition to the Canadian Cohort of Obstructive Lung Disease (CanCOLD) – to discover and replicate novel and clinically significant airway tree subtypes and their genetic basis. The proposed study provides a transformative opportunity to define and validate normal and clinically relevant tree variation in the general population and COPD cohorts. This research would result in robust, reproducible, image based novel quantitative airway tree structure subtypes from lung CT scans, and understand their role in COPD risk, prognosis, and their underlying genetic basis to help personalize COPD risk.