Silent Zones of Lung Disease in COPD

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Project Summary: Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the United States and is associated with substantial respiratory morbidity. COPD is characterized by spirometric airflow obstruction due to structural changes in lung parenchyma (emphysema) and airways. However, there exists a marked discordance between spirometry diagnosis and presence of emphysema on CT. Emphysema on inspiratory CT is defined by low-density areas <-950 Hounsfield Units (HU). By anatomically matching inspiratory and expiratory CT scans through image registration, we derived a CT measure of lung elasticity termed the Jacobian determinant of lung deformation (J) which is a point-by-point measure of lung expansion and contraction during respiration. We hypothesize that the CT-based lung mechanics will enable identification of regions that appear normal per traditional CT density criteria but are mechanically compromised during respiration. We will test the “Silent Zones” hypothesis by evaluating 10,300 current and former smokers enrolled in the Genetic Epidemiology of COPD (COPDGene) cohort with the following specific aims. In Aim 1, we will quantify Silent Zones by matching inspiratory and expiratory CT scans and to determine their associations with lung function, respiratory quality of life and functional capacity. In Aim 2, we will use 6,284 subjects who completed a second COPDGene visit after 5-years to quantify the percentage of Silent Zones progressed into emphysematous areas and also to determine the prognostic utility of Silent Zones by testing their association with FEV1 decline and mortality. In Aim 3, we will develop a deep convolutional neural network to identify Silent Zones directly from inspiratory CT scans, thus avoiding the computationally intensive image matching process. I will utilize this proposal to acquire advanced training in biostatistics, lung physiology, deep learning, parallel computing for large medical cohorts. The opportunities created by this Career Development Award will provide me with a clearly delineated path to acquire expertise and develop a research niche in the field of COPD. The aims of this research proposal and career development plan are possible through the active mentorship of Dr. Surya Bhatt, a leading expert in lung imaging research and the Director of UAB Lung Imaging Lab and Dr. Arie Nakhmani, an expert in computer vision, image registration, and machine learning methodologies. The proposed study will provide me with the skill set to achieve my long-term goal of an independent career in translational research focusing on medical imaging and machine learning applications for COPD.