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Improving diagnostic and therapeutic imaging tools for COPD

Institution: University of California, Los Angeles
Investigator(s): Anand Santhanam, Ph.D,
Award Cycle: 2018 (Cycle 27) Grant #: 27IR-0056 Award: $919,571
Subject Area: Pulmonary Disease
Award Type: High Impact Research Project Award

Initial Award Abstract

Chronic lower respiratory disease, primarily Chronic Obstructive Pulmonary Disease (COPD), was the third leading cause of death in the United States in 2011. Smoking accounts for as much as 85 to 90 percent of all COPD cases and as many as 8 out of 10 COPD-related deaths. COPD is a progressive lung disease that degrades the body’s ability to transport and absorb oxygen and eliminate carbon dioxide. For mild cases, this results in shortness of breath during everyday activities and for serious cases, death. Even mild COPD limits the patient’s ability to work and engage in normal physical exertion and household chores. Based on current diagnostic criteria for COPD, all tobacco users get affected with at least mild to moderate COPD. However, diagnostic studies that determine the severity and progression of COPD are relatively crude. Since there is a direct relation between smoking and lung tissue damage, we hypothesize that the measurement of tissue elastic properties, i.e., the stretchiness of the lung tissues, and airflow dynamics within a patient’s lungs will significantly improve COPD diagnostics and disease management for patients with smoking history. There is a need, though, to develop methods to measure tissue elasticity as well as airflow dynamics, which forms the focus of this proposal.

The overall goal of this proposal is to improve our ability to determine COPD severity and to monitor a patient’s response to treatment by developing a sophisticated patient-specific lung damage model that is based on data collected non-invasively while the patient is freely breathing.  The model will be produced using cutting edge lung imaging and image analysis, and a computational method to estimate the elastic lung tissue properties, coupled to airflow dynamics modeling.  We will combine these properties into a single computational model, which we term a flow-structure interaction (FSI) model. The FSI model will quantitatively characterize the lung’s functional physiology in greater detail than afforded by current methods. We hypothesize that disease stage, disease characteristics, treatment response, and other clinically relevant properties of COPD will be directly and easily measurable with patient-specific FSI modeling. To validate our hypothesis, we will conduct a pilot study on COPD patients that compares conventional clinical classifications with our FSI model-based disease classification as well as show that the FSI model can detect changes in treatment-initiated airflow and elastic tissue parameters. 

If successful, the proposed FSI model will open avenues of research that will provide new and critical clinical information for pulmonologists and pulmonary surgeons in the management of COPD. We hypothesize that the proposed FSI model will enable researchers to develop a non-invasive and sophisticated image-based approach to quantitatively evaluate and monitor smoking-induced COPD.  Current clinical tools are crude and provide the clinician with little guidance as to the disease status, progression, and treatment efficacy.  Our proposed approach will provide a rich and quantitative 3-dimensional dataset that will describe the mechanical function and properties of each portion of the lungs.  This information will be available to clinical researchers to develop clinical disease models that take advantage of the non-invasive nature of the FSI model.  Drug companies will have, for the first time, 3-dimensional elasticity information to both screen prospective trial participants and develop drug trials that will better evaluate drug efficacy.  Smokers, the majority of COPD patients, will directly benefit from advances in diagnosis and measurement of treatment response, enabled by the results of the proposed research.