In some individuals chronic tobacco smoke exposure results in emphysema and pulmonary fibrosis. Both of these parenchymal changes are irreversible, highlighting the importance of the early identification of their development and progression. Unfortunately, currently available methods for detecting the presence and evolution of these changes have limited sensitivity and specificity for very early disease and for subtle disease progression. Dr. Ash’s work has shown that automated objective analysis of computed tomography (CT) scans of the chest can detect clinically relevant radiologic findings called interstitial changes that may represent early pulmonary fibrosis, even in individuals without visually apparent disease. In the first aim of this proposal, Dr. Ash will refine and utilize a more sensitive and specific automated CT analysis tool that he and his lab have developed for the detection of both emphysema and interstitial changes. He will determine if emphysema and interstitial changes detected using this method are clinically significant in those patients deemed normal by previously performed visual analysis and in those deemed normal by other objective approaches. In the second aim, he will determine if areas of locally high density tissue in visually normal appearing lung parenchyma measured using augmented versions of his objective analysis tools are associated with mortality, other clinical outcomes, and peripheral measures of inflammation. Finally, in the third aim he will utilize these techniques to analyze longitudinal CT scans from the COPDGene study that were obtained over 10 years of follow-up, and will identify factors that predict or modify the development and progression of parenchymal changes on CT. Dr. Ash will perform this work in the Division of Pulmonary and Critical Care Medicine at Brigham and Women’s Hospital (BWH), a core teaching hospital of Harvard Medical School, under the mentorship of Dr. George Washko, an expert in the field of medical image analysis and the co-principal investigator of the Applied Chest Imaging Laboratory at BWH. With the guidance of Dr. Washko and his scientific advisory committee, Dr. Ash has developed a comprehensive five year training program to develop the skills needed to become an independent investigator with expertise in quantitative image analysis, including predictive modeling and statistical machine learning. Dr. Ash is dedicated to a career in academic medicine. His goal is to become a clinician-scientist using the skills gained during this award to improve our ability to detect and monitor smoking related lung disease. The techniques he has proposed may help identify modifiable risk factors and treatments for smoking related lung disease, determine which patients are likely to benefit from treatment, and monitor the response to therapy.