Chronic Obstructive Pulmonary Disease (COPD) affects up to 24 million people in the United States and is projected to be the 3rd leading cause of death worldwide by 2020 with a total cost of $50 billion. COPD has been traditionally dichotomized into the clinical phenotypes of emphysema and chronic bronchitis, but its underlying mechanisms are poorly understood. In particular, emphysema is defined as abnormal, permanent dilation of the distal airspaces. The development and progression of this pathologic process are associated with a decline in lung function and progressive clinical impairment. Computed tomographic (CT) imaging of the chest is increasingly being leveraged to quantify the disease and its progression objectively. Current approaches to quantify emphysema progression are limited and discard most of the spatial and temporal information in CT scans obtained at inspiration and expiration. In this proposal, we plan on developing computational components to prognosticate emphysema progression that builds upon image density markers and lung mechanical strain characteristics conditioned on their underlying emphysema subtypes. This proposal leverages our previous experience in computational emphysema subtyping to discover, validate and translate a novel panel of prognostic markers tailored around the postulated mechanisms of emphysema progression: inflammation injury and mechanical strain. To reach this goals, we will (1) develop an advanced emphysema subtyping approach using novel deep learning architectures, (2) develop a fast mass preserving large displacement registration approach to enable the discovery of local elastic properties of lung tissue between inspiratory and expiration CT scans, (3) discover new subtype-specific biomarker features based on image density relations and mechanical properties using unsupervised deep learning techniques within a common statistical framework, and (4) validate the prognostic value of the proposed biomarkers and their association with decline end-points and clinical outcomes to enable its clinical interpretation and translation. In addition to that, will be explored alternative prognostic models based on advanced machine learning techniques and performed a model comparison study to define the most prognostic model for emphysema progression. Our analysis will process 12,300 scans corresponding to 5,517 subjects with baseline and follow-up data from the COPDGene cohort –one of the largest cohort in COPD containing CT images at inspiration and expiration, respiratory and genetic measurements. The proposed methodology will provide reproducible, automatic and low-cost prognostic in-vivo biomarkers of emphysema progression that may enable the discovery of new therapies and translate them into clinical practice.