A Bayesian Nonparametric Model for Disease Subtyping: Application to Emphysema Phenotypes.

ABSTRACT: We introduce a novel Bayesian nonparametric model that uses the concept of disease trajectories for disease subtype identification. Although our model is general, we demonstrate that by treating fractions of tissue patterns derived from medical images as compositional data, our model can be applied to study distinct progression trends between population subgroups. Specifically, we apply our algorithm to quantitative emphysema measurements obtained from chest CT scans in the COPDGene Study and show several distinct progression patterns. As emphysema is one of the major components of chronic obstructive pulmonary disease (COPD), the third leading cause of death in the United States [1], an improved definition of emphysema and COPD subtypes is of great interest. We investigate several models

SUBMITTER: Ross JC 

PROVIDER: S-EPMC5267575 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature