Structural and functional assessments of COPD populations via image registration and unsupervised machine learning

There is notable heterogeneity in clinical presentation of patients with chronic obstructive pulmonary disease (COPD). Classification of COPD is usually based on the severity of airflow limitation (pre- and post- bronchodilator FEV1), which may not sensitively differentiate subpopulations with distinct phenotypes. A recent advance of quantitative medical imaging and data analysis techniques allows for deriving quantitative computed tomography (QCT) imaging-based metrics. These imaging-based metrics can be used to link structural and functional alterations at multiscale levels of human lung. We acquired QCT images of 800 former and current smokers from Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS). A GPU-based symmetric non-rigid image registration method was applied at expiration and inspiration to derived QCT-based imaging metrics at multiscale levels. With these imaging-based variables, we employed a machine learning method (an unsupervised clustering technique (K-means)) to identify imaging-based clusters. Four clusters were identified for both current and former smokers. Four clusters were identified for both current and former smokers with meaningful associations with clinical and biomarker measures. Results demonstrated that QCT imaging-based variables in patients with COPD can derive statistically stable and clinically meaningful clusters. This sub-grouping can help better categorize the disease phenotypes, ultimately leading to a development of an efficient therapy.