Seeing is believing-on the utility of CT in phenotyping chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition with complicated structural and functional impairments. For decades now, chest CT has been used to quantify various abnormalities related to COPD. More recently, with the newer data-driven approaches, biomarker development and validation have evolved rapidly. Studies now target multiple anatomical structures including lung parenchyma, the airways, the vasculature, and the fissures to better characterize COPD. This review explores the evolution of chest CT biomarkers in COPD, beginning with traditional thresholding approaches that quantify emphysema and airway dimensions. We then highlight some of the texture analysis efforts that have been made over the years for subtyping lung tissue. We also discuss image registration-based biomarkers that have enabled spatially-aware mechanisms for understanding local abnormalities within the lungs. More recently, deep learning has enabled automated biomarker extraction, offering improved precision in phenotype characterization and outcome prediction. We highlight the most recent of these approaches as well. Despite these advancements, several challenges remain in terms of dataset heterogeneity, model generalizability, and clinical interpretability. This review lastly provides a structured overview of these limitations and highlights future potential of CT biomarkers in personalized COPD management.