This study aims to explore structural and functional differences between healthy subjects and asthmatics via a refined and expanded set of quantitative computed tomography (QCT) imaging metrics at local (segmental) and global (lobar) scales. This set of multiscale imaging-based metrics is then employed to perform clustering analysis for the derivation of sub-asthma groups. We further explore the association of the imaging-based clusters with clinical metrics to establish the link between imaging and clinical phenotypes. We analyzed three populations of CT images: 61 healthy subjects, 107 non-severe asthmatics and 153 severe asthmatics at total lung capacity (TLC) and functional residual capacity (FRC). An image registration technique was employed to match TLC and FRC images for assessments of regional volume change and anisotropic deformation. Instead of using existing density-based air-trapping measure, a fraction-based air-trapping measure was proposed to account for inter-site and inter-subject variations of CT density. We also analyzed structural alterations of asthmatic airways, including bifurcation angle, airway circularity, wall thickness and hydraulic diameter. As compared with healthy subjects, severe asthmatics exhibited reduced air volume change (consistent with air-trapping) and more isotropic deformation in the basal lung regions, but increased air volume change associated with increased anisotropic deformation in the apical lung regions. Five imaging-derived clusters were identified and they exhibited strong associations with clinical metrics. The imaging-based clusters have the potential to guide the development of therapeutic intervention.

Dr. Choi's biosketch is located here, https://www.linkedin.com/in/sanghun-choi-42b73074

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