10.6084/m9.figshare.5335966.v1 Kirby M. Kirby M. Yin Y. Yin Y. Tschirren J. Tschirren J. Tan W.C. Tan W.C. Leipsic J. Leipsic J. Hague C.J. Hague C.J. Bourbeau J. Bourbeau J. Sin D.D. Sin D.D. Hogg J.C. Hogg J.C. Coxson H.O. Coxson H.O. for the CanCOLD Collaborative Research Group and the Canadian Respiratory Research Network for the CanCOLD Collaborative Research Group and the Canadian Respiratory Research Network Supplementary Material for: A Novel Method of Estimating Small Airway Disease Using Inspiratory-to-Expiratory Computed Tomography Karger Publishers 2017 Computed tomography Small airway disease Disease probability measure Chronic obstructive pulmonary disease 2017-08-23 10:21:01 Poster https://karger.figshare.com/articles/poster/Supplementary_Material_for_A_Novel_Method_of_Estimating_Small_Airway_Disease_Using_Inspiratory-to-Expiratory_Computed_Tomography/5335966 <p><b><i>Background:</i></b> Disease accumulates in the small airways without being detected by conventional measurements. <b><i>Objectives:</i></b> To quantify small airway disease using a novel computed tomography (CT) inspiratory-to-expiratory approach called the disease probability measure (DPM) and to investigate the association with pulmonary function measurements. <b><i>Methods:</i></b> Participants from the population-based CanCOLD study were evaluated using full-inspiration/full-expiration CT and pulmonary function measurements. Full-inspiration and full-expiration CT images were registered, and each voxel was classified as emphysema, gas trapping (GasTrap) related to functional small airway disease, or normal using two classification approaches: parametric response map (PRM) and DPM (VIDA Diagnostics, Inc., Coralville, IA, USA). <b><i>Results:</i></b> The participants included never-smokers (<i>n</i> = 135), at risk (<i>n</i> = 97), Global Initiative for Chronic Obstructive Lung Disease I (GOLD I) (<i>n</i> = 140), and GOLD II chronic obstructive pulmonary disease (<i>n</i> = 96). PRM<sub>GasTrap</sub> and DPM<sub>GasTrap</sub> measurements were significantly elevated in GOLD II compared to never-smokers (<i>p</i> < 0.01) and at risk (<i>p</i> < 0.01), and for GOLD I compared to at risk (<i>p</i> < 0.05). Gas trapping measurements were significantly elevated in GOLD II compared to GOLD I (<i>p</i> < 0.0001) using the DPM classification only. Overall, DPM classified significantly more voxels as gas trapping than PRM (<i>p</i> < 0.0001); a spatial comparison revealed that the expiratory CT Hounsfield units (HU) for voxels classified as DPM<sub>GasTrap</sub> but PRM<sub>Normal</sub> (PRM<sub>Normal</sub>- DPM<sub>GasTrap</sub> = -785 ± 72 HU) were significantly reduced compared to voxels classified normal by both approaches (PRM<sub>Normal</sub>-DPM<sub>Normal</sub> = -722 ± 89 HU; <i>p</i> < 0.0001). DPM and PRM<sub>GasTrap</sub> measurements showed similar, significantly associations with forced expiratory volume in 1 s (FEV<sub>1</sub>) (<i>p</i> < 0.01), FEV<sub>1</sub>/forced vital capacity (<i>p</i> < 0.0001), residual volume/total lung capacity (<i>p</i> < 0.0001), bronchodilator response (<i>p</i> < 0.0001), and dyspnea (<i>p</i> < 0.05). <b><i>Conclusion:</i></b> CT inspiratory-to-expiratory gas trapping measurements are significantly associated with pulmonary function and symptoms. There are quantitative and spatial differences between PRM and DPM classification that need pathological investigation.</p>