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>