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BMJ Open Respir Res. 2017 Nov 9;4(1):e000252. doi: 10.1136/bmjresp-2017-000252. eCollection 2017.

Differentiation of quantitative CT imaging phenotypes in asthma versus COPD.

Author information

1
Department of Mechanical Engineering, Kyungpook National University, Daegu, South Korea.
2
Department of Mechanical and Industrial Engineering, University of Iowa, Iowa City, Iowa, USA.
3
IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, Iowa, USA.
4
Department of Radiology, University of Iowa, Iowa City, Iowa, USA.
5
Department of Internal Medicine, University of Iowa, Iowa City, Iowa, USA.
6
Division of Pulmonary, Allergy and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
7
Departments of Internal Medicine and Pediatrics, Washington University School of Medicine, St. Louis, Missouri, USA.
8
Departments of Radiology and Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, Wisconsin, USA.
9
Department of Medical Physics and Biomedical Engineering, University of Wisconsin, Madison, Wisconsin, USA.
10
Mailman School of Public Health, Columbia University, New York, USA.
11
Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA.
12
Center for Genomics and Personalized Medicine, Wake Forest University, Winston-Salem, North Carolina, USA.
13
Department of Physiology, University of California, Los Angeles, California, USA.
14
Department of Global Public Health, University of North Carolina, Chapel Hill, North Carolina, USA.
15
School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA.
16
School of Medicine, University of Utah, Salt Lake City, Utah, USA.
17
Department of Radiology, University of Michigan, Ann Arbor, Michigan, USA.
18
Department of Medicine, University of California, Los Angeles, Los Angeles, California, USA.
19
Department of Medicine, Weill Cornell School of Medicine, Cornell University, New York, USA.
20
Marsico Lung Institute, University of North Carolina, Chapel Hill, North Carolina, USA.
21
School of Medicine, University of California at San Francisco, San Francisco, California, USA.

Abstract

Introduction:

Quantitative CT (QCT) imaging-based metrics have quantified disease alterations in asthma and chronic obstructive pulmonary disease (COPD), respectively. We seek to characterise the similarity and disparity between these groups using QCT-derived airway and parenchymal metrics.

Methods:

Asthma and COPD subjects (former-smoker status) were selected with a criterion of post-bronchodilator FEV1 <80%. Healthy non-smokers were included as a control group. Inspiratory and expiratory QCT images of 75 asthmatic, 215 COPD and 94 healthy subjects were evaluated. We compared three segmental variables: airway circularity, normalised wall thickness and normalised hydraulic diameter, indicating heterogeneous airway shape, wall thickening and luminal narrowing, respectively. Using an image registration, we also computed six lobar variables including per cent functional small-airway disease, per cent emphysema, tissue fraction at inspiration, fractional-air-volume change, Jacobian and functional metric characterising anisotropic deformation.

Results:

Compared with healthy subjects, both asthma and COPD subjects demonstrated a decreased airway circularity especially in large and upper lobar airways, and a decreased normalised hydraulic diameter in segmental airways. Besides, COPD subjects had more severe emphysema and small-airway disease, as well as smaller regional tissue fraction and lung deformation, compared with asthmatic subjects. The difference of emphysema, small-airway disease and tissue fraction between asthma and COPD was more prominent in upper and middle lobes.

Conclusions:

Patients with asthma and COPD, with a persistent FEV1 <80%, demonstrated similar alterations in airway geometry compared with controls, but different degrees of alterations in parenchymal regions. Density-based metrics measured at upper and middle lobes were found to be discriminant variables between patients with asthma and COPD.

KEYWORDS:

airway luminal narrowing; emphysema; functional small airway disease; image registration; quantitative computed tomography

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