. 2019 Jul 15;20(1):153.

doi: 10.1186/s12931-019-1121-z.

Imaging-based clusters in former smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and intermediate outcome measures in COPD study (SPIROMICS)

Babak Haghighi^{1

2}, Sanghun Choi³, Jiwoong Choi^{1

2}, Eric A Hoffman^{4

5

6}, Alejandro P Comellas⁶, John D Newell Jr⁴, Chang Hyun Lee^{4

7}, R Graham Barr⁸, Eugene Bleecker⁹, Christopher B Cooper¹⁰, David Couper¹¹, Mei Lan Han¹², Nadia N Hansel¹³, Richard E Kanner¹⁴, Ella A Kazerooni¹⁵, Eric A C Kleerup¹⁶, Fernando J Martinez¹⁷, Wanda O'Neal¹⁸, Robert Paine 3rd¹⁴, Stephen I Rennard^{19

20}, Benjamin M Smith^{21

22}, Prescott G Woodruff²³, Ching-Long Lin^{24

25

26

27

28}

Affiliations

¹ Department of Mechanical Engineering, University of Iowa, Iowa City, Iowa, USA.
² IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, Iowa, USA.
³ School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea.
⁴ Department of Radiology, University of Iowa, Iowa City, Iowa, USA.
⁵ Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA.
⁶ Department of Internal Medicine, University of Iowa, Iowa City, Iowa, USA.
⁷ Department of Radiology, College of Medicine, Seoul National University, Seoul, Republic of Korea.
⁸ Department of Epidemiology, Mailman School of Public Health, Columbia University Medical School, New York, NY, USA.
⁹ Department of Medicine, The University of Arizona, Tucson, AZ, USA.
¹⁰ Department of Physiology, UCLA, Los Angeles, CA, USA.
¹¹ Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.
¹² Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
¹³ School of Medicine, Johns Hopkins, Baltimore, MD, USA.
¹⁴ School of Medicine, University of Utah, Salt Lake City, UT, USA.
¹⁵ Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
¹⁶ Department of Medicine, UCLA, Los Angeles, CA, USA.
¹⁷ Department of Medicine, Weill Cornell Medical Center, New York, NY, USA.
¹⁸ School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
¹⁹ Department of Internal Medicine, University of Nebraska College of Medicine, Omaha, NE, USA.
²⁰ Clinical Discovery Unit, AstraZeneca, Cambridge, UK.
²¹ Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA.
²² McGill University Health Center Research Institute, Montreal, Canada.
²³ School of Medicine, UCSF, San Francisco, CA, USA.
²⁴ Department of Mechanical Engineering, University of Iowa, Iowa City, Iowa, USA. ching-long-lin@uiowa.edu.
²⁵ IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, Iowa, USA. ching-long-lin@uiowa.edu.
²⁶ Department of Radiology, University of Iowa, Iowa City, Iowa, USA. ching-long-lin@uiowa.edu.
²⁷ Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA. ching-long-lin@uiowa.edu.
²⁸ 2406 Seamans Center for the Engineering Art and Science, Iowa City, Iowa, 52242, USA. ching-long-lin@uiowa.edu.

PMID: 31307479
PMCID: PMC6631615
DOI: 10.1186/s12931-019-1121-z

Imaging-based clusters in former smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and intermediate outcome measures in COPD study (SPIROMICS)

Babak Haghighi et al. Respir Res. 2019.

. 2019 Jul 15;20(1):153.

doi: 10.1186/s12931-019-1121-z.

Authors

Affiliations

¹ Department of Mechanical Engineering, University of Iowa, Iowa City, Iowa, USA.
² IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, Iowa, USA.
³ School of Mechanical Engineering, Kyungpook National University, Daegu, Republic of Korea.
⁴ Department of Radiology, University of Iowa, Iowa City, Iowa, USA.
⁵ Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA.
⁶ Department of Internal Medicine, University of Iowa, Iowa City, Iowa, USA.
⁷ Department of Radiology, College of Medicine, Seoul National University, Seoul, Republic of Korea.
⁸ Department of Epidemiology, Mailman School of Public Health, Columbia University Medical School, New York, NY, USA.
⁹ Department of Medicine, The University of Arizona, Tucson, AZ, USA.
¹⁰ Department of Physiology, UCLA, Los Angeles, CA, USA.
¹¹ Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.
¹² Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA.
¹³ School of Medicine, Johns Hopkins, Baltimore, MD, USA.
¹⁴ School of Medicine, University of Utah, Salt Lake City, UT, USA.
¹⁵ Department of Radiology, University of Michigan, Ann Arbor, MI, USA.
¹⁶ Department of Medicine, UCLA, Los Angeles, CA, USA.
¹⁷ Department of Medicine, Weill Cornell Medical Center, New York, NY, USA.
¹⁸ School of Medicine, University of North Carolina, Chapel Hill, NC, USA.
¹⁹ Department of Internal Medicine, University of Nebraska College of Medicine, Omaha, NE, USA.
²⁰ Clinical Discovery Unit, AstraZeneca, Cambridge, UK.
²¹ Department of Medicine, College of Physicians and Surgeons, Columbia University, New York, NY, 10032, USA.
²² McGill University Health Center Research Institute, Montreal, Canada.
²³ School of Medicine, UCSF, San Francisco, CA, USA.
²⁴ Department of Mechanical Engineering, University of Iowa, Iowa City, Iowa, USA. ching-long-lin@uiowa.edu.
²⁵ IIHR-Hydroscience & Engineering, University of Iowa, Iowa City, Iowa, USA. ching-long-lin@uiowa.edu.
²⁶ Department of Radiology, University of Iowa, Iowa City, Iowa, USA. ching-long-lin@uiowa.edu.
²⁷ Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa, USA. ching-long-lin@uiowa.edu.
²⁸ 2406 Seamans Center for the Engineering Art and Science, Iowa City, Iowa, 52242, USA. ching-long-lin@uiowa.edu.

PMID: 31307479
PMCID: PMC6631615
DOI: 10.1186/s12931-019-1121-z

Abstract

Background: Quantitative computed tomographic (QCT) imaging-based metrics enable to quantify smoking induced disease alterations and to identify imaging-based clusters for current smokers. We aimed to derive clinically meaningful sub-groups of former smokers using dimensional reduction and clustering methods to develop a new way of COPD phenotyping.

Methods: An imaging-based cluster analysis was performed for 406 former smokers with a comprehensive set of imaging metrics including 75 imaging-based metrics. They consisted of structural and functional variables at 10 segmental and 5 lobar locations. The structural variables included lung shape, branching angle, airway-circularity, airway-wall-thickness, airway diameter; the functional variables included regional ventilation, emphysema percentage, functional small airway disease percentage, Jacobian (volume change), anisotropic deformation index (directional preference in volume change), and tissue fractions at inspiration and expiration.

Results: We derived four distinct imaging-based clusters as possible phenotypes with the sizes of 100, 80, 141, and 85, respectively. Cluster 1 subjects were asymptomatic and showed relatively normal airway structure and lung function except airway wall thickening and moderate emphysema. Cluster 2 subjects populated with obese females showed an increase of tissue fraction at inspiration, minimal emphysema, and the lowest progression rate of emphysema. Cluster 3 subjects populated with older males showed small airway narrowing and a decreased tissue fraction at expiration, both indicating air-trapping. Cluster 4 subjects populated with lean males were likely to be severe COPD subjects showing the highest progression rate of emphysema.

Conclusions: QCT imaging-based metrics for former smokers allow for the derivation of statistically stable clusters associated with unique clinical characteristics. This approach helps better categorization of COPD sub-populations; suggesting possible quantitative structural and functional phenotypes.

Keywords: COPD; Emphysema; Former smokers; Functional small airway disease; Imaging-based cluster analysis.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

**Fig. 1**
An expanded set of imaging-based metrics including emphysema percentage, tissue fraction at TLC and RV. a Inspirational image-based local structures: θ, Cr, WT*, and D_h*. b Expiration image-based global and lobar function: AirT%. c Inspiration image-based global and lobar function: Emph%. d Global structure:. e Registration-based global and lobar functions:.

**Fig. 2**
A summary of imaging and clinical characteristics between clusters

**Fig. 3**
a Percentage of emphysema (Emph%) for four clusters and the healthy control group (green). † P > 0.05 between clusters 1, 2, 3 and the healthy group. P < 0.05 between Cluster 4 and other groups for all pairwise comparisons b Percentage of small airway disease (fSAD%) for four clusters and the healthy control group (green). ‡ P < 0.05 for comparisons between four clusters 2, 3, 4 (red) and the healthy group for all pairwise comparison. P > 0.05 for between Cluster 1 and the healthy group

**Fig. 4**
Kernel density estimation (KDE) plots with contour labels based on Emph% and fSAD% for current and former smokers

See this image and copyright information in PMC

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Imaging-based clusters in former smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and intermediate outcome measures in COPD study (SPIROMICS)

Affiliations

Imaging-based clusters in former smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and intermediate outcome measures in COPD study (SPIROMICS)

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Conflict of interest statement

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