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Cancer Epidemiol Biomarkers Prev. 2019 Apr;28(4):724-730. doi: 10.1158/1055-9965.EPI-18-0886. Epub 2019 Jan 14.

Quantitative Imaging Markers of Lung Function in a Smoking Population Distinguish COPD Subgroups with Differential Lung Cancer Risk.

Author information

1
Karmanos Cancer Institute, Detroit, Michigan.
2
Department of Oncology, School of Medicine, Wayne State University, Detroit, Michigan.
3
Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City, Iowa.
4
Department of Radiology, Karmanos Cancer Institute, Detroit, Michigan.
5
Department of Public Health Sciences, Henry Ford Health System, Detroit, Michigan.
6
Josephine Ford Cancer Institute, Henry Ford Health System, Detroit, Michigan.
7
Department of Radiology, Henry Ford Health System, Detroit, Michigan.
8
Division of Pulmonary and Critical Care Medicine, Henry Ford Health System, Detroit, Michigan.
9
Department of Internal Medicine, School of Medicine, Wayne State University, Detroit, Michigan.
10
Comprehensive Cancer Center, University of Michigan, Ann Arbor, Michigan.
11
Karmanos Cancer Institute, Detroit, Michigan. schwarta@karmanos.org.

Abstract

BACKGROUND:

Chronic obstructive pulmonary disease (COPD) is a heterogeneous condition with respect to onset, progression, and response to therapy. Incorporating clinical- and imaging-based features to refine COPD phenotypes provides valuable information beyond that obtained from traditional clinical evaluations. We characterized the spectrum of COPD-related phenotypes in a sample of former and current smokers and evaluated how these subgroups differ with respect to sociodemographic characteristics, COPD-related comorbidities, and subsequent risk of lung cancer.

METHODS:

White (N = 659) and African American (N = 520) male and female participants without lung cancer (controls) in the INHALE study who completed a chest CT scan, interview, and spirometry test were used to define distinct COPD-related subgroups based on hierarchical clustering. Seven variables were used to define clusters: pack years, quit years, FEV1/FVC, % predicted FEV1, and from quantitative CT (qCT) imaging, % emphysema, % air trapping, and mean lung density ratio. Cluster definitions were then applied to INHALE lung cancer cases (N = 576) to evaluate lung cancer risk.

RESULTS:

Five clusters were identified that differed significantly with respect to sociodemographic (e.g., race, age) and clinical (e.g., BMI, limitations due to breathing difficulties) characteristics. Increased risk of lung cancer was associated with increasingly detrimental lung function clusters (when ordered from most detrimental to least detrimental).

CONCLUSIONS:

Measures of lung function vary considerably among smokers and are not fully explained by smoking intensity.

IMPACT:

Combining clinical (spirometry) and radiologic (qCT) measures of COPD defines a spectrum of lung disease that predicts lung cancer risk differentially among patient clusters.

PMID:
30642838
PMCID:
PMC6449213
[Available on 2020-04-01]
DOI:
10.1158/1055-9965.EPI-18-0886

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