Subtyping COPD by Using Visual and Quantitative CT Imaging Features.


Journal

Chest
ISSN: 1931-3543
Titre abrégé: Chest
Pays: United States
ID NLM: 0231335

Informations de publication

Date de publication:
01 2020
Historique:
received: 07 02 2019
revised: 06 06 2019
accepted: 10 06 2019
pubmed: 10 7 2019
medline: 22 7 2020
entrez: 9 7 2019
Statut: ppublish

Résumé

Multiple studies have identified COPD subtypes by using visual or quantitative evaluation of CT images. However, there has been no systematic assessment of a combined visual and quantitative CT imaging classification. We integrated visually defined patterns of emphysema with quantitative imaging features and spirometry data to produce a set of 10 nonoverlapping CT imaging subtypes, and we assessed differences between subtypes in demographic features, physiological characteristics, longitudinal disease progression, and mortality. We evaluated 9,080 current and former smokers in the COPDGene study who had available volumetric inspiratory and expiratory CT images obtained using a standardized imaging protocol. We defined 10 discrete, nonoverlapping CT imaging subtypes: no CT imaging abnormality, paraseptal emphysema (PSE), bronchial disease, small airway disease, mild emphysema, upper lobe predominant centrilobular emphysema (CLE), lower lobe predominant CLE, diffuse CLE, visual without quantitative emphysema, and quantitative without visual emphysema. Baseline and 5-year longitudinal characteristics and mortality were compared across these CT imaging subtypes. The overall mortality differed significantly between groups (P < .01) and was highest in the 3 moderate to severe CLE groups. Subjects having quantitative but not visual emphysema and subjects with visual but not quantitative emphysema were unique groups with mild COPD, at risk for progression, and with likely different underlying mechanisms. Subjects with PSE and/or moderate to severe CLE had substantial progression of emphysema over 5 years compared with findings in subjects with no CT imaging abnormality (P < .01). The combination of visual and quantitative CT imaging features reflects different underlying pathological processes in the heterogeneous COPD syndrome and provides a useful approach to reclassify types of COPD. ClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov.

Sections du résumé

BACKGROUND
Multiple studies have identified COPD subtypes by using visual or quantitative evaluation of CT images. However, there has been no systematic assessment of a combined visual and quantitative CT imaging classification. We integrated visually defined patterns of emphysema with quantitative imaging features and spirometry data to produce a set of 10 nonoverlapping CT imaging subtypes, and we assessed differences between subtypes in demographic features, physiological characteristics, longitudinal disease progression, and mortality.
METHODS
We evaluated 9,080 current and former smokers in the COPDGene study who had available volumetric inspiratory and expiratory CT images obtained using a standardized imaging protocol. We defined 10 discrete, nonoverlapping CT imaging subtypes: no CT imaging abnormality, paraseptal emphysema (PSE), bronchial disease, small airway disease, mild emphysema, upper lobe predominant centrilobular emphysema (CLE), lower lobe predominant CLE, diffuse CLE, visual without quantitative emphysema, and quantitative without visual emphysema. Baseline and 5-year longitudinal characteristics and mortality were compared across these CT imaging subtypes.
RESULTS
The overall mortality differed significantly between groups (P < .01) and was highest in the 3 moderate to severe CLE groups. Subjects having quantitative but not visual emphysema and subjects with visual but not quantitative emphysema were unique groups with mild COPD, at risk for progression, and with likely different underlying mechanisms. Subjects with PSE and/or moderate to severe CLE had substantial progression of emphysema over 5 years compared with findings in subjects with no CT imaging abnormality (P < .01).
CONCLUSIONS
The combination of visual and quantitative CT imaging features reflects different underlying pathological processes in the heterogeneous COPD syndrome and provides a useful approach to reclassify types of COPD.
TRIAL REGISTRY
ClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov.

Identifiants

pubmed: 31283919
pii: S0012-3692(19)31255-3
doi: 10.1016/j.chest.2019.06.015
pmc: PMC6965698
pii:
doi:

Banques de données

ClinicalTrials.gov
['NCT00608764']

Types de publication

Journal Article Multicenter Study Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

47-60

Subventions

Organisme : NHLBI NIH HHS
ID : U01 HL089897
Pays : United States

Commentaires et corrections

Type : CommentIn

Informations de copyright

Copyright © 2019 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

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Auteurs

Jinkyeong Park (J)

Channing Division of Network Medicine, Boston, MA; Department of Internal Medicine, Dongguk University Ilsan Hospital, Goyang-Si, Gyeonggi-do, South Korea.

Brian D Hobbs (BD)

Channing Division of Network Medicine, Boston, MA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA.

James D Crapo (JD)

Department of Medicine, National Jewish Health, Denver, CO.

Barry J Make (BJ)

Department of Medicine, National Jewish Health, Denver, CO.

Elizabeth A Regan (EA)

Department of Medicine, National Jewish Health, Denver, CO.

Stephen Humphries (S)

Department of Radiology, National Jewish Health, Denver, CO.

Vincent J Carey (VJ)

Channing Division of Network Medicine, Boston, MA.

David A Lynch (DA)

Department of Radiology, National Jewish Health, Denver, CO.

Edwin K Silverman (EK)

Channing Division of Network Medicine, Boston, MA; Division of Pulmonary and Critical Care Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA. Electronic address: ed.silverman@channing.harvard.edu.

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Classifications MeSH