Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
Comparative Study
. 2025 Apr 28:20:1279-1286.
doi: 10.2147/COPD.S505092. eCollection 2025.

Imaging Phenotypes Assessment by Using Quantitative Parameters for CT-Defined Subtypes of Chronic Obstructive Pulmonary Disease

Wufei Chen #  1 , Jing Zhu #  2   3 , Jixiang Ni  2   3 , Xiang Li  4 , Yu Li  2   3 , Wen Yin  2   3 , Shan Zhu  2   3 , Yang Xiao  2   3 , Ying Wang  2   3 , Hui Huang  2   3 , Yi Hu  2   3
Affiliations
Comparative Study

Imaging Phenotypes Assessment by Using Quantitative Parameters for CT-Defined Subtypes of Chronic Obstructive Pulmonary Disease

Wufei Chen et al. Int J Chron Obstruct Pulmon Dis. .

Abstract

Purpose: To explore the quantitative imaging phenotype differences for CT-defined subtypes classified by the Fleischner Society in patients with chronic obstructive pulmonary disease (COPD).

Patients and methods: A total of 228 COPD patients who underwent non-enhanced chest CT screening from 2018 to 2024 were included. All patients were divided into type-A (Absent emphysema that no or mild emphysema, Goddard score ≤8, regardless of bronchial wall thickening), type-E (Emphysema that significant emphysema, Goddard score >8, without bronchial wall thickening), and type-M (Mixed emphysema and bronchial wall thickening that both significant emphysema, Goddard score >8, and bronchial wall thickening ≥ grade 1 in ≥1 lung lobe). Imaging phenotype parameters included lung airspace analysis (LAA) and LAA size analysis (LAASA) in emphysema, airway wall, lung vessels and interstitial lung disease (ILD) extracted by a COPD-specific analysis software were analysis among three groups.

Results: Quantitative assessment of emphysema among three image phenotypes showed significant differences in full emphysema and full emphysema ratio based on LAA among three groups (P < 0.05). The areas of consolidation, ground-glass opacity, and reticular patterns were significantly larger in type-M than the other two types (P < 0.05). Quantitative assessment of small airways disease and small vessel parameters found smaller lumen-volume and larger wall-volume in whole lung level in the emphysema phenotype of type-M (P < 0.05) were found in the small vessel count in distance of 6 mm and 9mm from the pleura were significant differences among three groups (P < 0.05). The multivariate logistic regression analysis showed that the higher proportion of full emphysema ratio and wall-volume, a proportion of smaller lumen-volume, and a more noticeable interstitial lung alterations were associated with type-M.

Conclusion: A quantitative CT evaluation can further delineate the imaging phenotypes characteristics thereby in guiding to early diagnosis, severity assessment, and therapeutic recommendations in COPD patients.

Keywords: chronic obstructive pulmonary disease; computed tomography; image phenotypes; quantitative analysis.

PubMed Disclaimer

Conflict of interest statement

The authors report no conflicts of interest in this work.

Figures

Figure 1
Figure 1
Inspiratory LAA and LAAsize Analysis for a 69-year-old man who belonged to type-A. (A and D) LAA&LAAsize coronal pseudo-color image, The default threshold is −950 hounsfield Units (HU); (B) LAA three-dimensional image; (C and F) LAA&LAAsize Chart to show the volume, LAA% and D-slope. (E) LAA Size Log-Log to show the cluster diameter.
Figure 2
Figure 2
The predictive performance of LAA and Goddard score for type-M classification (0.616 vs 0.739, P=0.002).
Figure 3
Figure 3
Multivariate logistic regression analysis of quantitative phenotypes parameters.
See this image and copyright information in PMC

References

    1. Pulik K, Mycroft K, Korczynski P, Ciechanowicz AK, Gorska K. Metabolomic analysis of respiratory epithelial lining fluid in patients with chronic obstructive pulmonary disease-a systematic review. Cells-Basel. 2023;12(6). doi: 10.3390/cells12060833 - DOI - PMC - PubMed
    1. Nikolaou V, Massaro S, Fakhimi M, Stergioulas L, Price D. COPD phenotypes and machine learning cluster analysis: a systematic review and future research agenda. Resp Med. 2020;171:106093. doi: 10.1016/j.rmed.2020.106093 - DOI - PubMed
    1. Lange P, Ahmed E, Lahmar ZM, Martinez FJ, Bourdin A. Natural history and mechanisms of COPD. Respirology. 2021;26(4):298–321. doi: 10.1111/resp.14007 - DOI - PubMed
    1. Lynch DA, Austin JH, Hogg JC, et al. CT-definable subtypes of chronic obstructive pulmonary disease: a statement of the Fleischner society. Radiology. 2015;277(1):192–205. doi: 10.1148/radiol.2015141579 - DOI - PMC - PubMed
    1. Ohno Y, Seo JB, Parraga G, et al. Pulmonary functional imaging: part 1-state-of-the-art technical and physiologic underpinnings. Radiology. 2021;299(3):508–523. doi: 10.1148/radiol.2021203711 - DOI - PMC - PubMed

Publication types

MeSH terms