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European Radiology

, Volume 29, Issue 1, pp 176–185 | Cite as

Low morphometric complexity of emphysematous lesions predicts survival in chronic obstructive pulmonary disease patients

  • Jeongeun Hwang
  • Yeon-Mok Oh
  • Minho Lee
  • Seunghyun Choi
  • Joon Beom Seo
  • Sang Min LeeEmail author
  • Namkug KimEmail author
Computed Tomography
  • 177 Downloads

Abstract

Objectives

To investigate whether morphometric complexity in the lung can predict survival and act as a new prognostic marker in patients with chronic obstructive pulmonary disease (COPD).

Methods

COPD (n = 302) patients were retrospectively reviewed. All patients underwent volumetric computed tomography and pulmonary function tests at enrollment (2005–2015). For complexity analysis, we applied power law exponent of the emphysema size distribution (Dsize) as well as box-counting fractal dimension (Dbox3D) analysis. Patients’ survival at February 2017 was ascertained. Univariate and multivariate Cox proportional hazards analyses were performed, and prediction performances of various combinatorial models were compared.

Results

Patients were 66 ± 6 years old, had 41 ± 28 pack-years’ smoking history and variable GOLD stages (n = 20, 153, 108 and 21 in stages I−IV). The median follow-up time was 6.1 years (range: 0.2−11.6 years). Sixty-three patients (20.9%) died, of whom 35 died of lung-related causes. In univariate Cox analysis, lower Dsize and Dbox3D were significantly associated with both all-cause and lung-related mortality (both p < 0.001). In multivariate analysis, the backward elimination method demonstrated that Dbox3D, along with age and the BODE index, was an independent predictor of survival (p = 0.014; HR, 2.08; 95% CI, 1.16–3.71). The contributions of Dsize and Dbox3D to the combinatorial survival model were comparable with those of the emphysema index and lung-diffusing capacity.

Conclusions

Low morphometric complexity in the lung is a predictor of survival in patients with COPD.

Key Points

A newly suggested method for quantifying lung morphometric complexity is feasible.

Morphometric complexity measured on chest CT images predicts COPD patients’ survival.

Complexity, diffusing capacity and emphysema index contribute similarly to the survival model.

Keywords

COPD Emphysema Fractals Lung Survival 

Abbreviations

BODE

Integrated COPD prognostic index of four factors: the body mass index (B), the degree of airflow obstruction (O) and dyspnoea (D) and exercise capacity (E), measured by the 6-min walk test

cDLCO%

Percentage of diffusing capacity of the lung for carbon monoxide corrected by haemoglobin to the expected value

CI

Confidence interval

C-index

Concordance index

Dbox3D

Box-counting fractal dimension of the lung parenchyma in full-3D

Dsize

Power law exponent of the size distribution of emphysema clusters

EI%

Percentage of the lung volume occupied by emphysema

HR

Hazard ratio

KOLD

Korean obstructive lung disease

LAA

Low attenuation area

PLE

Power law exponent

Notes

Acknowledgements

Dain Eun designed the schematic representations in Fig. 1.

Funding

This study has received funding by the Basic Science Research Program through the National Research Foundation of Korea (NRF-2016R1D1A1A02937317).

Compliance with ethical standards

Guarantor

The scientific guarantor of this publication is Namkug Kim.

Conflict of interest

The authors of this article declare no relationships with any companies whose products or services may be related to the subject matter of the article.

Statistics and biometry

Nayoung Kim kindly provided statistical advice for this manuscript.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

The study subjects are from Korean Obstructive Lung Disease (KOLD) cohort. There are 54 articles by KOLD study group, and more than 100 articles stating the exact phrase “Korean Obstructive Lung Disease”. We suppose that many of those would have a substantial extent of subject overlaps with our current study. However, the key point of our current study was to suggest new imaging biomarkers, and those biomarkers, Dbox and Dsize, have never been measured for any of the subjects before, making our current finding novel.

Methodology

• retrospective

• diagnostic or prognostic study

• multicentre study

Supplementary material

330_2018_5551_MOESM1_ESM.docx (148 kb)
ESM 1 (DOCX 147 kb)

References

  1. 1.
    Labaki WW, Martinez CH, Martinez FJ et al (2017) The role of chest computed tomography in the evaluation and management of the patient with COPD. Am J Respir Crit Care Med 196:1372–1379CrossRefGoogle Scholar
  2. 2.
    Vogelmeier CF, Criner GJ, Martinez FJ et al (2017) Global strategy for the diagnosis, management, and prevention of chronic obstructive lung disease 2017 report: GOLD executive summary (vol 53, pg 128, 2017). Arch Bronconeumol 53:411–412CrossRefGoogle Scholar
  3. 3.
    Anthonisen NR, Wright EC, Hodgkin JE, Hopewell PC, Levin DC, Stevens PM (1986) Prognosis in chronic obstructive pulmonary-disease. Am Rev Respir Dis 133:14–20CrossRefGoogle Scholar
  4. 4.
    Celli BR, Cote CG, Marin JM et al (2004) The body-mass index, airflow obstruction, dyspnea, and exercise capacity index in chronic obstructive pulmonary disease. N Engl J Med 350:1005–1012CrossRefGoogle Scholar
  5. 5.
    Dawkins PA, Dowson LJ, Guest PJ, Stockley RA (2003) Predictors of mortality in alpha(1)-antitrypsin deficiency. Thorax 58:1020–1026CrossRefGoogle Scholar
  6. 6.
    Haruna A, Muro S, Nakano Y et al (2010) CT scan findings of emphysema predict mortality in COPD. Chest 138:635–640CrossRefGoogle Scholar
  7. 7.
    Kessler R, Faller M, Fourgaut G, Mennecier B, Weitzenblum E (1999) Predictive factors of hospitalization for acute exacerbation in a series of 64 patients with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 159:158–164CrossRefGoogle Scholar
  8. 8.
    Martinez FJ, Foster G, Curtis JL et al (2006) Predictors of mortality in patients with emphysema and severe airflow obstruction. Am J Respir Crit Care Med 173:1326–1334CrossRefGoogle Scholar
  9. 9.
    Tanabe N, Muro S, Sato S et al (2012) Longitudinal study of spatially heterogeneous emphysema progression in current smokers with chronic obstructive pulmonary disease. PLoS One 7:e44993Google Scholar
  10. 10.
    Kirby M, Tanabe N, Tan WC et al (2018) Total airway count on computed tomography and the risk of chronic obstructive pulmonary disease progression findings from a population-based study. Am J Respir Crit Care Med 197:56–65CrossRefGoogle Scholar
  11. 11.
    Mishima M, Hirai T, Itoh H et al (1999) Complexity of terminal airspace geometry assessed by lung computed tomography in normal subjects and patients with chronic obstructive pulmonary disease. Proc Natl Acad Sci U S A 96:8829–8834CrossRefGoogle Scholar
  12. 12.
    Grassberger P (1983) On the fractal dimension of the henon attractor. Phys Lett A 97:224–226CrossRefGoogle Scholar
  13. 13.
    Ott E (1993) Chaos in dynamical systems. Cambridge University Press, CambridgeGoogle Scholar
  14. 14.
    Weibel ER (1991) Fractal geometry - a design principle for living organisms. Am J Physiol 261:L361–L369PubMedGoogle Scholar
  15. 15.
    Weibel ER (2009) What makes a good lung? The morphometric basis of lung function. Swiss Med Wkly 139:375–386PubMedGoogle Scholar
  16. 16.
    Mandelbrot B (1983) The fractal geometry of nature. Freeman, New YorkCrossRefGoogle Scholar
  17. 17.
    Weibel ER (2013) It takes more than cells to make a good lung. Am J Respir Crit Care Med 187:342–346CrossRefGoogle Scholar
  18. 18.
    Vuidel G PFaCT Fractal analysis software. research team "Mobilities, city and transport" of the research centre ThéMA., France. Available via http://www.fractalyse.org/. Accessed 31 July 2017
  19. 19.
    Gilliard N, Pappert D, Spragg RG (1995) Fractal analysis of surfactant deposition in rabbit lungs. J Appl Physiol 78:862–866CrossRefGoogle Scholar
  20. 20.
    Glenny R, Robertson HT (1991) Spatial correlation - a corollary of fractal pulmonary perfusion. FASEB J 5:A404–A404Google Scholar
  21. 21.
    Glenny RW, Robertson HT (1990) Fractal properties of pulmonary blood-flow - characterization of spatial heterogeneity. J Appl Physiol 69:532–545CrossRefGoogle Scholar
  22. 22.
    Glenny RW, Robertson HT (1991) Fractal modeling of pulmonary blood-flow heterogeneity. J Appl Physiol 70:1024–1030CrossRefGoogle Scholar
  23. 23.
    Glenny RW, Robertson HT, Yamashiro S, Bassingthwaighte JB (1991) Applications of fractal analysis to physiology. J Appl Physiol 70:2351–2367CrossRefGoogle Scholar
  24. 24.
    Horsfield K (1990) Diameters, generations, and orders of branches in the bronchial tree. J Appl Physiol 68:1089–1097CrossRefGoogle Scholar
  25. 25.
    Park TS, Lee JS, Seo JB et al (2014) Study design and outcomes of korean obstructive lung disease (Kold) cohort study. Tuberc Respir Dis 76:169–174CrossRefGoogle Scholar
  26. 26.
    Hwang J, Lee M, Lee SM et al (2016) A size-based emphysema severity index: robust to the breath-hold-level variations and correlated with clinical parameters. Int J Chron Obstruct Pulmon Dis 11:1835–1841CrossRefGoogle Scholar
  27. 27.
    Lee M KN, Lee SM, Seo JB, Oh SY (2015) Size-based emphysema cluster analysis on low attenuation area in 3D volumetric CT: comparison with pulmonary functional testProc SPIE 9417, Medical Imaging 2015: Biomedical Applications in Molecular, Structural, and Functional Imaging, Orlando, FL, USA, p 91472VGoogle Scholar
  28. 28.
    Cox DR (1972) Regression models and life-tables. J Royal Stat Soc B 34:187–220Google Scholar
  29. 29.
    Wickham H, Francois R, Henry L, Muller K (2017) dplyr: a grammar of data manipulation. R package version 0.7.3. Available via https://CRAN.R-project.org/package=dplyr
  30. 30.
    Therneau TM (2015) A package for survival analysis in S. Available via https://CRAN.R-project.org/package=survival
  31. 31.
    Saha-Chaudhuri PJ, Hapb P (2013) survivalROC: Time-dependent ROC curve estimation from censored survival data. Available via https://CRAN.R-project.org/package=survivalROC
  32. 32.
    Bhatt SP, Bodduluri S, Hoffman EA et al (2017) Computed tomography measure of lung at risk and lung function decline in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 196:569–576CrossRefGoogle Scholar
  33. 33.
    Parr DG (2017) Quantifying the lung at risk in chronic obstructive pulmonary disease does emphysema beget emphysema? Am J Respir Crit Care Med 196:535–536CrossRefGoogle Scholar
  34. 34.
    Ley B, Flicker BM, Hartman TE et al (2014) Idiopathic pulmonary fibrosis: CT and risk of death. Radiology 273:570–579CrossRefGoogle Scholar
  35. 35.
    MacIntyre N, Crapo RO, Viegi G et al (2005) Standardisation of the single-breath determination of carbon monoxide uptake in the lung. Eur Respir J 26:720–735CrossRefGoogle Scholar

Copyright information

© European Society of Radiology 2018

Authors and Affiliations

  1. 1.Asan Institute for Life SciencesAsan Medical CenterSeoulRepublic of Korea
  2. 2.Department of Pulmonary and Critical Care Medicine, University of Ulsan College of MedicineAsan Medical CenterSeoulRepublic of Korea
  3. 3.Department of Convergence Medicine, University of Ulsan College of MedicineAsan Medical CenterSeoulRepublic of Korea
  4. 4.Biomedical Engineering Research Center, Asan Institute of Life SciencesAsan Medical CenterSeoulRepublic of Korea
  5. 5.Department of Radiology and Research Institute of Radiology, University of Ulsan College of MedicineAsan Medical CenterSeoulRepublic of Korea

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