Low morphometric complexity of emphysematous lesions predicts survival in chronic obstructive pulmonary disease patients
- 282 Downloads
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).
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.
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.
Low morphometric complexity in the lung is a predictor of survival in patients with COPD.
• 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.
KeywordsCOPD Emphysema Fractals Lung Survival
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
Percentage of diffusing capacity of the lung for carbon monoxide corrected by haemoglobin to the expected value
Box-counting fractal dimension of the lung parenchyma in full-3D
Power law exponent of the size distribution of emphysema clusters
Percentage of the lung volume occupied by emphysema
Korean obstructive lung disease
Low attenuation area
Power law exponent
Dain Eun designed the schematic representations in Fig. 1.
This study has received funding by the Basic Science Research Program through the National Research Foundation of Korea (NRF-2016R1D1A1A02937317).
Compliance with ethical standards
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.
Written informed consent was obtained from all subjects (patients) in this study.
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.
• diagnostic or prognostic study
• multicentre study
- 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
- 13.Ott E (1993) Chaos in dynamical systems. Cambridge University Press, CambridgeGoogle Scholar
- 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
- 20.Glenny R, Robertson HT (1991) Spatial correlation - a corollary of fractal pulmonary perfusion. FASEB J 5:A404–A404Google Scholar
- 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.Cox DR (1972) Regression models and life-tables. J Royal Stat Soc B 34:187–220Google Scholar
- 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.Therneau TM (2015) A package for survival analysis in S. Available via https://CRAN.R-project.org/package=survival
- 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