Heterogeneity in pulmonary emphysema: Analysis of CT attenuation using Gaussian mixture model

Purpose To utilize Gaussian mixture model (GMM) for the quantification of chronic obstructive pulmonary disease (COPD) and to evaluate the combined use of multiple types of quantification. Materials and methods Eighty-seven patients (67 men, 20 women; age, 67.4 ± 11.0 years) who had undergone computed tomography (CT) and pulmonary function test (PFT) were included. The heterogeneity of CT attenuation in emphysema (HC) was obtained by analyzing a distribution of CT attenuation with GMM. The percentages of low-attenuation volume in the lungs (LAV), wall area of bronchi (WA), and the cross-sectional area of small pulmonary vessels (CSA) were also calculated. The relationships between COPD quantifications and the PFT results were evaluated by Pearson’s correlation coefficients and through linear models, with the best models selected using Akaike information criterion (AIC). Results The correlation coefficients with FEV1 were as follows: LAV, −0.505; HC, −0.277; CSA, 0.384; WA, –0.196. The correlation coefficients with FEV1/FVC were: LAV, –0.640; HC, –0.136; CSA, 0.288; WA, –0.131. For predicting FEV1, the smallest AIC values were obtained in the model with LAV, HC, CSA, and WA. For predicting FEV1/FVC, the smallest AIC values were obtained in the model with LAV and HC. In both models, the coefficient of HC was statistically significant (P-values = 0.000880 and 0.0441 for FEV1 and FEV1/FVC, respectively). Conclusion GMM was applied to COPD quantification. The results of this study show that COPD severity was associated with HC. In addition, it is shown that the combined use of multiple types of quantification made the evaluation of COPD severity more reliable.

[1]  R. Stockley,et al.  Assessment of pulmonary neutrophilic inflammation in emphysema by quantitative positron emission tomography. , 2012, American journal of respiratory and critical care medicine.

[2]  B. C. Penney,et al.  A Gaussian mixture model for definition of lung tumor volumes in positron emission tomography. , 2007, Medical physics.

[3]  Hayit Greenspan,et al.  Constrained Gaussian mixture model framework for automatic segmentation of MR brain images , 2006, IEEE Transactions on Medical Imaging.

[4]  Grace Parraga,et al.  Using pulmonary imaging to move chronic obstructive pulmonary disease beyond FEV1. , 2014, American journal of respiratory and critical care medicine.

[5]  Takeshi Kubo,et al.  Emphysema distribution and annual changes in pulmonary function in male patients with chronic obstructive pulmonary disease , 2012, Respiratory Research.

[6]  Thomas K. Pilgram,et al.  Quantitative CT assessment of emphysema and airways in relation to lung cancer risk. , 2011, Radiology.

[7]  Katherine P Andriole,et al.  Lung volumes and emphysema in smokers with interstitial lung abnormalities. , 2011, The New England journal of medicine.

[8]  S. Matsumoto,et al.  Paired inspiratory/expiratory volumetric CT and deformable image registration for quantitative and qualitative evaluation of airflow limitation in smokers with or without copd. , 2015, Academic radiology.

[9]  H. Akaike.  A new look at the statistical model identification , 1974 .

[10]  S Kingshighway,et al.  Quantitative CT assessment of emphysema and airways in relation to lung cancer risk , 2012 .

[11]  G. Turino.  COPD and biomarkers: the search goes on , 2008, Thorax.

[12]  F. Martinez,et al.  Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. , 2007, American journal of respiratory and critical care medicine.

[13]  M. Hasegawa,et al.  Airflow limitation and airway dimensions in chronic obstructive pulmonary disease. , 2006, American journal of respiratory and critical care medicine.

[14]  S. Matsumoto,et al.  Emphysema quantification by combining percentage and size distribution of low-attenuation lung regions. , 2014, AJR. American journal of roentgenology.

[15]  Raúl San José Estépar,et al.  Paired inspiratory-expiratory chest CT scans to assess for small airways disease in COPD , 2013, Respiratory Research.

[16]  J. Seo,et al.  Quantitative Assessment of Emphysema, Air Trapping, and Airway Thickening on Computed Tomography , 2008, Lung.

[17]  B. Zheng,et al.  Impact of Emphysema Heterogeneity on Pulmonary Function , 2014, PloS one.

[18]  L. Edwards,et al.  Quantifying the extent of emphysema: factors associated with radiologists' estimations and quantitative indices of emphysema severity using the ECLIPSE cohort. , 2011, Academic radiology.

[19]  Ella A. Kazerooni,et al.  CT-based Biomarker Provides Unique Signature for Diagnosis of COPD Phenotypes and Disease Progression , 2012, Nature Medicine.

[20]  B Suki,et al.  Complexity of terminal airspace geometry assessed by lung computed tomography in normal subjects and patients with chronic obstructive pulmonary disease. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[21]  Raúl San José Estépar,et al.  Quantitative CT measurement of cross-sectional area of small pulmonary vessel in COPD: correlations with emphysema and airflow limitation. , 2010, Academic radiology.

[22]  Tomoki Nakaya,et al.  An Information Statistical Approach to the Modifiable Areal Unit Problem in Incidence Rate Maps , 2000 .

[23]  N. Müller,et al.  "Density mask". An objective method to quantitate emphysema using computed tomography. , 1988, Chest.

[24]  Joyce D. Schroeder,et al.  A Combined Pulmonary-Radiology Workshop for Visual Evaluation of COPD: Study Design, Chest CT Findings and Concordance with Quantitative Evaluation , 2012, COPD.

[25]  B. van Ginneken,et al.  The relationship between lung function impairment and quantitative computed tomography in chronic obstructive pulmonary disease , 2011, European Radiology.

[26]  P. Paré,et al.  Computed tomographic measurements of airway dimensions and emphysema in smokers. Correlation with lung function. , 2000, American journal of respiratory and critical care medicine.