Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Significance of Low-Attenuation Cluster Analysis on Quantitative CT in the Evaluation of Chronic Obstructive Pulmonary Disease

  • Nambu, Atsushi (Department of Radiology, National Jewish Health) ;
  • Zach, Jordan (Department of Radiology, National Jewish Health) ;
  • Kim, Song Soo (Department of Radiology, National Jewish Health) ;
  • Jin, Gongyoung (Department of Radiology, National Jewish Health) ;
  • Schroeder, Joyce (Department of Radiology, National Jewish Health) ;
  • Kim, Yu-Il (Department of Medicine, National Jewish Health) ;
  • Bowler, Russell (Division of Pulmonary Medicine, Department of Medicine, National Jewish Health) ;
  • Lynch, David A (Department of Radiology, National Jewish Health)
  • Received : 2017.02.27
  • Accepted : 2017.07.02
  • Published : 2018.02.01
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Abstract

Objective: To assess clinical feasibility of low-attenuation cluster analysis in evaluation of chronic obstructive pulmonary disease (COPD). Materials and Methods: Subjects were 199 current and former cigarette smokers that underwent CT for quantification of COPD and had physiological measurements. Quantitative CT (QCT) measurements included low-attenuation area percent (LAA%) (voxels ${\leq}-950$ Hounsfield unit [HU]), and two-dimensional (2D) and three-dimensional D values of cluster analysis at three different thresholds of CT value (-856, -910, and -950 HU). Correlation coefficients between QCT measurements and physiological indices were calculated. Multivariable analyses for percentage of predicted forced expiratory volume at one second (%FEV1) was performed including sex, age, body mass index, LAA%, and D value had the highest correlation coefficient with %FEV1 as independent variables. These analyses were conducted in subjects including those with mild COPD (global initiative of chronic obstructive lung disease stage = 0-II). Results: LAA% had a higher correlation coefficient (-0.549, p < 0.001) with %FEV1 than D values in subjects while $2D\;D_{-910HU}$ (-0.350, p < 0.001) revealed slightly higher correlation coefficient than LAA% (-0.343, p < 0.001) in subjects with mild COPD. Multivariable analyses revealed that LAA% and $2D\;D\;value_{-910HU}$ were significant independent predictors of %FEV1 in subjects and that only $2D\;D\;value_{-910HU}$ revealed a marginal p value (0.05) among independent variables in subjects with mild COPD. Conclusion: Low-attenuation cluster analysis provides incremental information regarding physiologic severity of COPD, independent of LAA%, especially with mild COPD.

Keywords

References

  1. Lynch DA, Al-Qaisi MA. Quantitative computed tomography in chronic obstructive pulmonary disease. J Thorac Imaging 2013;28:284-290 https://doi.org/10.1097/RTI.0b013e318298733c
  2. Ostridge K, Wilkinson TM. Present and future utility of computed tomography scanning in the assessment and management of COPD. Eur Respir J 2016;48:216-228 https://doi.org/10.1183/13993003.00041-2016
  3. Gould GA, Redpath AT, Ryan M, Warren PM, Best JJ, Flenley DC, et al. Lung CT density correlates with measurements of airflow limitation and the diffusing capacity. Eur Respir J 1991;4:141-146
  4. Nakano Y, Muro S, Sakai H, Hirai T, Chin K, Tsukino M, et al. Computed tomographic measurements of airway dimensions and emphysema in smokers. Correlation with lung function. Am J Respir Crit Care Med 2000;162(3 Pt 1):1102-1108 https://doi.org/10.1164/ajrccm.162.3.9907120
  5. Sandek K, Bratel T, Lagerstrand L, Rosell H. Relationship between lung function, ventilation-perfusion inequality and extent of emphysema as assessed by high-resolution computed tomography. Respir Med 2002;96:934-943 https://doi.org/10.1053/rmed.2002.1371
  6. Aziz ZA, Wells AU, Desai SR, Ellis SM, Walker AE, MacDonald S, et al. Functional impairment in emphysema: contribution of airway abnormalities and distribution of parenchymal disease. AJR Am J Roentgenol 2005;185:1509-1515 https://doi.org/10.2214/AJR.04.1578
  7. Grydeland TB, Thorsen E, Dirksen A, Jensen R, Coxson HO, Pillai SG, et al. Quantitative CT measures of emphysema and airway wall thickness are related to D(L)CO. Respir Med 2011;105:343-351 https://doi.org/10.1016/j.rmed.2010.10.018
  8. Mohamed Hoesein FA, de Hoop B, Zanen P, Gietema H, Kruitwagen CL, van Ginneken B, et al. CT-quantified emphysema in male heavy smokers: association with lung function decline. Thorax 2011;66:782-787 https://doi.org/10.1136/thx.2010.145995
  9. Mets OM, Murphy K, Zanen P, Gietema HA, Lammers JW, van Ginneken B, et al. The relationship between lung function impairment and quantitative computed tomography in chronic obstructive pulmonary disease. Eur Radiol 2012;22:120-128 https://doi.org/10.1007/s00330-011-2237-9
  10. Washko GR, Criner GJ, Mohsenifar Z, Sciurba FC, Sharafkhaneh A, Make BJ, et al. Computed tomographic-based quantification of emphysema and correlation to pulmonary function and mechanics. COPD 2008;5:177-186 https://doi.org/10.1080/15412550802093025
  11. Müller NL, Staples CA, Miller RR, Abboud RT. "Density mask." An objective method to quantitate emphysema using computed tomography. Chest 1988;94:782-787 https://doi.org/10.1378/chest.94.4.782
  12. Gevenois PA, De Vuyst P, de Maertelaer V, Zanen J, Jacobovitz D, Cosio MG, et al. Comparison of computed density and microscopic morphometry in pulmonary emphysema. Am J Respir Crit Care Med 1996;154:187-192 https://doi.org/10.1164/ajrccm.154.1.8680679
  13. Madani A, Zanen J, de Maertelaer V, Gevenois PA. Pulmonary emphysema: objective quantification at multi-detector row CT- -comparison with macroscopic and microscopic morphometry. Radiology 2006;238:1036-1043 https://doi.org/10.1148/radiol.2382042196
  14. Haruna A, Muro S, Nakano Y, Ohara T, Hoshino Y, Ogawa E, et al. CT scan findings of emphysema predict mortality in COPD. Chest 2010;138:635-640 https://doi.org/10.1378/chest.09-2836
  15. Diaz AA, Bartholmai B, San Jose Estepar R, Ross J, Matsuoka S, Yamashiro T, et al. Relationship of emphysema and airway disease assessed by CT to exercise capacity in COPD. Respir Med 2010;104:1145-1151 https://doi.org/10.1016/j.rmed.2010.02.023
  16. Han MK, Kazerooni EA, Lynch DA, Liu LX, Murray S, Curtis JL, et al. Chronic obstructive pulmonary disease exacerbations in the COPDGene study: associated radiologic phenotypes. Radiology 2011;261:274-282 https://doi.org/10.1148/radiol.11110173
  17. Grydeland TB, Dirksen A, Coxson HO, Pillai SG, Sharma S, Eide GE, et al. Quantitative computed tomography: emphysema and airway wall thickness by sex, age and smoking. Eur Respir J 2009;34:858-865 https://doi.org/10.1183/09031936.00167908
  18. Ashraf H, Lo P, Shaker SB, de Bruijne M, Dirksen A, Tonnesen P, et al. Short-term effect of changes in smoking behaviour on emphysema quantification by CT. Thorax 2011;66:55-60 https://doi.org/10.1136/thx.2009.132688
  19. Shaker SB, Stavngaard T, Laursen LC, Stoel BC, Dirksen A. Rapid fall in lung density following smoking cessation in COPD. COPD 2011;8:2-7 https://doi.org/10.3109/15412555.2010.541306
  20. Mishima M, Hirai T, Itoh H, Nakano Y, Sakai H, Muro S, et al. 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 1999;96:8829-8834 https://doi.org/10.1073/pnas.96.16.8829
  21. Gietema HA, Müller NL, Fauerbach PV, Sharma S, Edwards LD, Camp PG, et al. Quantifying the extent of emphysema: factors associated with radiologists’ estimations and quantitative indices of emphysema severity using the ECLIPSE cohort. Acad Radiol 2011;18:661-671 https://doi.org/10.1016/j.acra.2011.01.011
  22. Mitsunobu F, Ashida K, Hosaki Y, Tsugeno H, Okamoto M, Nishida K, et al. Complexity of terminal airspace geometry assessed by computed tomography in asthma. Am J Respir Crit Care Med 2003;167:411-417 https://doi.org/10.1164/rccm.2112070
  23. Coxson HO, Whittall KP, Nakano Y, Rogers RM, Sciurba FC, Keenan RJ, et al. Selection of patients for lung volume reduction surgery using a power law analysis of the computed tomographic scan. Thorax 2003;58:510-514 https://doi.org/10.1136/thorax.58.6.510
  24. Madani A, Van Muylem A, de Maertelaer V, Zanen J, Gevenois PA. Pulmonary emphysema: size distribution of emphysematous spaces on multidetector CT images-- comparison with macroscopic and microscopic morphometry. Radiology 2008;248:1036-1041 https://doi.org/10.1148/radiol.2483071434
  25. Tanabe N, Muro S, Hirai T, Oguma T, Terada K, Marumo S, et al. Impact of exacerbations on emphysema progression in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 2011;183:1653-1659 https://doi.org/10.1164/rccm.201009-1535OC
  26. Regan EA, Hokanson JE, Murphy JR, Make B, Lynch DA, Beaty TH, et al. Genetic epidemiology of COPD (COPDGene) study design. COPD 2010;7:32-43
  27. Wanger J, Clausen JL, Coates A, Pedersen OF, Brusasco V, Burgos F, et al. Standardisation of the measurement of lung volumes. Eur Respir J 2005;26:511-522 https://doi.org/10.1183/09031936.05.00035005
  28. Macintyre N, Crapo RO, Viegi G, Johnson DC, van der Grinten CP, Brusasco V, et al. Standardisation of the single-breath determination of carbon monoxide uptake in the lung. Eur Respir J 2005;26:720-735 https://doi.org/10.1183/09031936.05.00034905
  29. Zach JA, Newell JD Jr, Schroeder J, Murphy JR, Curran-Everett D, Hoffman EA, et al. Quantitative computed tomography of the lungs and airways in healthy nonsmoking adults. Invest Radiol 2012;47:596-602 https://doi.org/10.1097/RLI.0b013e318262292e

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