Journal of Thoracic Imaging:
Symposium Review Articles
Quantitative Computed Tomography in Chronic Obstructive Pulmonary Disease
Lynch, David A. MD; Al-Qaisi, Mustafa A. MD
Division of Radiology, National Jewish Health, Denver, CO
Supported by US National Institutes of Health (NIH) COPDGene study, Award Numbers R01HL089897 and R01HL089856 from the National Heart, Lung, And Blood Institute (NHLBI). The COPDGene project is also supported by the COPD Foundation through contributions made to an Industry Advisory Board comprising AstraZeneca, Boehringer Ingelheim, Novartis, Pfizer, Siemens, and Sunovion. Dr Lynch’s institution and laboratory receives research support from the NHLBI, Siemens Inc., Perceptive Imaging Inc., and Centocor Inc.
Dr Lynch is a consultant to Perceptive Imaging Inc., Boehringer Ingelheim Inc., Genentech Inc., Gilead Inc., and Intermune Inc. The remaining author declares no conflicts of interest.
Reprints: David A. Lynch, MD, Division of Radiology, National Jewish Health, 1400 Jackson Street, Denver, CO 80206 (e-mail: email@example.com).
Quantitative computed tomography is being increasingly used to quantify the features of chronic obstructive pulmonary disease, specifically emphysema, air trapping, and airway abnormality. For quantification of emphysema, the density mask technique is most widely used, with threshold on the order of-950 HU, but percentile cutoff may be less sensitive to volume changes. Sources of variation include depth of inspiration, scanner make and model, technical parameters, and cigarette smoking. On expiratory computed tomography (CT), air trapping may be quantified by evaluating the percentage of lung volume less than a given threshold (eg, −856 HU) by comparing lung volumes and attenuation on expiration and inspiration or, as done more recently, by coregistering inspiratory and expiratory CT scans. All of these indices correlate well with the severity of physiological airway obstruction. By constructing a 3-dimensional model of the airway from volumetric CT, it is possible to measure dimensions (external and internal diameters and airway wall thickness) of segmental and subsegmental airways orthogonal to their long axes. Measurement of airway parameters correlates with the severity of airflow obstruction and with the history of chronic obstructive pulmonary disease exacerbation.
Chronic obstructive pulmonary disease (COPD) is now the third most common cause of death in the United States (after heart disease and malignancy).1 Unlike heart disease and malignancy, mortality from COPD has increased progressively over the last 10 years. In its 2013 update, the Global Initiative for Obstructive Lung Disease (GOLD) group defines COPD as follows: “COPD, a common preventable and treatable disease, is characterized by persistent airflow limitation that is usually progressive and associated with an enhanced chronic inflammatory response in the airways and lung to noxious particles or gases. Exacerbations and comorbidities contribute to the overall severity in individual patients.”2 The diagnosis of COPD is made on spirometry on the basis of the presence of a postbronchodilator FEV1/FVC ratio <0.70. The severity of COPD is commonly classified according to the GOLD staging system, wherein GOLD 1 is defined as FEV1≥80% of that predicted, GOLD 2 is FEV1 50% to 80% of that predicted, GOLD 3 is FEV1 30% to 50% of that predicted, and GOLD 4 is FEV1<30% of that predicted. More recently, the GOLD groupings have been further subdivided on the basis of symptom severity to provide an index of exacerbation risk.2 Although COPD is most commonly related to tobacco smoking, other risk factors include outdoor air pollution, occupational exposure, and indoor exposure to burning wood and other biomass fuels.2
Although COPD is a convenient umbrella term, its routine use obscures the fact that the morphologic manifestations of this group of obstructive diseases vary widely, a fact that is readily apparent to the clinical radiologist. Individuals with similar levels of physiological impairment may have substantial, little, or no emphysema. Discrete subphenotypes of COPD include emphysema of varying morphologic appearance, large airway abnormality, and small airway obstruction. Increasing awareness of the heterogeneity of COPD has led to increased use of computed tomography (CT) to characterize COPD for purposes of genetic evaluation and identification of specific subgroups that may be amenable to therapeutic trials. Although visual assessment is important in determining the presence and characteristics of emphysema, there is increasing interest in the use of quantitative imaging to provide more precise estimates of the severity and distribution of emphysema, gas trapping, and airway wall thickening. Several large cohorts of cigarette smokers have now been quite extensively characterized by CT, resulting in increased knowledge of the clinical correlates of quantitative computed tomography parameters.3–5 The purpose of this paper is to present current knowledge on the use of QCT for assessment of emphysema, gas trapping, and airway abnormality that would contribute to the clinical syndrome of COPD. The most optimal CT acquisition techniques for COPD have been covered elsewhere in this symposium.6
Emphysematous lung destruction results in replacement of the normal lung (which has a typical attenuation of about −850 HU on inspiratory CT) with air-containing spaces, with CT attenuation close to −1000 HU. From the early days of CT, it was apparent that measurement of CT attenuation values could help quantify the extent of emphysema. Müller et al7 were the first to describe and validate pathologically the density mask technique, in which CT pixels with attenuation below a certain threshold value (initially −910 HU) were defined as areas of emphysema. Using a different approach, Coxson et al8 used CT to evaluate lung weight, gas and tissue volume, and estimated lung surface to volume ratios and surface area and confirmed that these measures correlated with the histologic extent of emphysema. Bankier et al9 demonstrated that QCT measurements correlated better with macroscopic measurements of emphysema compared with visual CT scoring. More recent evaluation with thin-section CT using multidetector scanners showed that the highest correlation between QCT metrics and histology is found when the CT threshold is set at −960 or −970 HU.10 However, in the interests of balancing sensitivity and specificity, the threshold of −950 HU is now more commonly used5,11,12 (Figs. 1A, B). An alternative approach to emphysema quantification, based on the frequency histogram of lung attenuation, evaluates the CT attenuation at a given percentile along the histogram (eg, the first or the 15th percentile).12 There is some evidence that the percentile approach is more robust for longitudinal evaluation of emphysema and less sensitive to changes in lung volume.13–15 Histologic correlation has shown that the optimal percentile value for this determination, measured on multidetector CT, is the first percentile.10 However, because of concern regarding the presence of artifacts from image noise and truncation artifacts at the first percentile level, most studies have used the 15th percentile threshold.15,16
It is important to remember that measurement of % low-attenuation areas (%LAA), although correlating moderately well with the histologic severity of emphysema, is not a direct measurement of emphysema. The term “% emphysema” is widely used to refer to such CT measurements but is imprecise and may give rise to confusion. The annotation %LAA−950 (or %LAA−910, etc.) is preferred because it is more precise. The term RA950 is also used sometimes to indicate the relative area of the lung <−950 HU.
As emphysema is a regionally distributed disease, it makes sense to determine the zonal or lobar distribution of emphysema. Most available QCT methods can divide each lung into upper, mid, and lower zones of equal height or volume, and ratios between upper and lower lung LAA measurements can be computed. Newer methods can also permit segmentation of lobes to compute lobar volumes and the extent of low-attenuation areas.
To quantify the size of emphysematous spaces (Fig. 1C), several investigators have used the D value or α value. This represents the slope of the log-log plot of the cumulative frequency-size distributions of the %LAA−950. Mishima et al17 found that smokers with normal %LAA had lower D values compared with nonsmokers, suggesting that the D value might be a sensitive method for detecting early emphysema. However, Madani et al18 found that the D value did not correlate with macroscopic or microscopic indices of emphysema.
While evaluating subjects with emphysema, one must minimize the sources of variation. The major sources of variation in the quantification of emphysema include variation in lung volume, technical CT parameters, and cigarette smoking. Madani et al19 showed that measures of emphysema changed significantly when scans were obtained at 100%, 90%, 80%, 70%, and 50% of vital capacity. However, the change between 100% and 90% of vital capacity was relatively slight. Although several studies have used spirometers to standardize the inspired lung volume,20,21 such systems are not widely available, and the strong physiological correlations obtained without lung volume control suggest that it may not be necessary. In the absence of a spirometer, careful coaching of the patient by the technologist is important to achieve total lung capacity.
Madani et al22 also showed in a different study that the %LAA−960 decreases with increasing slice thickness and increasing tube current. Boedeker et al23 showed that differences in the reconstruction algorithm have a large effect on the measurement of low-attenuation areas. In particular, the use of overenhancing reconstruction algorithms resulted in a shift of 9.4% in CT measurements of emphysema, presumably because of increased image noise simulating emphysema. For this reason, a smooth reconstruction algorithm is generally used in QCT evaluation of emphysema. The substantial variation in CT emphysema measures with technical factors emphasizes the importance of using a standardized acquisition technique. Use of the same technique is particularly important in longitudinal studies.
Several authors have shown that current smokers appear to have lower levels of emphysema compared with former smokers.24,25 Even more intriguingly, the extent of “emphysema” appears to increase quite rapidly after smoking cessation, reflecting a fall in lung attenuation.26,27 This effect is presumed to be due to a smoking-induced increase in inflammatory cells in the lung in current smokers, resulting in increased lung attenuation, so that partial volume averaging masks the areas of low-attenuation emphysema. Therefore, smoking status should always be taken into account when assessing the severity of emphysema by QCT.
Several studies have evaluated the ability of CT to detect progression of emphysema on longitudinal evaluation. The main source of variation is related to changes in lung volume. Correction for lung volume may be performed using a sponge model, in which the % emphysema on the follow-up scan is corrected using the achieved lung volume on the baseline scan.28 Correction for lung volume reduces the variability in emphysema quantification by a factor of 2.29 In a randomized controlled study of subjects with α-1 antitrypsin deficiency, change in the 15th percentile CT density (corrected for lung volume) was found to be more sensitive as an index of progression compared with measures of physiology or health status.30 The combined analysis of 2 clinical trials on intravenous α-1 antitrypsin augmentation showed that this medication significantly reduces the decline in lung density in subjects with α-1 antitrypsin deficiency.31
Gietema and colleagues evaluated 157 subjects with smoking-related emphysema enrolled in a lung cancer screening study who had undergone repeat CT scans within 3 months and found that the limits of agreement for %LAA<−950 HU were from −1.3% to +1.1%, suggesting that CT can detect a change of 1.1% in the extent of emphysema with 95% probability. Recently, Coxson et al5 found an average annual decline in lung density of 1.13 g/L after correction for lung volume in a group of 1928 current and former smokers. The decline was more rapid in women than in men and in current smokers than in former smokers.
End-expiratory CT, whether obtained at functional residual capacity or at residual volume, is an excellent way to assess gas trapping in COPD. Most studies have evaluated the presence of gas trapping by evaluating the % low attenuation at a threshold of −856 or −850 HU (LAAexp856 or LAAexp850) (Figs. 1D, E). This value is chosen because it is the attenuation of a normally inflated inspiratory lung; hence, the concept has been that expiratory lung should have higher attenuation than this. Murphy et al32 in a study on 216 cigarette smokers showed that LAAexp850 provided remarkably high correlations (r=0.85 to 0.90) with the FEV1/FVC ratio and with the predicted FEV1%. Schroeder et al33 found similar levels of correlation in a study on 4062 COPDGene subjects with and without COPD. QCT evaluation of the severity of emphysema and expiratory gas trapping provides a simple way to assign individual COPD subjects to subgroups characterized by predominant emphysema, mixed emphysema and gas trapping, and predominant gas trapping (Fig. 2).
Other authors have used other indices of gas trapping, including the ratio of inspiratory to expiratory lung volume, inspiratory to expiratory lung attenuation ratio, and the expiratory to inspiratory relative volume change of voxels with attenuation values between −860 and −950. Mets et al34 found that the inspiratory to expiratory lung attenuation ratio provided the strongest correlation with physiological air trapping. The same group studied 1140 subjects enrolled in a lung cancer screening study and found that a diagnostic model that included LAA−950insp, ratio of inspiratory to expiratory lung volume, body mass index, smoking pack-years, and smoking status permitted the accurate diagnosis of COPD.35 As screening CT becomes more widely implemented for lung cancer detection, it is possible that routine expiratory CT may become part of the algorithm to permit detection of unrecognized COPD.
One of the challenges in the evaluation of expiratory gas trapping in COPD is that a simple threshold measurement does not distinguish between gas trapping due to emphysema and small airway disease. Several authors have used deformation techniques to register the inspiration image to the expiration image and calculate a voxel-by-voxel ventilation map based on the change in CT attenuation between expiration and inspiration32,36,37 (Fig. 3). Using this technique, Galban et al37 generated a parametric response map on the basis of the assumption that voxels of lung with inspiratory CT attenuation <−950 HU were emphysematous, whereas voxels that were >−950 HU on inspiration but <−856 HU on expiration represented nonemphysematous functional small airway disease. Murphy et al,32 using a similar technique, found that measures based on expiratory CT provided the best correlations with the FEV1/FVC ratio and with the presence or absence of COPD, whereas measures based on coregistered inspiratory and expiratory images provided better classification for the GOLD stage of COPD.
Radiologic evaluation of the airways is helpful in COPD to provide an index of bronchial inflammation and remodeling, to correlate with exacerbation and other symptoms, and to provide a window into understanding abnormality of the small airways. There have been substantial recent advances in the quantitative evaluation of the segmental and subsegmental airways. Currently available software permits multiplanar reconstruction of airways from thin-section volumetric data sets, permitting measurement of luminal diameter and wall thickness to the level of subsegmental or subsubsegmental airways (Fig. 4). Parameters available for evaluating the airways include absolute measures (bronchial lumen diameter or area, bronchial wall thickness or area, and total bronchial area) and relative measures (eg, bronchial wall area %). A commonly used summary measure of the bronchial wall area is the square root of the wall area of a hypothetical bronchus of internal perimeter 10 mm, calculated from the linear regression of all measured bronchi, referred to as Pi10.38
Nakano et al39 showed that there was a correlation between wall area of the small airways, measured using histology, and wall area of the large airways, measured using CT. Han et al4 showed that CT-quantified bronchial wall thickness and severity of emphysema were independently associated with exacerbation frequency and could be used to define bronchial-predominant and emphysema-predominant subtypes of COPD. Grydeland et al40 showed that Pi10 was independently related to symptoms of dyspnea, cough, and wheezing in subjects with COPD. There are modest correlations between airway wall area % and physiological impairment.33,41 In addition, Washko et al41 showed that, on multivariate analysis, wall area % and peak airway attenuation appeared to be independent predictors of predicted FEV1%.
There is increasing interest in using more sophisticated textural analysis to evaluate smoking-related lung injury, including emphysema. Ginsburg et al42 showed that a texture-based approach could discriminate quite effectively between the lungs of never-smokers, smokers without emphysema, and smokers with emphysema. This suggests that textural analysis may be able to identify the early phase of smoking-related lung injury before the development of emphysema.
CONCORDANCE WITH VISUAL EVALUATION
Although QCT measures correlate with the severity of visually assessed emphysema, the level of correlation is not strong. In the COPDGene workshop, in which 58 observers independently scored CT scans of 294 subjects, agreement on the pattern and extent of emphysema was poor to moderate, and concordance between visual and quantitative assessment of the presence of emphysema was only 75%.43 Gietema and colleagues found that, in those subjects with less severe categories of emphysema, radiologists tended to visually underestimate the extent of emphysema compared with quantitative measures, whereas in those with more severe emphysema the radiologists tended to relatively overestimate the extent of emphysema. Thus, QCT and visual evaluation may provide complementary and independent assessments of the severity of emphysema, particularly in those with less severe abnormality. Interestingly, although the presence of emphysema on visual assessment is associated with lung cancer,44,45 QCT measurement of emphysema has not been shown to be independently associated with lung cancer.46–48
Density mask and percentile methods are well-validated measures of the severity of emphysema. More recently, expiratory CT has been widely used for assessing the severity of gas trapping, and it correlates remarkably well with the physiological indices of airway obstruction. Quantitative assessment of COPD also requires evaluation of the airways, with wall area % and Pi10 being the most widely used indices. Exciting new research directions include increased use of textural and local histogram methods to characterize emphysema, inspiratory to expiratory image registration to help distinguish gas trapping due to emphysema from small airway abnormalities, and advances in our understanding of airway wall thickening due to inflammation and remodeling.
3. Gietema HA, Schilham AM, van Ginneken B, et al..Monitoring of smoking-induced emphysema with CT in a lung cancer screening setting: detection of real increase in extent of emphysema.Radiology.2007;244:890–897.
4. Han MK, Kazerooni EA, Lynch DA, et al..Chronic obstructive pulmonary disease exacerbations in the COPDGene study: associated radiologic phenotypes.Radiology.2011;261:274–282.
5. Coxson HO, Dirksen A, Edwards LD, et al..The presence and progression of emphysema in COPD as determined by CT scanning and biomarker expression: a prospective analysis from the ECLIPSE study.Lancet Respir Med.2013;1:129–136.
6. Newell J, Sieren J, Hoffman E.Development of quantitative CT lung protocols.J Thorac Imaging.2013(In press).
7. Müller NL, Staples CA, Miller RR, et al..“Density mask”. An objective method to quantitate emphysema using computed tomography.Chest.1988;94:782–787.
8. Coxson HO, Rogers RM, Whittall KP, et al..A quantification of the lung surface area in emphysema using computed tomography.Am J Respir Crit Care Med.1999;159:851–856.
9. Bankier AA, De Maertelaer V, Keyzer C, et al..Pulmonary emphysema: subjective visual grading versus objective quantification with macroscopic morphometry and thin-section CT densitometry.Radiology.1999;211:851–858.
10. Madani A, Zanen J, de Maertelaer V, et al..Pulmonary emphysema: objective quantification at multi-detector row CT—comparison with macroscopic and microscopic morphometry.Radiology.2006;238:1036–1043.
11. Regan EA, Hokanson JE, Murphy JR, et al..Genetic epidemiology of COPD (COPDGene) study design.COPD.2010;7:32–43.
12. Heussel CP, Herth FJF, Kappes J, et al..Fully automatic quantitative assessment of emphysema in computed tomography: comparison with pulmonary function testing and normal values.Eur Radiol.2009;19:2391–2402.
13. Dirksen A.Monitoring the progress of emphysema by repeat computed tomography scans with focus on noise reduction.Proc Am Thorac Soc.2008;5:925–928.
14. Dirksen A, Friis M, Olesen KP, et al..Progress of emphysema in severe alpha 1-antitrypsin deficiency as assessed by annual CT.Acta Radiol.1997;38:826–832.
15. Stolk J, Dirksen A, van der Lugt AA, et al..Repeatability of lung density measurements with low-dose computed tomography in subjects with alpha-1-antitrypsin deficiency-associated emphysema.Invest Radiol.2001;36:648–651.
16. Heussel CP, Herth FJ, Kappes J, et al..Fully automatic quantitative assessment of emphysema in computed tomography: comparison with pulmonary function testing and normal values.Eur Radiol.2009;19:2391–2402.
17. Mishima M, Hirai T, Itoh H, 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 USA.1999;96:8829–8834.
18. Madani A, Van Muylem A, de Maertelaer V, et al..Pulmonary emphysema: size distribution of emphysematous spaces on multidetector CT images—comparison with macroscopic and microscopic morphometry.Radiology.2008;248:1036–1041.
19. Madani A, Van Muylem A, Gevenois PA.Pulmonary emphysema: effect of lung volume on objective quantification at thin-section CT.Radiology.2010;257:260–268.
20. Fuld MK, Grout RW, Guo J, et al..Systems for lung volume standardization during static and dynamic MDCT-based quantitative assessment of pulmonary structure and function.Acad Radiol.2012;19:930–940.
21. Moroni C, Mascalchi M, Camiciottoli G, et al..Comparison of spirometric-gated and -ungated HRCT in COPD.J Comput Assist Tomogr.2003;27:375–379.
22. Madani A, De Maertelaer V, Zanen J, et al..Pulmonary emphysema: radiation dose and section thickness at multidetector CT quantification—comparison with macroscopic and microscopic morphometry.Radiology.2007;243:250–257.
23. Boedeker KL, McNitt-Gray MF, Rogers SR, et al..Emphysema: effect of reconstruction algorithm on CT imaging measures.Radiology.2004;232:295–301.
24. Grydeland TB, Dirksen A, Coxson HO, et al..Quantitative computed tomography: emphysema and airway wall thickness by sex, age and smoking.Eur Respir J.2009;34:858–865.
25. Camiciottoli G, Cavigli E, Grassi L, et al..Prevalence and correlates of pulmonary emphysema in smokers and former smokers. A densitometric study of participants in the ITALUNG trial.Eur Radiol.2009;19:58–66.
26. Ashraf H, Lo P, Shaker SB, et al..Short-term effect of changes in smoking behaviour on emphysema quantification by CT.Thorax.2011;66:55–60.
27. Shaker SB, Stavngaard T, Laursen LC, et al..Rapid fall in lung density following smoking cessation in COPD.COPD.2011;8:2–7.
28. Bakker ME, Stolk J, Putter H, et al..Variability in densitometric assessment of pulmonary emphysema with computed tomography.Invest Radiol.2005;40:777–783.
29. Stoel BC, Putter H, Bakker ME, et al..Volume correction in computed tomography densitometry for follow-up studies on pulmonary emphysema.Proc Am Thorac Soc.2008;5:919–924.
30. Dirksen A, Piitulainen E, Parr DG, et al..Exploring the role of CT densitometry: a randomised study of augmentation therapy in alpha1-antitrypsin deficiency.Eur Respir J.2009;33:1345–1353.
31. Stockley RA, Parr DG, Piitulainen E, et al..Therapeutic efficacy of alpha-1 antitrypsin augmentation therapy on the loss of lung tissue: an integrated analysis of 2 randomised clinical trials using computed tomography densitometry.Respir Res.2010;11:136.
32. Murphy K, Pluim JPW, van Rikxoort EM, et al..Toward automatic regional analysis of pulmonary function using inspiration and expiration thoracic CT.Med Phys.2012;39:1650–1662.
33. Schroeder JD, McKenzie AS, Zach JA, et al..Relationships between airflow obstruction and quantitative CT measurements of emphysema, air trapping and airways in subjects with and without COPD.Am J Roentgenol.2013(In press).
34. Mets OM, Zanen P, Lammers JW, et al..Early identification of small airways disease on lung cancer screening CT: comparison of current air trapping measures.Lung.2012 Dec;190:629–633.
35. Mets OM, Buckens CF, Zanen P, et al..Identification of chronic obstructive pulmonary disease in lung cancer screening computed tomographic scans.JAMA.2011;306:1775–1781.
36. Barbosa EM Jr, Song G, Tustison N, et al..Computational analysis of thoracic multidetector row HRCT for segmentation and quantification of small airway air trapping and emphysema in obstructive pulmonary disease.Acad Radiol.2011;18:1258–1269.
37. Galban CJ, Han MK, Boes JL, et al..Computed tomography-based biomarker provides unique signature for diagnosis of COPD phenotypes and disease progression.Nat Med.2012;18:1711–1715.
38. Grydeland TB, Thorsen E, Dirksen A, et al..Quantitative CT measures of emphysema and airway wall thickness are related to D(L)CO.Respir Med.2011;105:343–351.
39. Nakano Y, Wong JC, de Jong PA, et al..The prediction of small airway dimensions using computed tomography.Am J Respir Crit Care Med.2005;171:142–146.
40. Grydeland TB, Dirksen A, Coxson HO, et al..Quantitative computed tomography measures of emphysema and airway wall thickness are related to respiratory symptoms.Am J Respir Crit Care Med.2010;181:353–359.
41. Washko GR, Dransfield MT, Estepar RSJ, et al..Airway wall attenuation: a biomarker of airway disease in subjects with COPD.J Appl Physiol.2009;107:185–191.
42. Ginsburg SB, Lynch DA, Bowler RP, et al..Automated texture-based quantification of centrilobular nodularity and centrilobular emphysema in chest CT images.Acad Radiol.2012;19:1241–1251.
43. Barr RG, Berkowitz EA, Bigazzi F, et al..A combined pulmonary-radiology workshop for visual evaluation of COPD: study design, chest CT findings and concordance with quantitative evaluation.COPD.2012;9:151–159.
44. de Torres JP, Bastarrika G, Wisnivesky JP, et al..Assessing the relationship between lung cancer risk and emphysema detected on low-dose CT of the chest.Chest.2007;132:1932–1938.
45. Wilson DO, Weissfeld JL, Balkan A.Association of radiographic emphysema and airflow obstruction with lung cancer.Am Journal Respir Crit Care Med.2008;178:738–744.
46. Gierada DS, Guniganti P, Newman BJ, et al..Quantitative CT assessment of emphysema and airways in relation to lung cancer risk.Radiology.2011;261:950–959.
47. Wilson DO, Leader JK, Fuhrman CR, et al..Quantitative computed tomography analysis, airflow obstruction, and lung cancer in the pittsburgh lung screening study.J Thorac Oncol.2011;6:1200–1205.
48. Kishi K, Gurney JW, Schroeder DR, et al..The correlation of emphysema or airway obstruction with the risk of lung cancer: a matched case-controlled study.Eur Respir J.2002;19:1093–1098.
computed tomography; chronic obstructive pulmonary disease; emphysema; gas trapping
© 2013 by Lippincott Williams & Wilkins
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