- Open Access
Quantification of lung surface area using computed tomography
© The Author(s) 2010
- Received: 15 June 2010
- Accepted: 31 October 2010
- Published: 31 October 2010
To refine the CT prediction of emphysema by comparing histology and CT for specific regions of lung. To incorporate both regional lung density measured by CT and cluster analysis of low attenuation areas for comparison with histological measurement of surface area per unit lung volume.
The histological surface area per unit lung volume was estimated for 140 samples taken from resected lung specimens of fourteen subjects. The region of the lung sampled for histology was located on the pre-operative CT scan; the regional CT median lung density and emphysematous lesion size were calculated using the X-ray attenuation values and a low attenuation cluster analysis. Linear mixed models were used to examine the relationships between histological surface area per unit lung volume and CT measures.
The median CT lung density, low attenuation cluster analysis, and the combination of both were important predictors of surface area per unit lung volume measured by histology (p < 0.0001). Akaike's information criterion showed the model incorporating both parameters provided the most accurate prediction of emphysema.
Combining CT measures of lung density and emphysematous lesion size provides a more accurate estimate of lung surface area per unit lung volume than either measure alone.
- Lung Density
- Histological Measurement
- Percentile Point
- Lung Surface Area
- Alveolar Wall Destruction
The major pathological components responsible for the decrease in maximal expiratory flow that characterize Chronic Obstructive Pulmonary Disease (COPD) include an increase in airway resistance due to small airway remodeling and obliteration, and a decrease in elastic recoil secondary to the parenchymal tissue destruction which characterizes emphysema [1–3]. Separating the contribution of each of these two components can provide better understanding of the natural history of disease, allow monitoring of disease progression, evaluate the impact of a therapeutic intervention and potentially guide the most appropriate therapeutic target in individual patients. The fact that pulmonary function tests cannot separate these structural changes , and because pathological estimates can only do so in surgical or postmortem specimens, has led to attempts to use chest CT scans to measure these changes in vivo.
A number of quantitative CT lung densitometry measurements have been employed to measure the extent of emphysema including, 1) the relative area of lung with attenuation values lower than various thresholds [5–10], 2) a specific percentile point on the frequency-attenuation distribution curve [8, 9, 11], and 3) median lung inflation . However, measurement of lung density may not be the most efficient way to detect emphysema if tissue destruction is accompanied by "remodeling" of the lung parenchyma, such as fibrosis [13–15]. Mishima was the first to introduce cluster analysis of low attenuation areas - a method to measure the size distribution of low attenuation regions . Although validation of this parameter against pathologic standards is controversial , we postulated that cluster analysis would supplement lung densitometry in the detection and quantification of emphysema since it is less likely to be affected by tissue deposition.
In the present study, we tested the relationship between the histopathologic reference standard for emphysema - airspace surface area per unit lung volume (SA/V), and two CT measurements: CT lung densitometry (median lung density) and CT cluster analysis. We hypothesized that the combination of the two CT measurements will be superior to the sole use of either in the prediction of SA/V.
Mean ± SD
67.0 ± 3.1
61.8 - 72.0
5 female:9 male
Smoking (pack yrs)
59.6 ± 44.4
24.8 - 173.0
169.1 ± 7.2
157.0 - 180.0
66.6 ± 12.5
44.0 - 90.0
78.7 ± 16.1
46.7 - 114.5
67.5 ± 8.8
45.9 - 79.0
DLCO % pred
70.4 ± 10.3
47.8 - 90.6
All subjects received a pre-operative, non-contrast helical CT scan in the supine position at the end of full inspiration. 11 subjects were scanned using a GE LightSpeed Ultra CT scanner (General Electric Medical Systems, Milwaukee, WI) with the following settings: 120 kVp, 114 mAs, and 5 mm slices thickness; and 3 subjects were scanned using a Siemens Sensation 16 CT scanner (Siemens Medical Solutions; Erlangen, Germany) with the following parameters: 120 kVp, 115 mAs, and 5 mm slice thickness. The scanners were calibrated regularly using standard water and air phantoms to allow for comparisons between individuals and between scanners.
where L = the length of the grid unit line, ΣI = the number of intersections counted, ΣPtissue is the number of line end points that fall on tissue.
where ΣP total is the number of line end points counted in one image.
SA/V for each of the samples was corrected for shrinkage. The shrinkage factor was determined by measuring the length of one side of the blocks prior to fixation processing and then dividing by the length of that side of the cut sections post-fixation (shrinkage factor: 1.30 ± 0.13)
The primary outcome was the histologically measured SA/V and the independent variables included the median CT lung density and the CT cluster analysis value D. We used a linear mixed model (the REstricted Maximum Likelihood method, REML) to incorporate the within subject variance of the measurements since ten measurements were made from each lung specimen , and we examined the association between the outcome and the two independent variables with the gender, age and patient's body mass index (BMI) being covariates. To test whether CT cluster analysis could supplement lung densitometry (i.e., median lung density) in detecting histological emphysema, we compared the prediction of SA/V using median CT lung density or the CT cluster analysis value D to a third model, which incorporated both variables using Akaike's Information Criterion (AIC) based on the Maximum Likelihood Estimation . The model with the smallest AIC value is considered to be the best model . Analyses were performed using SAS version 9.1 (Carey, N.C.). Statistical significance was defined at a p-value less than 0.05. Continuous variables are expressed as mean ± SD.
The subject demographics are shown in Table 1. The level of airway obstruction of the subjects was relatively mild with only one subject in stage 3 according to the Global Initiative for Obstructive Lung Disease (GOLD) categories . Five subjects were stage 2, two stage 1, and the remaining six subjects had normal lung function.
Quantitative Histology and Quantitative CT Measurements
Histological and Quantitative CT Measurements for 140 Tissue Samples from 14 Subjects
Median CT lung density
Low Attenuation Cluster
161.4 ~ 275.3
5.6 ~ 7.9
0.2 ~ 1.1
175.1 ~ 265.6
6.5 ~ 7.5
0.1 ~ 0.7
102.5 ~ 215.3
5.9 ~ 8.3
0.2 ~ 0.9
182.7 ~ 438.6
4.2 ~ 5.8
0.6 ~ 2.5
39.2 ~ 122.2
11.7 ~ 39.1
0.1 ~ 0.3
172.0 ~ 253.9
4.7 ~ 6.9
0.2 ~ 1.2
84.3 ~ 171.3
8.2 ~ 14.8
0.1 ~ 0.4
171.9 ~ 289.2
5.6 ~ 9.3
0.3 ~ 1.2
90.6 ~ 260.1
7.3 ~ 13.8
0.1 ~ 0.6
227.4 ~ 464.1
2.9 ~ 4.8
1.1 ~ 2.0
141.7 ~ 256.5
3.2 ~ 6.7
0.6 ~ 2.0
320.2 ~ 445.6
3.6 ~ 5.9
0.9 ~ 2.2
78.0 ~ 248.3
6.1 ~ 14.8
0.1 ~ 0.7
237.6 ~ 332.6
4.8 ~ 6.3
0.6 ~ 2.0
SA/V = 4.62 + 1631.99 × median CT lung density;
SA/V = 168.44 + 69.21 × CT cluster analysis value D;
SA/V = 6.04 + 1597.05 × median CT lung density + 11.19 × CT cluster analysis value D.
A comparison of the three models using the Akaike's Information Criterion showed that the model incorporating both CT lung density and low attenuation cluster analysis yielded the smallest AIC value indicating that it is the best model for predicting SA/V (the AIC was 904 for CT lung density alone, 927 for CT cluster analysis alone and 897 for the model incorporating both variables).
The current data also suggest that the cluster analysis value D, per se, is a valid quantitative CT estimate of emphysema because it significantly, and independently, correlated with the histological measurement of surface area per unit lung volume (Figure 3). This finding is at variance with that of Madani et al . We think this discrepancy might be because we chose a different HU cutoff to define the "low attenuation cluster". Madani et al chose -960HU and 1st percentile point as the cutoff whereas we used a relatively higher HU value: -856HU. As we explained in the methods section that -856 HU is converted from a lung tissue inflation value of 6.0 ml/g, which was previously shown to represent the boundary between normal and mild emphysematous lung .
In the current study, we chose surface area per unit lung volume (i.e., SA/V) as the histological reference. This variable has been shown to separate normal lung from emphysematous tissue , and its calculation (Equation 1 and 2) is linearly related to the mean linear intercept (i.e., Lm), which has been used by other groups to estimate emphysema microscopically .
This study has some limitations. First, in the current study, we only used one CT densitometry measurement, median lung density. While Gevenois has shown using thin slice CT scans (1 mm) that -950 HU detects both macroscopic and microscopic emphysema they also showed that using this cut-off 6.8% would be the upper limit of normal and therefore the threshold between normal and diseased . However, previous studies using thick slice CT scans shows that threshold cut-offs such as -910 HU only pick up large emphysematous holes in the lung  while a threshold of -856 HU estimates the small holes . Therefore, with this data in mind, we chose the mean lung density threshold, because of the small size of pathologic specimens (2 × 2 cm2) that we were comparing to the thick slice CT values and the relatively mild degree of emphysema present in our subjects and specimens. We cannot comment on the supplementary role of CT cluster analysis to other more traditional whole lung CT densitometry measurements of emphysema, such as low attenuation area and percentile point, etc. However, we believe it is reasonable to assume that CT cluster analysis would supplement the other CT densitometry measurements since all such measurements rely on choosing a cutoff value from the X-ray attenuation distribution histogram, either along the X axis (i.e., low attenuation area) or along the Y axis (i.e., percentile point). The extent, to which, CT cluster analysis supplements the different CT densitometry measurements might vary depending on the threshold use and, therefore, further studies including other densitometry measurements may provide more information. Secondly, we used -856HU, based on our previous experience with thick slice CT scans that identified this HU threshold as effective in identifying mild emphysematous areas . We realize that CT scan slices in our previous study were of 10 mm thickness whereas in the current study were of 5 mm slice thickness. Due to limitations in CT scanner technology, we are not able to test whether this threshold is equally effective using either slice thickness. Lastly, the pre-surgery CT images were acquired using two different CT scanners could have introduced errors in CT lung density measurement. However since the X-ray radiation dose is similar (120 kVp and 114 mAs on GE scanner; 120 kVp and 115 mAs on Siemens scanner), we believe this effect is small. Moreover we have previously shown that CT densitometry measurements using similar acquisition protocols are comparable between these CT scanners .
The difference in Akaike's Information Criterion (AIC) between the models appears small but this does not mean that the added information of the combined model is small. The AIC cannot be interpreted using a traditional "hypothesis testing" statistical paradigm. It does not generate a P value, does not reach conclusions about "statistical significance", and does not "reject" any model. AIC determines how well the data supports each model, taking into account both the goodness-of-fit (sum-of-squares) and the number of parameters in the model. Ultimately, the model with the smallest AIC is considered the best, although the AIC value itself is not meaningful .
In conclusion, the results of this study show that an accurate comparison between CT and histological measurements can be achieved by precisely mapping the location of the histological sample to its in vivo location on the CT. In addition, the CT cluster analysis value D can supplement CT densitometry in detecting and quantifying emphysema. The additional benefit may be due to the fact that cluster analysis is more sensitive to true tissue destruction and immune to the artifact caused by the deposition of connective tissue that may accompany the emphysematous process.
The authors thank Anh-Toan Tran, BSc and Ida Chan, MD for technical assistance in developing and supporting the lung analysis application.
PDP is a Michael Smith Foundation for Health Research Distinguished scholar and the Jacob Churg Distinguished Researcher. DDS is a Canada Research Chair in COPD and a Senior Scholar with the Michael Smith Foundation for Health Research. HOC was Parker B Francis Fellow in Pulmonary Research during the time of this research. HOC is currently a Canadian Institutes of Health Research (CIHR)/British Columbia Lung Association New Investigator. HOC is also supported, in part, by the University of Pittsburgh COPD SCCOR NIH 1P50 HL084948 and R01 HL085096 from the National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD to the University of Pittsburgh. This project was funded by a CIHR Industry partnership grant with GlaxoSmithKline.
- Burrows B, Knudson RJ, Cline MG, Lebowitz MD: Quantitative relationships between cigarette smoking and ventilatory function. Am Rev Respir Dis 1977, 115:195–205.PubMedGoogle Scholar
- Hogg JC, Macklem PT, Thurlbeck WM: Site and nature of airway obstruction in chronic obstructive lung disease. N Engl J Med 1968, 278:1355–1360.View ArticlePubMedGoogle Scholar
- Macklem PT, Mead J: Resistance of central and peripheral airways measured by a retrograde catheter. J Appl Physiol 1967, 22:395–401.PubMedGoogle Scholar
- Fraser RS, Paré PD, Colman NC, Muller NL: Diagnosis of Diseases of the Chest. Fourth edition. Philadelphia: Saunders; 1999.Google Scholar
- Bankier AA, De Maertelaer V, Keyzer C, Gevenois PA: Pulmonary emphysema: subjective visual grading versus objective quantification with macroscopic morphometry and thin-section CT densitometry. Radiology 1999, 211:851–858.View ArticlePubMedGoogle Scholar
- Gevenois PA, De Vuyst P, de Maertelaer V, Zanen J, Jacobovitz D, Cosio MG, Yernault JC: Comparison of computed density and microscopic morphometry in pulmonary emphysema. Am J Respir Crit Care Med 1996, 154:187–192.View ArticlePubMedGoogle Scholar
- Hayhurst MD, MacNee W, Flenley DC, Wright D, McLean A, Lamb D, Wightman AJ, Best J: Diagnosis of pulmonary emphysema by computerised tomography. Lancet 1984, 2:320–322.View ArticlePubMedGoogle Scholar
- 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.View ArticlePubMedGoogle Scholar
- 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.View ArticlePubMedGoogle Scholar
- Muller NL, Staples CA, Miller RR, Abboud RT: "Density mask". An objective method to quantitate emphysema using computed tomography. Chest 1988, 94:782–787.View ArticlePubMedGoogle Scholar
- Gould GA, MacNee W, McLean A, Warren PM, Redpath A, Best JJ, Lamb D, Flenley DC: CT measurements of lung density in life can quantitate distal airspace enlargement - an essential defining feature of human emphysema. Am Rev Respir Dis 1988, 137:380–392.View ArticlePubMedGoogle Scholar
- Coxson HO, Rogers RM, Whittall KP, D'Yachkova Y, Pare PD, Sciurba FC, Hogg JC: A quantification of the lung surface area in emphysema using computed tomography. Am J Respir Crit Care Med 1999, 159:851–856.View ArticlePubMedGoogle Scholar
- Lang MR, Fiaux GW, Gillooly M, Stewart JA, Hulmes DJ, Lamb D: Collagen content of alveolar wall tissue in emphysematous and non-emphysematous lungs. Thorax 1994, 49:319–326.View ArticlePubMedPubMed CentralGoogle Scholar
- Tonelli M, Stern EJ, Glenny RW: HRCT evident fibrosis in isolated pulmonary emphysema. J Comput Assist Tomogr 1997, 21:322–323.View ArticlePubMedGoogle Scholar
- Cardoso WV, Sekhon HS, Hyde DM, Thurlbeck WM: Collagen and elastin in human pulmonary emphysema. Am Rev Respir Dis 1993, 147:975–981.View ArticlePubMedGoogle Scholar
- Mishima M, Hirai T, Itoh H, Nakano Y, Sakai H, Muro S, Nishimura K, Oku Y, Chin K, Ohi M, 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.View ArticlePubMedPubMed CentralGoogle Scholar
- Miller A, Thornton JC, Warshaw R, Anderson H, Teirstein AS, Selikoff IJ: Single breath diffusing capacity in a representative sample of the population of Michigan, a large industrial state. Predicted values, lower limits of normal, and frequencies of abnormality by smoking history. Am Rev Respir Dis 1983, 127:270–277.PubMedGoogle Scholar
- Hogg JC, Chu F, Utokaparch S, Woods R, Elliott WM, Buzatu L, Cherniack RM, Rogers RM, Sciurba FC, Coxson HO, Pare PD: The nature of small-airway obstruction in chronic obstructive pulmonary disease. N Engl J Med 2004, 350:2645–2653.View ArticlePubMedGoogle Scholar
- Howard CV, Reed MG: Unbiased Stereology: Three-Dimensional Measurement in Microscopy, Second Edition Summary. Second edition. Liverpool, UK: Taylor & Francis Inc; 2004.Google Scholar
- Yuan R, Mayo JR, Hogg JC, Pare PD, McWilliams AM, Lam S, Coxson HO: The Effects of Radiation Dose and CT Manufacturer on Measurements of Lung Densitometry. Chest 2007, 132:617–623.View ArticlePubMedGoogle Scholar
- Hedlund LW, Vock P, Effmann EL: Evaluating lung density by computed tomography. Semin Respir Med 1983, 5:76–87.View ArticleGoogle Scholar
- Coxson HO, Whittall KP, Nakano Y, Rogers RM, Sciurba FC, Keenan RJ, Hogg JC: Selection of patients for lung volume reduction surgery using a power law analysis of the computed tomographic scan. Thorax 2003, 58:510–514.View ArticlePubMedPubMed CentralGoogle Scholar
- Feldman HA: Families of lines: random effects in linear regression analysis. J Appl Physiol 1988, 64:1721–1732.PubMedGoogle Scholar
- Verbeke G, Molenberghs G: Linear Mixed Models for Longitudinal Data. Springer-Verlag New York; 2000.Google Scholar
- Ljung L: System Identification: Theory for the User. Upper Saddle River, NJ: Prentice-Hal PTR; 1999.View ArticleGoogle Scholar
- Rabe KF, Hurd S, Anzueto A, Barnes PJ, Buist SA, Calverley P, Fukuchi Y, Jenkins C, Rodriguez-Roisin R, van Weel C, Zielinski J: Global strategy for the diagnosis, management, and prevention of chronic obstructive pulmonary disease: GOLD executive summary. Am J Respir Crit Care Med 2007, 176:532–555.View ArticlePubMedGoogle Scholar
- Miller RR, Muller NL, Vedal S, Morrison NJ, Staples CA: Limitations of computed tomography in the assessment of emphysema. Am Rev Respir Dis 1989, 139:980–983.View ArticlePubMedGoogle Scholar
- Lindsey JK, Jones B: Choosing among generalized linear models applied to medical data. Stat Med 1998, 17:59–68.View ArticlePubMedGoogle Scholar
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