CT-derived Biomechanical Metrics Improve Agreement Between Spirometry and Emphysema

Rationale and Objectives

Many patients with chronic obstructive pulmonary disease (COPD) have marked discordance between forced expiratory volume in 1 second (FEV 1 ) and degree of emphysema on computed tomography (CT). Biomechanical differences between these patients have not been studied. We aimed to identify reasons for the discordance between CT and spirometry in some patients with COPD.

Materials and Methods

Subjects with Global initiative for chronic Obstructive Lung Disease stages I–IV from a large multicenter study (The Genetic Epidemiology of COPD) were arranged by percentiles of %predicted FEV 1 and emphysema on CT. Three categories were created using differences in percentiles: Cat spir with predominant airflow obstruction/minimal emphysema, Cat CT with predominant emphysema/minimal airflow obstruction, and Cat matched with matched FEV 1 and emphysema. Image registration was used to derive Jacobian determinants, a measure of lung elasticity, anisotropy, and strain tensors, to assess biomechanical differences between groups. Regression models were created with the previously mentioned categories as outcome variable, adjusting for demographics, scanner type, quantitative CT-derived emphysema, gas trapping, and airway thickness (model 1), and after adding biomechanical CT metrics (model 2).

Results

Jacobian determinants, anisotropy, and strain tensors were strongly associated with FEV 1 . With Cat matched as control, model 2 predicted Cat spir and Cat CT better than model 1 (Akaike information criterion 255.8 vs. 320.8). In addition to demographics, the strongest independent predictors of FEV 1 were Jacobian mean (β = 1.60,95%confidence intervals [CI] = 1.16 to 1.98; P < 0.001), coefficient of variation (CV) of Jacobian (β = 1.45,95%CI = 0.86 to 2.03; P < 0.001), and CV of strain (β = 1.82,95%CI = 0.68 to 2.95; P = 0.001). CVs of Jacobian and strain are both potential markers of biomechanical lung heterogeneity.

Conclusions

CT-derived measures of lung mechanics improve the link between quantitative CT and spirometry, offering the potential for new insights into the linkage between regional parenchymal destruction and global decrement in lung function in patients with COPD.

Introduction

The diagnosis of chronic obstructive pulmonary disease (COPD) is currently based on the detection of airflow obstruction by spirometry . It is increasingly recognized that airflow obstruction as measured by impairment in the forced expiratory volume in 1 second (FEV 1 ) does not fully explain the morbidity associated with the disease, and this functional definition can be complemented by anatomic measures of disease using widely available imaging modalities . Computed tomography (CT) has become the gold standard in the quantitative assessment of the presence and distribution of emphysema, a major component of COPD, and relies on using a fixed Hounsfield threshold value below which all lung areas are deemed emphysematous in a CT scan obtained at full inspiration . CT measures of emphysema correlate well with pathology , and numerous studies have shown a strong correlation between spirometry and CT emphysema . The agreement between CT emphysema andspirometry is however not perfect, and in some cases, CT densitometry may be more sensitive than spirometry in detecting emphysema .

It is our observation that many patients with COPD have marked discordance between FEV 1 and degree of emphysema on volumetric CT . Some subjects with severe airflow obstruction have mild emphysema on CT and conversely, some patients with severe emphysematous destruction of the lung have relatively mild spirometric impairment. Although some of these differences, especially in the former group, are likely due to airway narrowing, the reasons for this discrepancy between expected changes on spirometry and CT have not been systematically studied, particularly in the disproportionate emphysema group. Because airflow obstruction is due to a combination of airway narrowing and loss of elastic recoil due to emphysema, it is possible that static single-volume CT images do not capture lung mechanics sufficiently to explain lung function defects. We hypothesized that biomechanical measures of regional lung tissue expansion and contraction using image registration applied to paired inspiratory and expiratory CT scans will provide a link between CT-derived quantitative measures and spirometry. Through a demonstration of this link, we seek to provide an improved understanding of patient-specific links between the presence and distribution of quantitative emphysema and airflow obstruction.

Materials and Methods

Data Collection

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Case Selection

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Image Registration

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Statistical Analyses

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Results

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TABLE 1

Demographic Information, Radiographic, and Spirometry Measures

Cat spir ( n = 100) Cat CT ( n = 97) Cat matched ( n = 100) Age (years) 60.4(7.8) ** 65.0(8.2) 64.5(9.0) Sex(%Men) 60(60) 69(71) 57(57) Race(%Non-Hispanic Whites) 74(74) * 85(88) 84(84) BMI(kg/m 2 ) 33(7.3) *** 26.6(5.2) 26.4(6.0) Smoking pack-years 57.3(29.0) 51.4(25.8) 50.4(24.3) FEV 1 (L) 1.15(0.35) ** 2.75(0.68) *** 1.45(0.82) FEV 1 % predicted 38.0(9.0) *** 92.6(13.7) *** 50.8(26.6) FVC(L) 2.29(0.63) *** 4.48(0.94) *** 3.01(0.94) FEV 1 /FVC 0.51(0.10) * 0.61(0.07) *** 0.46(0.17) %Emphysema(LAA insp < −950 HU) 1.5(1.2) *** 17.3(8.9) 19.4(18.0) %Gas trapping(LAA exp < −856 HU) 20.5(12.6) *** 34.9(11.8) ** 44.5(26.4) Wall area% 65.1(2.7) *** 59.3(2.4) *** 62.2(2.8)

BMI, body mass index; Cat CT , category with disproportionate CT abnormality; Cat matched , matched CT and spirometric abnormalities; Cat spir , category with disproportionate spirometric abnormality; FEV 1 , forced expiratory volume in 1 second; FVC, forced vital capacity. LAA exp < −856 HU, low attenuation areas <−856 Hounsfield units at end expiration; LAA insp < −950 HU, low attenuation areas <−950 Hounsfield units at end inspiration; Wall Area% = (wall area/total bronchial area) × 100, calculated as the average of six segmental bronchi in each subject.

All values expressed as mean(standard deviation) unless otherwise specified. P < 0.05; P < 0.01; P < 0.001.

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TABLE 2

Univariate and Multivariable Linear Regression for Prediction of FEV 1

Variable Univariate Multivariable Parameter Estimate 95% CI_P_ Value Parameter Estimate 95% CI_P_ Value Age (years) −0.010 −0.022 to 0.003 0.134 −0.19 −0.026 to −0.011 <0.001 Male sex −0.69 −0.90 to −0.48 <0.001 −0.53 −0.65 to −0.40 <0.001 LAA insp < −950 HU −0.010 −0.018 to −0.003 0.007 −0.026 −0.035 to −0.016 <0.001 LAA exp < −856 HU −0.014 −0.019 to −0.009 <0.001 0.006 0.001 to 0.014 0.115 Wall area% −0.21 −0.26 to −0.16 <0.001 −0.07 −0.10 to −0.05 <0.001 Jacobian mean 2.49 2.17 to 2.81 <0.001 1.60 1.16 to 1.98 <0.001 Jacobian CV 3.90 3.38 to 4.42 <0.001 1.45 0.86 to 2.03 <0.001 Strain CV −1.68 −3.06 to −0.30 0.017 1.82 0.68 to 2.95 0.001

CI, confidence intervals; CV, coefficient of variation; FEV 1 , forced expiratory volume in the first second; LAA insp < −950 HU, low attenuation areas <−950 Hounsfield units at end inspiration; LAA exp < −856 HU, low attenuation areas <−856 Hounsfield units at end expiration. Wall Area% = (wall area/total bronchial area) × 100, calculated as the average of six segmental bronchi in each subject.

Multivariable model included variables significant on univariate analyses in the table and were also adjusted for age, body mass index, sex, race, and scanner type. R 2 for multivariable model = 0.73.

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TABLE 3

Biomechanical CT Measures for the Three Categories

Cat spir ( n = 100) Cat CT ( n = 97) Cat matched ( n = 100) Jacobian mean 1.38(0.18) 1.73(0.20) ** 1.44(0.23) Jacobian CV 0.21(0.08) * 0.46(0.16) ** 0.25(0.10) Strain mean 0.36(0.12) * 0.65(0.15) ** 0.41(0.16) Strain CV 0.57(0.07) ** 0.61(0.06) 0.62(0.09) ADI mean 1.03(0.53) 3.20(2.71) ** 1.34(0.94) ADI CV 1.06(0.36) * 1.71(0.69) ** 1.25(0.36)

ADI, anisotropic deformation index; Cat spir , category with disproportionate spirometric abnormality; Cat CT , category with disproportionate CT abnormality; Cat matched , matched CT and spirometric abnormalities; CT, computed tomography; CV, coefficient of variation.

All values expressed as mean(standard deviation) unless otherwise specified. P < 0.05; P < 0.001.

TABLE 4

Multivariable Logistic Regression Models for the Prediction of Disproportionate Categories

Model 1 Cat spir Cat CT Odds Ratio 95%CI Odds Ratio 95%CI LAA < −950 insp 0.40 *** 0.25–0.62 0.94 0.91–1.03 LAA < −856 exp 1.14 ** 1.06–1.23 1.05 * 1.01–1.10 WA% 1.96 *** 1.53–2.52 1.47 *** 1.24–1.75

Model 2 Cat spir Cat CT Odds Ratio 95%CI Odds Ratio 95%CI LAA < −950 insp 0.62 * 0.39–0.98 0.96 0.89–1.04 LAA < −856 exp 0.94 0.84–1.06 0.98 0.92–1.04 WA% 1.63 *** 1.22–2.18 1.20 0.98–1.47 Jacobian mean 17.05 *** 4.84–60.11 4.38 *** 2.15–8.93 Jacobian CV 21.98 * 4.47–65.04 5.96 *** 2.75–12.9 Strain CV 2.16 0.76–6.15 1.83 * 1.01–3.31

Cat CT , category with disproportionate CT abnormality; Cat matched , matched CT and spirometric abnormalities; Cat spir , category with disproportionate spirometric abnormality; CV, coefficient of variation; LAA insp < −950 HU, low attenuation areas <−950 Hounsfield units at end inspiration; LAA exp < −856 HU, low attenuation areas <−856 Hounsfield units at end expiration; Wall Area% = (wall area/total bronchial area) × 100, calculated as the average of six segmental bronchi in each subject.

All models adjusted for age, race, sex, body mass index, and scanner type. P < 0.05; P < 0.01; P < 0.001.

R 2 for model 1 = 0.56; R 2 for model 2 = 0.66.

AIC for model 1 = 320.8; AIC for model 2 = 255.8.

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Discussion

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Appendix

Supplementary Material

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Appendix S1

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Table S1

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CT-derived Biomechanical Metrics Improve Agreement Between Spirometry and Emphysema

Rationale and Objectives

Materials and Methods

Results

Conclusions

Introduction

Materials and Methods

Data Collection

Case Selection

Image Registration

Statistical Analyses

Results

Discussion

Appendix

Supplementary Material

References

Further Reading

Anatomic Eponyms in Neuroradiology

Clinical Decision Support at the Point-of-Order Entry

CT Angiography in Patients with Peripheral Arterial Disease