Variation in quantitative CT air trapping in heavy smokers on repeat CT examinations

Utilization of Quantitative Computed Tomography Assessment to Identify Bronchiolitis Obliterans Syndrome After Single Lung Transplantation

OBJECTIVE

To evaluate quantitative chest computed tomography (CT) methods for the detection of air trapping (AT) and to assess its diagnostic performance for the diagnosis of bronchiolitis obliterans syndrome (BOS) in single lung transplant (SLT) patients.

METHODS

Adult patients who had a SLT at a single transplant center and underwent CT scan after transplantation were retrospectively included. CT findings of air trapping were measured by three different methods: expiratory air-trapping index (ATIexp), mean lung density on expiratory acquisition (MLDexp) and expiratory to inspiratory ratio of mean lung density (E/I-ratio(MLD). Sensitivity, specificity and diagnostic accuracy of the three methods for the detection of BOS status evaluated by serial routine measures of pulmonary function tests (gold standard) were assessed.

RESULTS

Forty-six SLT patients (52.2% females, mean age 58 ± 6 years) were included in the analysis, 12 (26%) patients with a diagnosis of BOS. Quantitative CT diagnosis of AT ranged from 26 to 35%. Sensitivity, specificity and accuracy of each method for the detection of BOS were 85.7%, 84.7% and 85.0% for ATIexp, 78.5%, 93.4% and 90.0% for MLD and 64.2%, 89.1% and 83.3% E/I-ratio(MLD), respectively.

CONCLUSION

Quantitative measures of AT obtained from standard CT are feasible and show high specificity and accuracy for the detection of BOS in SLT patients.

High-resolution CT pulmonary findings in children with severe asthma

OBJECTIVE

To compare quantitative CT parameters between children with severe asthma and healthy subjects, correlating to their clinical features.

METHODS

We retrospectively analyzed CT data from 19 school-aged children (5-17 years) with severe asthma and 19 control school-aged children with pectus excavatum. The following CT parameters were evaluated: total lung volume (TLV), mean lung density (MLD), CT air trapping index (AT%) (attenuation ≤856 HU), airway wall thickness (AWT), and percentage of airway wall thickness (AWT%). Multi-detector computed tomography (MDCT) data were correlated to the following clinical parameters: forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), forced expiratory flow at 25-75% (FEF 25-75%), FEV1/FVC ratio, sputum and bronchoalveolar lavage analysis, serum IgE levels, and previous hospitalizations due to asthma.

RESULTS

Asthma patients presented higher mean values of AT% (23.8 ± 6.7% vs. controls, 9.7 ± 3.2%), AWT (1.46 ± 0.22 mm vs. controls, 0.47 ± -735 ± 28 HU vs. controls, -666 ± 19 HU). Mean AT% was 29.0 ± 4.7% in subjects with previous hospitalization against 19.2 ± 5.0% in those with no prior hospitalization (p < 0.001). AT% presented very strong negative correlations with FVC (r = -0.933, p < 0.001) and FEV1 (r = -0.841, p < 0.001) and a moderate correlation with FEF 25-75% (r = -0.608, p = 0.007). AT% correlation with FEV1/FVC ratio and serum IgE was weak (r = -0.184, p = 0.452, and r = -0.363, p = 0.202) CONCLUSION: Children with severe asthma present differences in quantitative chest CT scans compared to healthy controls with strong correlations with pulmonary function tests and previous hospitalizations due to asthma.

Air trapping in usual interstitial pneumonia pattern at CT: prevalence and prognosis

This study was conducted to evaluate the presence of air trapping in patients with idiopathic pulmonary fibrosis (IPF) and other interstitial lung diseases (ILDs) (non-IPF), showing the radiological pattern of usual interstitial pneumonia (UIP). Retrospectively, we included 69 consecutive patients showing the typical UIP pattern on computed tomography (CT), and 15 final diagnosis of IPF with CT pattern “inconsistent with UIP” due to extensive air trapping. Air trapping at CT was assessed qualitatively by visual analysis and quantitatively by automated-software. In the quantitative analysis, significant air trapping was defined as >6% of voxels with attenuation between −950 to −856 HU on expiratory CT (expiratory air trapping index [ATIexp]) or an expiratory to inspiratory (E/I) ratio of mean lung density >0.87. The sample comprised 51 (60.7%) cases of IPF and 33 (39.3%) cases of non-IPF ILD. Most patients did not have air trapping (E/I ratio ≤0.87, n = 53, [63.1%]; ATIexp ≤6%, n = 45, [53.6%]). Air trapping in the upper lobes was the only variable distinguishing IPF from non-IPF ILD (prevalence, 3.9% vs 33.3%, p < 0.001). In conclusion, air trapping is common in patients with ILDs showing a UIP pattern on CT, as determined by qualitative and quantitative evaluation, and should not be considered to be inconsistent with UIP. On subjective visual assessment, air trapping in the upper lobes was associated with a non-IPF diagnoses.

Use of MDCT to Assess the Results of Bronchial Thermoplasty

OBJECTIVE

The purpose of this study was to evaluate the use of MDCT to assess response to bronchial thermoplasty treatment for severe persistent asthma.

MATERIALS AND METHODS

MDCT data from 26 patients with severe persistent asthma who underwent imaging before and after bronchial thermoplasty were analyzed retrospectively. Changes in the following parameters were assessed: total lung volume, mean lung density, airway wall thickness, CT air trapping index (attenuation < -856 HU), and expiratory-inspiratory ratio of mean lung density (E/I index). Asthma Quality of Life Questionnaire score changes were also assessed.

RESULTS

Median total lung volumes before and after bronchial thermoplasty were 2668 mL (range, 2226-3096 mL) and 2399 mL (range, 1964-2802 mL; p = 0.08), respectively. Patients also showed a pattern of obstruction improvement in air trapping values (median before thermoplasty, 14.25%; median after thermoplasty, 3.65%; p < 0.001] and in mean lung density values ± SD (before thermoplasty, -702 ± 72 HU; after thermoplasty, -655 ± 66 HU; p < 0.01). Median airway wall thickness also decreased after bronchial thermoplasty (before thermoplasty, 1.5 mm; after thermoplasty, 1.1 mm; p < 0.05). There was a mean Asthma Quality of Life Questionnaire overall score change of 1.00 ± 1.35 (p < 0.001), indicating asthma clinical improvement.

CONCLUSION

Our study showed improvement in CT measurements after bronchial thermoplasty, along with Asthma Quality of Life Questionnaire score changes. Thus, MDCT could be useful for imaging evaluation of patients undergoing this treatment.

Risk of Lung Cancer Associated with COPD Phenotype Based on Quantitative Image Analysis

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a risk factor for lung cancer. This study evaluates alternative measures of COPD based on spirometry and quantitative image analysis to better define a phenotype that predicts lung cancer risk.

METHODS

A total of 341 lung cancer cases and 752 volunteer controls, ages 21 to 89 years, participated in a structured interview, standardized CT scan, and spirometry. Logistic regression, adjusted for age, race, gender, pack-years, and inspiratory and expiratory total lung volume, was used to estimate the odds of lung cancer associated with FEV1/FVC, percent voxels less than -950 Hounsfield units on the inspiratory scan (HUI) and percent voxels less than -856 HU on expiratory scan (HUE).

RESULTS

The odds of lung cancer were increased 1.4- to 3.1-fold among those with COPD compared with those without, regardless of assessment method; however, in multivariable modeling, only percent voxels <-856 HUE as a continuous measure of air trapping [OR = 1.04; 95% confidence interval (CI), 1.03-1.06] and FEV1/FVC < 0.70 (OR = 1.71; 95% CI, 1.21-2.41) were independent predictors of lung cancer risk. Nearly 10% of lung cancer cases were negative on all objective measures of COPD.

CONCLUSION

Measures of air trapping using quantitative imaging, in addition to FEV1/FVC, can identify individuals at high risk of lung cancer and should be considered as supplementary measures at the time of screening for lung cancer.

IMPACT

Quantitative measures of air trapping based on imaging provide additional information for the identification of high-risk groups who might benefit the most from lung cancer screening. Cancer Epidemiol Biomarkers Prev; 25(9); 1341-7. ©2016 AACR.

Quantitative computed tomography imaging of airway remodeling in severe asthma

Asthma is a heterogeneous condition and approximately 5-10% of asthmatic subjects have severe disease associated with structure changes of the airways (airway remodeling) that may develop over time or shortly after onset of disease. Quantitative computed tomography (QCT) imaging of the tracheobronchial tree and lung parenchyma has improved during the last 10 years, and has enabled investigators to study the large airway architecture in detail and assess indirectly the small airway structure. In severe asthmatics, morphologic changes in large airways, quantitatively assessed using 2D-3D airway registration and recent algorithms, are characterized by airway wall thickening, luminal narrowing and bronchial stenoses. Extent of expiratory gas trapping, quantitatively assessed using lung densitometry, may be used to assess indirectly small airway remodeling. Investigators have used these quantitative imaging techniques in order to attempt severity grading of asthma, and to identify clusters of asthmatic patients that differ in morphologic and functional characteristics. Although standardization of image analysis procedures needs to be improved, the identification of remodeling pattern in various phenotypes of severe asthma and the ability to relate airway structures to important clinical outcomes should help target treatment more effectively.

Quantitative Assessment of Global and Regional Air Trappings Using Non-Rigid Registration and Regional Specific Volume Change of Inspiratory/Expiratory CT Scans: Studies on Healthy Volunteers and Asthmatics

Objective

The purpose of this study was to compare air trapping in healthy volunteers with asthmatics using pulmonary function test and quantitative data, such as specific volume change from paired inspiratory CT and registered expiratory CT.

Materials and Methods

Sixteen healthy volunteers and 9 asthmatics underwent paired inspiratory/expiratory CT. ΔSV, which represents the ratio of air fraction released after exhalation, was measured with paired inspiratory and anatomically registered expiratory CT scans. Air trapping indexes, ΔSV_0.4 and ΔSV_0.5, were defined as volume fraction of lung below 0.4 and 0.5 ΔSV, respectively. To assess the gravity effect of air-trapping, ΔSV values of anterior and posterior lung at three different levels were measured and ΔSV ratio of anterior lung to posterior lung was calculated. Color-coded ΔSV map of the whole lung was generated and visually assessed. Mean ΔSV, ΔSV_0.4, and ΔSV_0.5 were compared between healthy volunteers and asthmatics. In asthmatics, correlation between air trapping indexes and clinical parameters were assessed.

Results

Mean ΔSV, ΔSV_0.4, and ΔSV_0.5 in asthmatics were significantly higher than those in healthy volunteer group (all p < 0.05). ΔSV values in posterior lung in asthmatics were significantly higher than those in healthy volunteer group (p = 0.049). In asthmatics, air trapping indexes, such as ΔSV_0.5 and ΔSV_0.4, showed negative strong correlation with FEF_25-75, FEV₁, and FEV₁/FVC. ΔSV map of asthmatics showed abnormal geographic pattern in 5 patients (55.6%) and disappearance of anterior-posterior gradient in 3 patients (33.3%).

Conclusion

Quantitative assessment of ΔSV (the ratio of air fraction released after exhalation) shows the difference in extent of air trapping between health volunteers and asthmatics.

A new quantitative index of lobar air trapping in chronic obstructive pulmonary disease (COPD): comparison with conventional methods

PURPOSE

To determine the usefulness of newly-proposed index (attenuation-volume index, AVI: increase in mean value of lung attenuation (MVLA) divided by volume decrease ratio (VDR)) for quantitative assessment of lobar air trapping (LAT) using expiratory/inspiratory (E/I) computed tomography (CT) by minimizing influence of respiratory level.

MATERIALS AND METHODS

Institutional review board approved study protocol. Twenty-one moderate or severe COPD (group A), 16 mild COPD (group B) and 26 normal volunteers (group C) underwent both E/I scans via 320-row CT and pulmonary functional test (PFT). Volume image data were automatically segmented into six lung lobes with minimal manual intervention. AVI, pixel index (PI), air trapping ratio (ATR) and relative volume change (RVC860-950) were calculated in total lung (TL) and each lobe. Four indices in TL were correlated with both PFT result and VDR and those in TL and each lobe were compared between three groups.

RESULTS

Similar to ATR, AVI correlated with both FEV1/FVC (r=0.772, p<0.01) and RV/TLC (r=-0.726, p<0.01) and demonstrated a significant difference between three groups in both TL (group A: 1.69±0.45, group B: 2.21±0.45 and group C: 2.80±0.44) and five lobes except for left lingular segment. In a lobe-based analysis regarding relationship with VDR, AVI was much less dependent than ATR, although regression lines of groups A and C were separated for AVI as well as ATR. Coefficient of variation of either PI or RVC860-950 was significantly larger than that of AVI.

CONCLUSION

AVI can be a more suitable CT index for quantitative evaluation of LAT, minimizing influence of respiratory level.

Repeatability and Sample Size Assessment Associated with Computed Tomography-Based Lung Density Metrics

RATIONALE AND OBJECTIVES

Density-based metrics assess severity of lung disease but vary with lung inflation and method of scanning. The aim of this study was to evaluate the repeatability of single center, CT-based density metrics of the lung in a normal population and assess study sample sizes needed to detect meaningful changes in lung density metrics when scan parameters and volumes are tightly controlled.

MATERIALS AND METHODS

Thirty-seven subjects (normal smokers and non-smokers) gave consent to have randomly assigned repeated, breath-held scans at either inspiration (90% vital capacity: TLC) or expiration (20% vital capacity: FRC). Repeated scans were analyzed for: mean lung density (MLD), 15^th percentile point of the density histogram (P₁₅), low attenuation areas (LAA) and alpha (fractal measure of hole size distribution). Using inter-subject differences and previously reported bias, sample size was estimated from month or yearly change in density metrics obtained from published literature (i.e. meaningful change).

RESULTS

Inter-scan difference measurements were small for density metrics (ICC > 0.80) and average ICCs for whole lung alpha_-910 and alpha_-950 were 0.57 and 0.64, respectively. Power analyses demonstrated that, under the control conditions with minimal extrinsic variation, population sizes needed to detect meaningful changes in density measures for TLC or FRC repeated scans ranged from a few (20-40) to a few hundred subjects, respectively.

CONCLUSION

A meaningful sample size was predicted from this study using volume-controlled normal subjects in a controlled imaging environment. Under proper breath-hold conditions, high repeatability was obtained in cohorts of normal smokers and non-smokers.

Repeatability and Sample Size Assessment Associated with Computed Tomography-Based Lung Density Metrics

RATIONALE AND OBJECTIVES

Density-based metrics assess severity of lung disease but vary with lung inflation and method of scanning. The aim of this study was to evaluate the repeatability of single center, CT-based density metrics of the lung in a normal population and assess study sample sizes needed to detect meaningful changes in lung density metrics when scan parameters and volumes are tightly controlled.

MATERIALS AND METHODS

Thirty-seven subjects (normal smokers and non-smokers) gave consent to have randomly assigned repeated, breath-held scans at either inspiration (90% vital capacity: TLC) or expiration (20% vital capacity: FRC). Repeated scans were analyzed for: mean lung density (MLD), 15th percentile point of the density histogram (P15), low attenuation areas (LAA) and alpha (fractal measure of hole size distribution). Using inter-subject differences and previously reported bias, sample size was estimated from month or yearly change in density metrics obtained from published literature (i.e. meaningful change).

RESULTS

Inter-scan difference measurements were small for density metrics (ICC > 0.80) and average ICCs for whole lung alpha-910 and alpha-950 were 0.57 and 0.64, respectively. Power analyses demonstrated that, under the control conditions with minimal extrinsic variation, population sizes needed to detect meaningful changes in density measures for TLC or FRC repeated scans ranged from a few (20-40) to a few hundred subjects, respectively.

CONCLUSION

A meaningful sample size was predicted from this study using volume-controlled normal subjects in a controlled imaging environment. Under proper breath-hold conditions, high repeatability was obtained in cohorts of normal smokers and non-smokers.

Diagnosis of chronic obstructive pulmonary disease in lung cancer screening Computed Tomography scans: independent contribution of emphysema, air trapping and bronchial wall thickening

Background

Beyond lung cancer, screening CT contains additional information on other smoking related diseases (e.g. chronic obstructive pulmonary disease, COPD). Since pulmonary function testing is not regularly incorporated in lung cancer screening, imaging biomarkers for COPD are likely to provide important surrogate measures for disease evaluation. Therefore, this study aims to determine the independent diagnostic value of CT emphysema, CT air trapping and CT bronchial wall thickness for COPD in low-dose screening CT scans.

Methods

Prebronchodilator spirometry and volumetric inspiratory and expiratory chest CT were obtained on the same day in 1140 male lung cancer screening participants. Emphysema, air trapping and bronchial wall thickness were automatically quantified in the CT scans. Logistic regression analysis was performed to derivate a model to diagnose COPD. The model was internally validated using bootstrapping techniques.

Results

Each of the three CT biomarkers independently contributed diagnostic value for COPD, additional to age, body mass index, smoking history and smoking status. The diagnostic model that included all three CT biomarkers had a sensitivity and specificity of 73.2% and 88.%, respectively. The positive and negative predictive value were 80.2% and 84.2%, respectively. Of all participants, 82.8% was assigned the correct status. The C-statistic was 0.87, and the Net Reclassification Index compared to a model without any CT biomarkers was 44.4%. However, the added value of the expiratory CT data was limited, with an increase in Net Reclassification Index of 4.5% compared to a model with only inspiratory CT data.

Conclusion

Quantitatively assessed CT emphysema, air trapping and bronchial wall thickness each contain independent diagnostic information for COPD, and these imaging biomarkers might prove useful in the absence of lung function testing and may influence lung cancer screening strategy. Inspiratory CT biomarkers alone may be sufficient to identify patients with COPD in lung cancer screening setting.