Cigarette smoking and airway wall thickness on CT scan in a multi-ethnic cohort: the MESA Lung Study

Airway Tree Caliber and Susceptibility to Pollution-associated Emphysema: MESA Air and Lung Studies

Rationale: Airway tree morphology varies in the general population and may modify the distribution and uptake of inhaled pollutants. Objectives: We hypothesized that smaller airway caliber would be associated with emphysema progression and would increase susceptibility to air pollutant-associated emphysema progression. Methods: MESA (Multi-Ethnic Study of Atherosclerosis) is a general population cohort of adults 45-84 years old from six U.S. communities. Airway tree caliber was quantified as the mean of airway lumen diameters measured from baseline cardiac computed tomography (CT) (2000-2002). Percentage emphysema, defined as percentage of lung pixels below -950 Hounsfield units, was assessed up to five times per participant via cardiac CT scan (2000-2007) and equivalent regions on lung CT scan (2010-2018). Long-term outdoor air pollutant concentrations (particulate matter with an aerodynamic diameter ⩽2.5 μm, oxides of nitrogen, and ozone) were estimated at the residential address with validated spatiotemporal models. Linear mixed models estimated the association between airway tree caliber and emphysema progression; modification of pollutant-associated emphysema progression was assessed using multiplicative interaction terms. Measurements and Main Results: Among 6,793 participants (mean ± SD age, 62 ± 10 yr), baseline airway tree caliber was 3.95 ± 1.1 mm and median (interquartile range) of percentage emphysema was 2.88 (1.21-5.68). In adjusted analyses, 10-year emphysema progression rate was 0.75 percentage points (95% confidence interval, 0.54-0.96%) higher in the smallest compared with largest airway tree caliber quartile. Airway tree caliber also modified air pollutant-associated emphysema progression. Conclusions: Smaller airway tree caliber was associated with accelerated emphysema progression and modified air pollutant-associated emphysema progression. A better understanding of the mechanisms of airway-alveolar homeostasis and air pollutant deposition is needed.

Chest CT-based automated vertebral fracture assessment using artificial intelligence and morphologic features

BACKGROUND

Spinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD.

PURPOSE

We present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi-parametric freeze-and-grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts.

METHODS

A chest CT-based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel-level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi-parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior-posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi-site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n = 3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n = 80) and validation (n = 40) datasets for the DL-based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n = 236) was evaluated.

RESULTS

The vertebral segmentation module achieved a Dice score of .984 as compared to manual outlining results as reference (n = 100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being .980 and .989, respectively. The vertebral labelling module achieved 100% accuracy (n = 100). For low dose CT, the segmentation module produced image-level minimum and maximum Dice scores of .995 and .999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n = 236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants.

CONCLUSIONS

Our CT-based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population-based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population-based studies has increased to reduce cumulative radiation exposure.

CT-derived pectoralis composition and incident pneumonia hospitalization using fully automated deep-learning algorithm: multi-ethnic study of atherosclerosis

BACKGROUND

Pneumonia-related hospitalization may be associated with advanced skeletal muscle loss due to aging (i.e., sarcopenia) or chronic illnesses (i.e., cachexia). Early detection of muscle loss may now be feasible using deep-learning algorithms applied on conventional chest CT.

OBJECTIVES

To implement a fully automated deep-learning algorithm for pectoralis muscle measures from conventional chest CT and investigate longitudinal associations between these measures and incident pneumonia hospitalization according to Chronic Obstructive Pulmonary Disease (COPD) status.

MATERIALS AND METHODS

This analysis from the Multi-Ethnic Study of Atherosclerosis included participants with available chest CT examinations between 2010 and 2012. We implemented pectoralis muscle composition measures from a fully automated deep-learning algorithm (Mask R-CNN, built on the Faster Region Proposal Network (R-) Convolutional Neural Network (CNN) with an extension for mask identification) for two-dimensional segmentation. Associations between CT-derived measures and incident pneumonia hospitalizations were evaluated using Cox proportional hazards models adjusted for multiple confounders which include but are not limited to age, sex, race, smoking, BMI, physical activity, and forced-expiratory-volume-at-1 s-to-functional-vital-capacity ratio. Stratification analyses were conducted based on baseline COPD status.

RESULTS

This study included 2595 participants (51% female; median age: 68 (IQR: 61, 76)) CT examinations for whom we implemented deep learning-derived measures for longitudinal analyses. Eighty-six incident pneumonia hospitalizations occurred during a median 6.67-year follow-up. Overall, pectoralis muscle composition measures did not predict incident pneumonia. However, in fully-adjusted models, only among participants with COPD (N = 507), CT measures like extramyocellular fat index (hazard ratio: 1.98, 95% CI: 1.22, 3.21, p value: 0.02), were independently associated with incident pneumonia.

CONCLUSION

Reliable deep learning-derived pectoralis muscle measures could predict incident pneumonia hospitalization only among participants with known COPD.

CLINICAL RELEVANCE STATEMENT

Pectoralis muscle measures obtainable at zero additional cost or radiation exposure from any chest CT may have independent predictive value for clinical outcomes in chronic obstructive pulmonary disease patients.

KEY POINTS

•Identification of independent and modifiable risk factors of pneumonia can have important clinical impact on patients with chronic obstructive pulmonary disease. •Opportunistic CT measures of adipose tissue within pectoralis muscles using deep-learning algorithms can be quickly obtainable at zero additional cost or radiation exposure. •Deep learning-derived pectoralis muscle measurements of intermuscular fat and its subcomponents are independently associated with subsequent incident pneumonia hospitalization.

A Survey on Artificial Intelligence in Pulmonary Imaging

Over the last decade, deep learning (DL) has contributed a paradigm shift in computer vision and image recognition creating widespread opportunities of using artificial intelligence in research as well as industrial applications. DL has been extensively studied in medical imaging applications, including those related to pulmonary diseases. Chronic obstructive pulmonary disease, asthma, lung cancer, pneumonia, and, more recently, COVID-19 are common lung diseases affecting nearly 7.4% of world population. Pulmonary imaging has been widely investigated toward improving our understanding of disease etiologies and early diagnosis and assessment of disease progression and clinical outcomes. DL has been broadly applied to solve various pulmonary image processing challenges including classification, recognition, registration, and segmentation. This paper presents a survey of pulmonary diseases, roles of imaging in translational and clinical pulmonary research, and applications of different DL architectures and methods in pulmonary imaging with emphasis on DL-based segmentation of major pulmonary anatomies such as lung volumes, lung lobes, pulmonary vessels, and airways as well as thoracic musculoskeletal anatomies related to pulmonary diseases.

Lung Imaging in COPD Part 1: Clinical Usefulness

COPD is a condition characterized by chronic airflow obstruction resulting from chronic bronchitis, emphysema, or both. The clinical picture is usually progressive with respiratory symptoms such as exertional dyspnea and chronic cough. For many years, spirometry was used to establish a diagnosis of COPD. Recent advancements in imaging techniques allow quantitative and qualitative analysis of the lung parenchyma as well as related airways and vascular and extrapulmonary manifestations of COPD. These imaging methods may allow prognostication of disease and shed light on the efficacy of pharmacologic and nonpharmacologic interventions. This is the first of a two-part series of articles on the usefulness of imaging methods in COPD, and it highlights useful information that clinicians can obtain from these imaging studies to make more accurate diagnosis and therapeutic decisions.

Quantitative CT Analysis Based on Smoking Habits and Chronic Obstructive Pulmonary Disease in Patients with Normal Chest CT

PURPOSE

To assess normal CT scans with quantitative CT (QCT) analysis based on smoking habits and chronic obstructive pulmonary disease (COPD).

MATERIALS AND METHODS

From January 2013 to December 2014, 90 male patients with normal chest CT and quantification analysis results were enrolled in our study [non-COPD never-smokers (n = 38) and smokers (n = 45), COPD smokers (n = 7)]. In addition, an age-matched cohort study was performed for seven smokers with COPD. The square root of the wall area of a hypothetical bronchus of internal perimeter 10 mm (Pi10), skewness, kurtosis, mean lung attenuation (MLA), and percentage of low attenuation area (%LAA) were evaluated.

RESULTS

Among patients without COPD, the Pi10 of smokers (4.176 ± 0.282) was about 0.1 mm thicker than that of never-smokers (4.070 ± 0.191, p = 0.047), and skewness and kurtosis of smokers (2.628 ± 0.484 and 6.448 ± 3.427) were lower than never-smokers (2.884 ± 0.624, p = 0.038 and 8.594 ± 4.944, p = 0.02). The Pi10 of COPD smokers (4.429 ± 0.435, n = 7) was about 0.4 mm thicker than never-smokers without COPD (3.996 ± 0.115, n = 14, p = 0.005). There were no significant differences in MLA and %LAA between groups (p > 0.05).

CONCLUSION

Even on normal CT scans, QCT showed that the airway walls of smokers are thicker than never-smokers regardless of COPD and it preceded lung parenchymal changes.

Association of dysanapsis with mortality among older adults

Dysanapsis – an anthropometric mismatch between airway tree calibre and lung size that is common in the general population – is strongly associated with all-cause mortality and increases susceptibility to tobacco smoking-related diseases https://bit.ly/42oDe8J

Airway Detection in COPD at Low-Dose CT Using Deep Learning and Multiparametric Freeze and Grow

PURPOSE

To present and validate a fully automated airway detection method at low-dose CT in patients with chronic obstructive pulmonary disease (COPD).

MATERIALS AND METHODS

In this retrospective study, deep learning (DL) and freeze-and-grow (FG) methods were optimized and applied to automatically detect airways at low-dose CT. Four data sets were used: two data sets consisting of matching standard- and low-dose CT scans from the Genetic Epidemiology of COPD (COPDGene) phase II (2014-2017) cohort (n = 2 × 236; mean age ± SD, 70 years ± 9; 123 women); one data set consisting of low-dose CT scans from the COPDGene phase III (2018-2020) cohort (n = 335; mean age ± SD, 73 years ± 8; 173 women); and one data set consisting of low-dose, anonymized CT scans from the 2003 Dutch-Belgian Randomized Lung Cancer Screening trial (n = 55) acquired by using different CT scanners. Performance measures for different methods were computed and compared by using the Wilcoxon signed rank test.

RESULTS

At low-dose CT, 56 294 of 62 480 (90.1%) airways of the reference total airway count (TAC) and 32 109 of 37 864 (84.8%) airways of the peripheral TAC (TAC_p), detected at standard-dose CT, were detected. Significant losses (P < .001) of 14 526 of 76 453 (19.0%) airways and 884 of 6908 (12.8%) airways in the TAC and 12 256 of 43 462 (28.2%) airways and 699 of 3882 (18.0%) airways in the TAC_p were observed, respectively, for the multiprotocol and multiscanner data without retraining. When using the automated low-dose CT method, TAC values of 347, 342, 323, and 266 and TAC_p values of 205, 202, 289, and 141 were observed for those who have never smoked and participants at Global Initiative for Chronic Obstructive Lung Disease stages 0, 1, and 2, respectively, which were superior to the respective values previously reported for matching groups when using a semiautomated method at standard-dose CT.

CONCLUSION

A low-cost, automated CT-based airway detection method was suitable for investigation of airway phenotypes at low-dose CT.Keywords: Airway, Airway Count, Airway Detection, Chronic Obstructive Pulmonary Disease, CT, Deep Learning, Generalizability, Low-Dose CT, Segmentation, Thorax, LungClinical trial registration no. NCT00608764 Supplemental material is available for this article. © RSNA, 2022.

Small Airway Wall Thickening Assessed by Computerized Tomography Is Associated With Low Lung Function in Chinese Carbon Black Packers

Nanoscale carbon black as virtually pure elemental carbon can deposit deep in the lungs and cause pulmonary injury. Airway remodeling assessed using computed tomography (CT) correlates well with spirometry in patients with obstructive lung diseases. Structural airway changes caused by carbon black exposure remain unknown. Wall and lumen areas of sixth and ninth generations of airways in 4 lobes were quantified using end-inhalation CT scans in 58 current carbon black packers (CBPs) and 95 non-CBPs. Carbon content in airway macrophage (CCAM) in sputum was quantified to assess the dose-response. Environmental monitoring and CCAM showed a much higher level of elemental carbon exposure in CBPs, which was associated with higher wall area and lower lumen area with no change in total airway area for either airway generation. This suggested small airway wall thickening is a major feature of airway remodeling in CBPs. When compared with wall or lumen areas, wall area percent (WA%) was not affected by subject characteristics or lobar location and had greater measurement reproducibility. The effect of carbon black exposure status on WA% did not differ by lobes. CCAM was associated with WA% in a dose-dependent manner. CBPs had lower FEV1 (forced expiratory volume in 1 s) than non-CBPs and mediation analysis identified that a large portion (41-72%) of the FEV1 reduction associated with carbon black exposure could be explained by WA%. Small airway wall thickening as a major imaging change detected by CT may underlie the pathology of lung function impairment caused by carbon black exposure.

Chronic exposure to diesel exhaust may cause small airway wall thickening without lumen narrowing: a quantitative computerized tomography study in Chinese diesel engine testers

Background

Diesel exhaust (DE) is a major source of ultrafine particulate matters (PM) in ambient air and contaminates many occupational settings. Airway remodeling assessed using computerized tomography (CT) correlates well with spirometry in patients with obstructive lung diseases. Structural changes of small airways caused by chronic DE exposure is unknown. Wall and lumen areas of 6th and 9th generations of four candidate airways were quantified using end-inhalation CT scans in 78 diesel engine testers (DET) and 76 non-DETs. Carbon content in airway macrophage (CCAM) in sputum was quantified to assess the dose-response relationship.

Results

Environmental monitoring and CCAM showed a much higher PM exposure in DETs, which was associated with higher wall area and wall area percent for 6th generation of airways. However, no reduction in lumen area was identified. No study subjects met spirometry diagnosis of airway obstruction. This suggested that small airway wall thickening without lumen narrowing may be an early feature of airway remodeling in DETs. The effect of DE exposure status on wall area percent did not differ by lobes or smoking status. Although the trend test was of borderline significance between categorized CCAM and wall area percent, subjects in the highest CCAM category has a 14% increase in wall area percent for the 6th generation of airways compared to subjects in the lowest category. The impact of DE exposure on FEV1 can be partially explained by the wall area percent with mediation effect size equal to 20%, P_perm = 0.028).

Conclusions

Small airway wall thickening without lumen narrowing may be an early image feature detected by CT and underlie the pathology of lung injury in DETs. The pattern of changes in small airway dimensions, i.e., thicker airway wall without lumen narrowing caused by occupational DE exposure was different to that (i.e., thicker airway wall with lumen narrowing) seen in our previous study of workers exposed to nano-scale carbon black aerosol, suggesting constituents other than carbon cores may contribute to such differences. Our study provides some imaging indications of the understanding of the pulmonary toxicity of combustion derived airborne particulate matters in humans.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12989-021-00406-1.

Generation-based study of airway remodeling in smokers with normal-looking CT with normalization to control inter-subject variability

PURPOSE

With the help of quantitative computed tomography (QCT), it is possible to identify smoking-associated airway remodeling. However, there is currently little information on whether QCT-based airway metrics are sensitive to early airway wall remodeling in subclinical phases of smoking-associated airway disease. This study aimed to evaluate a predictive model that normalized airway parameters and investigate structural airway alterations in smokers with normal-looking CT using the normalization scheme.

METHODS

In this retrospective analysis, 222 non-smokers (male 97, female 125) and 69 smokers (male 66, female 3) from January 2014 to December 2016 were included, and airway parameters were quantitatively analyzed. To control inter-subject variability, multiple linear regressions of tracheal wall thickness (WT), diameter (D), and luminal area (LA) were performed, adjusted for age, sex, and height. Using this normalization scheme, airway parameters with matched generation were compared between smokers and non-smokers.

RESULTS

Using the normalization scheme, it was possible to assess generation-based structural alterations of the airways in subclinical smokers. Smokers showed diffuse luminal narrowing of airways for most generations (P < 0.05, except 3rd generation), no change in wall thickness of the proximal bronchi (1st-3rd generation), and a thinning of distal airways (P <0.05, ≥4th generation).

CONCLUSION

QCT assessment for subclinical smokers can help identify minimal structural changes in airways induced by smoking.

A CT-Based Automated Algorithm for Airway Segmentation Using Freeze-and-Grow Propagation and Deep Learning

Chronic obstructive pulmonary disease (COPD) is a common lung disease, and quantitative CT-based bronchial phenotypes are of increasing interest as a means of exploring COPD sub-phenotypes, establishing disease progression, and evaluating intervention outcomes. Reliable, fully automated, and accurate segmentation of pulmonary airway trees is critical to such exploration. We present a novel approach of multi-parametric freeze-and-grow (FG) propagation which starts with a conservative segmentation parameter and captures finer details through iterative parameter relaxation. First, a CT intensity-based FG algorithm is developed and applied for airway tree segmentation. A more efficient version is produced using deep learning methods generating airway lumen likelihood maps from CT images, which are input to the FG algorithm. Both CT intensity- and deep learning-based algorithms are fully automated, and their performance, in terms of repeat scan reproducibility, accuracy, and leakages, is evaluated and compared with results from several state-of-the-art methods including an industry-standard one, where segmentation results were manually reviewed and corrected. Both new algorithms show a reproducibility of 95% or higher for total lung capacity (TLC) repeat CT scans. Experiments on TLC CT scans from different imaging sites at standard and low radiation dosages show that both new algorithms outperform the other methods in terms of leakages and branch-level accuracy. Considering the performance and execution times, the deep learning-based FG algorithm is a fully automated option for large multi-site studies.

LOCALLY ADAPTIVE HALF-MAX METHODS FOR AIRWAY LUMEN-AREA AND WALL-THICKNESS AND THEIR REPEAT CT SCAN REPRODUCIBILITY

Quantitative computed tomography (CT)-based characterization of bronchial metrics is increasingly being used to investigate chronic obstructive pulmonary disease (COPD)-related phenotypes. Automated methods for airway measurements benefit large multi-site studies by reducing cost and subjectivity errors. Critical challenges for CT-based analysis of airway morphology are related to location of lumen and wall transitions in the presence of varying scales and intensity-contrasts from proximal to distal sites. This paper introduces locally adaptive half-max methods to locate airway lumen and wall transitions and compute cross-sectional lumen area and wall-thickness. Also, the method uses a consistency analysis of wall-thickness to avoid adjoining-structure-artifacts. Experimental results show that computed bronchial measures at individual anatomic airway tree locations are repeat CT scan reproducible with intra-class correlation coefficient (ICC) values exceeding 0.9 and 0.8 for lumen-area and wall-thickness, respectively. Observed ICC values for derived morphologic measures, e.g., lumen-area compactness (ICC>0.67) and tapering (ICC>0.47) are relatively lower.

A Genetic Risk Score Associated with Chronic Obstructive Pulmonary Disease Susceptibility and Lung Structure on Computed Tomography

Rationale: Chronic obstructive pulmonary disease (COPD) has been associated with numerous genetic variants, yet the extent to which its genetic risk is mediated by variation in lung structure remains unknown.Objectives: To characterize associations between a genetic risk score (GRS) associated with COPD susceptibility and lung structure on computed tomography (CT).Methods: We analyzed data from MESA Lung (Multi-Ethnic Study of Atherosclerosis Lung Study), a U.S. general population-based cohort, and SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study). A weighted GRS was calculated from 83 SNPs that were previously associated with lung function. Lung density, spatially matched airway dimensions, and airway counts were assessed on full-lung CT. Generalized linear models were adjusted for age, age squared, sex, height, principal components of genetic ancestry, smoking status, pack-years, CT model, milliamperes, and total lung volume.Measurements and Main Results: MESA Lung and SPIROMICS contributed 2,517 and 2,339 participants, respectively. Higher GRS was associated with lower lung function and increased COPD risk, as well as lower lung density, smaller airway lumens, and fewer small airways, without effect modification by smoking. Adjustment for CT lung structure, particularly small airway measures, attenuated associations between the GRS and FEV₁/FVC by 100% and 60% in MESA and SPIROMICS, respectively. Lung structure (P < 0.0001), but not the GRS (P > 0.10), improved discrimination of moderate-to-severe COPD cases relative to clinical factors alone.Conclusions: A GRS associated with COPD susceptibility was associated with CT lung structure. Lung structure may be an important mediator of heritability and determinant of personalized COPD risk.

Prediction of Pulmonary Function in Patients with Chronic Obstructive Pulmonary Disease: Correlation with Quantitative CT Parameters

Objective

We aimed to evaluate correlations between computed tomography (CT) parameters and pulmonary function test (PFT) parameters according to disease severity in patients with chronic obstructive pulmonary disease (COPD), and to determine whether CT parameters can be used to predict PFT indices.

Materials and Methods

A total of 370 patients with COPD were grouped based on disease severity according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) I–IV criteria. Emphysema index (EI), air-trapping index, and airway parameters such as the square root of wall area of a hypothetical airway with an internal perimeter of 10 mm (Pi10) were measured using automatic segmentation software. Clinical characteristics including PFT results and quantitative CT parameters according to GOLD criteria were compared using ANOVA. The correlations between CT parameters and PFT indices, including the ratio of forced expiratory volume in one second to forced vital capacity (FEV₁/FVC) and FEV₁, were assessed. To evaluate whether CT parameters can be used to predict PFT indices, multiple linear regression analyses were performed for all patients, Group 1 (GOLD I and II), and Group 2 (GOLD III and IV).

Results

Pulmonary function deteriorated with increase in disease severity according to the GOLD criteria (p < 0.001). Parenchymal attenuation parameters were significantly worse in patients with higher GOLD stages (p < 0.001), and Pi10 was highest for patients with GOLD III (4.41 ± 0.94 mm). Airway parameters were nonlinearly correlated with PFT results, and Pi10 demonstrated mild correlation with FEV₁/FVC in patients with GOLD II and III (r = 0.16, p = 0.06 and r = 0.21, p = 0.04, respectively). Parenchymal attenuation parameters, airway parameters, EI, and Pi10 were identified as predictors of FEV₁/FVC for the entire study sample and for Group 1 (R² = 0.38 and 0.22, respectively; p < 0.001). However, only parenchymal attenuation parameter, EI, was identified as a predictor of FEV₁/FVC for Group 2 (R² = 0.37, p < 0.001). Similar results were obtained for FEV₁.

Conclusion

Airway and parenchymal attenuation parameters are independent predictors of pulmonary function in patients with mild COPD, whereas parenchymal attenuation parameters are dominant independent predictors of pulmonary function in patients with severe COPD.

Imaging-based clusters in former smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and intermediate outcome measures in COPD study (SPIROMICS)

BACKGROUND

Quantitative computed tomographic (QCT) imaging-based metrics enable to quantify smoking induced disease alterations and to identify imaging-based clusters for current smokers. We aimed to derive clinically meaningful sub-groups of former smokers using dimensional reduction and clustering methods to develop a new way of COPD phenotyping.

METHODS

An imaging-based cluster analysis was performed for 406 former smokers with a comprehensive set of imaging metrics including 75 imaging-based metrics. They consisted of structural and functional variables at 10 segmental and 5 lobar locations. The structural variables included lung shape, branching angle, airway-circularity, airway-wall-thickness, airway diameter; the functional variables included regional ventilation, emphysema percentage, functional small airway disease percentage, Jacobian (volume change), anisotropic deformation index (directional preference in volume change), and tissue fractions at inspiration and expiration.

RESULTS

We derived four distinct imaging-based clusters as possible phenotypes with the sizes of 100, 80, 141, and 85, respectively. Cluster 1 subjects were asymptomatic and showed relatively normal airway structure and lung function except airway wall thickening and moderate emphysema. Cluster 2 subjects populated with obese females showed an increase of tissue fraction at inspiration, minimal emphysema, and the lowest progression rate of emphysema. Cluster 3 subjects populated with older males showed small airway narrowing and a decreased tissue fraction at expiration, both indicating air-trapping. Cluster 4 subjects populated with lean males were likely to be severe COPD subjects showing the highest progression rate of emphysema.

CONCLUSIONS

QCT imaging-based metrics for former smokers allow for the derivation of statistically stable clusters associated with unique clinical characteristics. This approach helps better categorization of COPD sub-populations; suggesting possible quantitative structural and functional phenotypes.

Prognostic Significance of Large Airway Dimensions on Computed Tomography in the General Population. The Multi-Ethnic Study of Atherosclerosis (MESA) Lung Study

RATIONALE

Large airway dimensions on computed tomography (CT) have been associated with lung function, symptoms, and exacerbations in chronic obstructive pulmonary disease (COPD), as well as with symptoms in smokers with preserved spirometry. Their prognostic significance in persons without lung disease remains undefined.

OBJECTIVES

To examine associations between large airway dimensions on CT and respiratory outcomes in a population-based cohort of adults without prevalent lung disease.

METHODS

The Multi-Ethnic Study of Atherosclerosis recruited participants ages 45-84 years without cardiovascular disease in 2000-2002; we excluded participants with prevalent chronic lower respiratory disease (CLRD). Spirometry was measured in 2004-2006 and 2010-2012. CLRD hospitalizations and deaths were classified by validated criteria through 2014. The average wall thickness for a hypothetical airway of 10-mm lumen perimeter on CT (Pi10) was calculated using measures of airway wall thickness and lumen diameter. Models were adjusted for age, sex, principal components of ancestry, body mass index, smoking, pack-years, scanner, percent emphysema, genetic risk score, and initial forced expiratory volume in 1 second (FEV1) percent predicted.

RESULTS

Greater Pi10 was associated with 9% faster FEV1 decline (95% confidence interval [CI], 2 to 15%; P = 0.012) and increased incident COPD (odds ratio, 2.22; 95% CI, 1.43-3.45; P = 0.0004) per standard deviation among 1,830 participants. Over 78,147 person-years, higher Pi10 was associated with a 57% higher risk of first CLRD hospitalization or mortality (P = 0.0496) per standard deviation. Of Pi10's component measures, both greater airway wall thickness and narrower lumen predicted incident COPD and CLRD clinical events.

CONCLUSIONS

In adults without CLRD, large airway dimensions on CT were prospectively associated with accelerated lung function decline and increased risks of COPD and CLRD hospitalization and mortality.

Differences in Respiratory Symptoms and Lung Structure Between Hispanic and Non-Hispanic White Smokers: A Comparative Study

Background: Prior studies have demonstrated that U.S. Hispanic smokers have a lower risk of decline in lung function and chronic obstructive pulmonary disease (COPD) compared with non-Hispanic whites (NHW). This suggests there might be racial-ethnic differences in susceptibility in cigarette smoke-induced respiratory symptoms, lung parenchymal destruction, and airway and vascular disease, as well as in extra-pulmonary manifestations of COPD. Therefore, we aimed to explore respiratory symptoms, lung function, and pulmonary and extra-pulmonary structural changes in Hispanic and NHW smokers. Methods: We compared respiratory symptoms, lung function, and computed tomography (CT) measures of emphysema-like tissue, airway disease, the branching generation number (BGN) to reach a 2-mm-lumen-diameter airway, and vascular pruning as well as muscle and fat mass between 39 Hispanic and 39 sex-, age- and smoking exposure-matched NHW smokers. Results: Hispanic smokers had higher odds of dyspnea than NHW after adjustment for COPD and asthma statuses (odds ratio[OR] = 2.96; 95% confidence interval [CI] 1.09-8.04), but no significant differences were found in lung function and CT measurements. Conclusions: While lung function and CT measures of the lung structure were similar, dyspnea is reported more frequently by Hispanic than matched-NHW smokers. It seems to be an impossible puzzle but it's easy to solve a Rubik' Cube using a few algorithms.

Airway wall thickness on HRCT scans decreases with age and increases with smoking

Background

To investigate if age, gender and smoking are associated with airway wall thickness (AWT) measured by high resolution computed tomography (HRCT) and if higher AWT is associated with lower levels of pulmonary function in healthy current- and never-smokers with a wide age range.

Methods

HRCT scans were performed in 99 subjects (48 never- and 51 current-smokers, median age 39 years [IQR 22 – 54], 57% males). The AWT at an internal perimeter of 10 mm (AWT Pi10) was calculated as an overall measurement of AWT, based on all measurements throughout the lungs. Extensive pulmonary function testing was performed in all subjects.

Results

Higher age was associated with a lower AWT Pi10 (b = −0.003, p < 0.001). Current-smokers had a higher AWT Pi10 than never-smokers (mean 0.49 mm versus 0.44 mm, p = 0.022). In multivariate analysis, age and current-smoking were independently associated with AWT Pi10 (age b = −0.002, p < 0.001, current-smoking b = 0.041, p = 0.021), whereas gender was not (b = 0.011, p = 0.552). Higher AWT Pi10 was associated with a lower FEV₁, FEV₁/FVC, FEF_25–75 and higher R5, R20 and X5.

Conclusions

AWT decreases with higher age, possibly reflecting structural changes of the airways. Additionally, current-smokers have a higher AWT, possibly due to remodeling or inflammation. Finally, higher AWT is associated with a lower level of pulmonary function, even in this population of healthy subjects.

Trial registration

This Study was registered at www.clinicaltrials.gov with number NCT00848406 on 19 February 2009.

Electronic supplementary material

The online version of this article (doi:10.1186/s12890-017-0363-0) contains supplementary material, which is available to authorized users.

Computed Tomographic Airway Morphology in Chronic Obstructive Pulmonary Disease. Remodeling or Innate Anatomy?

Computed tomographic measures of central airway morphology have been used in clinical, epidemiologic, and genetic investigation as an inference of the presence and severity of small-airway disease in smokers. Although several association studies have brought us to believe that these computed tomographic measures reflect airway remodeling, a careful review of such data and more recent evidence may reveal underappreciated complexity to these measures and limitations that prompt us to question that belief. This Perspective offers a review of seminal papers and alternative explanations of their data in the light of more recent evidence. The relationships between airway morphology and lung function are observed in subjects who never smoked, implying that native airway structure indeed contributes to lung function; computed tomographic measures of central airways such as wall area, lumen area, and total bronchial area are smaller in smokers with chronic obstructive pulmonary disease versus those without chronic obstructive pulmonary disease; and the airways are smaller as disease severity increases. The observations suggest that (1) native airway morphology likely contributes to the relationships between computed tomographic measures of airways and lung function; and (2) the presence of smaller airways in those with chronic obstructive pulmonary disease versus those without chronic obstructive pulmonary disease as well as their decrease with disease severity suggests that smokers with chronic obstructive pulmonary disease may simply have smaller airways to begin with, which put them at greater risk for the development of smoking-related disease.

Poor symptom control is associated with reduced CT scan segmental airway lumen area in smokers with asthma

BACKGROUND

Cigarette smoking is associated with worse symptoms in asthma and abnormal segmental airways in healthy subjects. We tested the hypothesis that current symptom control in smokers with asthma is associated with altered segmental airway dimensions measured by CT scan.

METHODS

In 93 subjects with mild, moderate, and severe asthma (smokers and never smokers), we recorded Asthma Control Questionnaire-6 (ACQ-6) score, spirometry (FEV1; forced expiratory flow rate, midexpiratory phase [FEF(25%-75%)]), residual volume (RV), total lung capacity (TLC), and CT scan measures of the right bronchial (RB) and left bronchial (LB) segmental airway dimensions (wall thickness, mm; lumen area, mm²) in the RB3/LB3, RB6/LB6, and RB10/LB10 (smaller) airways.

RESULTS

The CT scan segmental airway (RB10 and LB10) lumen area was reduced in smokers with asthma compared with never smokers with asthma; RB10, 16.6 mm² (interquartile range, 12.4-19.2 mm²) vs 19.6 mm² (14.7-24.2 mm²) (P = .01); LB10, 14.8 mm² (12.1-19.0 mm²) vs 19.9 mm² (14.5-25.0 mm²) (P = .003), particularly in severe disease, with no differences in wall thickness or in larger airway (RB3 and LB3) dimensions. In smokers with asthma, a reduced lumen area in fifth-generation airways (RB10 or LB10) was associated with poor symptom control (higher ACQ-6 score) (-0.463 [-0.666 to -0.196], P = .001, and -0.401 [-0.619 to -0.126], P = .007, respectively) and reduced postbronchodilator FEF(25%-75%) (0.521 [0.292-0.694], P < .001, and [0.471 [0.236-0.654], P = .001, respectively) and higher RV/TLC %.

CONCLUSIONS

The CT scan segmental airway lumen area is reduced in smokers with asthma compared with never smokers with asthma, particularly in severe disease, and is associated with worse current symptom control and small airway dysfunction.

Understanding the contribution of native tracheobronchial structure to lung function: CT assessment of airway morphology in never smokers

Background

Computed tomographic (CT) airway lumen narrowing is associated with lower lung function. Although volumetric CT measures of airways (wall volume [WV] and lumen volume [LV]) compared to cross sectional measures can more accurately reflect bronchial morphology, data of their use in never smokers is scarce. We hypothesize that native tracheobronchial tree morphology as assessed by volumetric CT metrics play a significant role in determining lung function in normal subjects. We aimed to assess the relationships between airway size, the projected branching generation number (BGN) to reach airways of <2mm lumen diameter –the site for airflow obstruction in smokers- and measures of lung function including forced expiratory volume in 1 second (FEV₁) and forced expiratory flow between 25% and 75% of vital capacity (FEF 25–75).

Methods

We assessed WV and LV of segmental and subsegmental airways from six bronchial paths as well as lung volume on CT scans from 106 never smokers. We calculated the lumen area ratio of the subsegmental to segmental airways and estimated the projected BGN to reach a <2mm-lumen-diameter airway assuming a dichotomized tracheobronchial tree model. Regression analysis was used to assess the relationships between airway size, BGN, FEF 25–75, and FEV₁.

Results

We found that in models adjusted for demographics, LV and WV of segmental and subsegmental airways were directly related to FEV₁ (P <0.05 for all the models). In adjusted models for age, sex, race, LV and lung volume or height, the projected BGN was directly associated with FEF 25–75 and FEV₁ (P = 0.001) where subjects with lower FEV₁ had fewer calculated branch generations between the subsegmental bronchus and small airways. There was no association between airway lumen area ratio and lung volume.

Conclusion

We conclude that in never smokers, those with smaller central airways had lower airflow and those with lower airflow had less parallel airway pathways independent of lung size. These findings suggest that variability in the structure of the tracheobronchial tree may influence the risk of developing clinically relevant smoking related airway obstruction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12931-015-0181-y) contains supplementary material, which is available to authorized users.

Chronic respiratory symptoms associated with airway wall thickening measured by thin-slice low-dose CT

OBJECTIVE

In lung cancer screening, the prevalence of chronic respiratory symptoms is high among heavy smokers. The purpose of this study was to compare CT-derived airway wall measurements between male smokers with and those without chronic respiratory symptoms.

MATERIALS AND METHODS

Fifty male heavy smokers with chronic respiratory symptoms (cough, excessive mucus secretion, dyspnea, and wheezing) and 50 without any respiratory symptom were randomly selected from the Dutch-Belgian Randomized Lung Cancer Screening Trial. Thin-slice low-dose CT images were evaluated with dedicated software for airway measurements. Wall area percentage and airway wall thickness were measured from trachea to bronchi in five different pulmonary lobes of airways with a luminal diameter of 5 mm or greater. Association between airway wall measurements and respiratory symptoms was analyzed by multiple linear regression adjusted for age, body mass index, smoking status, emphysema, and pulmonary function.

RESULTS

After adjustment for relevant factors, a significant positive association between airway wall measurements and respiratory symptoms was found in airways with a luminal diameter between 5 to 10 mm (p < 0.01), but not in airways measuring 10 mm or greater (p > 0.05). At the airway level between 5 to 10 mm, the mean wall area percentages were 51.5% ± 7.9%. Airway wall thicknesses were 1.54 ± 0.39 mm and 1.37 ± 0.35 mm (p < 0.001).

CONCLUSION

Male heavy smokers with chronic respiratory symptoms in lung cancer screening, who are at high-risk of chronic bronchitis, have bronchial wall thickening in airways with a luminal diameter of 5-10 mm but not in larger airways.

Development of quantitative computed tomography lung protocols

The purpose of this review article is to review the process of developing optimal computed tomography (CT) protocols for quantitative lung CT (QCT). In this review, we discuss the following important topics: QCT-derived metrics of lung disease; QCT scanning protocols; quality control; and QCT image processing software. We will briefly discuss several QCT-derived metrics of lung disease that have been developed for the assessment of emphysema, small airway disease, and large airway disease. The CT scanning protocol is one of the most important elements in a successful QCT. We will provide a detailed description of the current move toward optimizing the QCT protocol for the assessment of chronic obstructive pulmonary disorder and asthma. Quality control of CT images is also a very important part of the QCT process. We will discuss why it is necessary to use CT scanner test objects (phantoms) to provide frequent periodic checks on the CT scanner calibration to ensure precise and accurate CT numbers. We will discuss the use of QCT image processing software to segment the lung and extract the desired QCT metrics of lung disease. We will discuss the practical issues of using this software. The data obtained from the image processing software are then combined with those from other clinical examinations, health status questionnaires, pulmonary physiology, and genomics to increase our understanding of obstructive lung disease and improve our ability to design new therapies for these diseases.

Computed tomographic measures of airway morphology in smokers and never-smoking normals

Bronchial wall area percent (WA% = 100 × wall area/total bronchial cross sectional area) is a standard computed tomographic (CT) measure of central airway morphology utilized in smokers with chronic obstructive pulmonary disease (COPD). Although it provides significant clinical correlations, the range of reported WA% is narrow. This suggests limited macroscopic change in response to smoking or that remodeling proportionally affects the airway wall and lumen dimensions such that their ratio is preserved. The objective of this study is to assess central airway wall area (WA), lumen area (Ai), and total bronchial area (Ao) from CT scans of 5,179 smokers and 92 never smoking normal subjects. In smokers, WA, Ai, and Ao were positively correlated with forced expiratory volume in 1 s (FEV1) expressed as a percent of predicted (FEV1%), and the WA% was negatively correlated with FEV1% (P < 0.0001 for all comparisons). Importantly, smokers with lower FEV1% tended to have airways of smaller cross-sectional area with lower WA. The increases in the WA% across GOLD stages of chronic obstructive pulmonary disease (COPD) can therefore not be due to increases in WA. The data suggest two possible origins for the WA% increases: 1) central airway remodeling resulting in overall reductions in airway caliber in excess of the decreased WA or 2) those with COPD had smaller native airways before they began smoking. In both cases, these observations provide an explanation for the limited range of values of WA% across stages of COPD.

Airway tree segmentation in serial block-face cryomicrotome images of rat lungs

A highly automated method for the segmentation of airways in the serial block-face cryomicrotome images of rat lungs is presented. First, a point inside of the trachea is manually specified. Then, a set of candidate airway centerline points is automatically identified. By utilizing a novel path extraction method, a centerline path between the root of the airway tree and each point in the set of candidate centerline points is obtained. Local disturbances are robustly handled by a novel path extraction approach, which avoids the shortcut problem of standard minimum cost path algorithms. The union of all centerline paths is utilized to generate an initial airway tree structure, and a pruning algorithm is applied to automatically remove erroneous subtrees or branches. Finally, a surface segmentation method is used to obtain the airway lumen. The method was validated on five image volumes of Sprague-Dawley rats. Based on an expert-generated independent standard, an assessment of airway identification and lumen segmentation performance was conducted. The average of airway detection sensitivity was 87.4% with a 95% confidence interval (CI) of (84.9, 88.6)%. A plot of sensitivity as a function of airway radius is provided. The combined estimate of airway detection specificity was 100% with a 95% CI of (99.4, 100)%. The average number and diameter of terminal airway branches was 1179 and 159 μm, respectively. Segmentation results include airways up to 31 generations. The regression intercept and slope of airway radius measurements derived from final segmentations were estimated to be 7.22 μm and 1.005, respectively. The developed approach enables the quantitative studies of physiology and lung diseases in rats, requiring detailed geometric airway models.

Asthma and lung structure on computed tomography: the Multi-Ethnic Study of Atherosclerosis Lung Study

BACKGROUND

The potential consequences of asthma in childhood and young adulthood on lung structure in older adults have not been studied in a large, population-based cohort.

OBJECTIVE

The authors hypothesized that a history of asthma onset in childhood (age 18 years or before) or young adulthood (age 19-45 years) was associated with altered lung structure on computed tomography in later life.

METHODS

The Multi-Ethnic Study of Atherosclerosis Lung Study recruited 3965 participants and assessed asthma history by using standardized questionnaires, guideline-based spirometry, and segmental airway dimensions and percentage of low attenuation area (%LAA) on computed tomographic scans.

RESULTS

Asthma with onset in childhood and young adulthood was associated with large decrements in FEV(1) among participants with a mean age of 66 years (-365 mL and -343 mL, respectively; P < .001). Asthma with onset in childhood and young adulthood was associated with increased mean airway wall thickness standardized to an internal perimeter of 10 mm (0.1 mm, P < .001 for both), predominantly from narrower segmental airway lumens (-0.39 mm and -0.34 mm, respectively; P < .001). Asthma with onset in childhood and young adulthood also was associated with a greater %LAA (1.69% and 4.30%, respectively; P < .001). Findings were similar among never smokers, except that differential %LAA in childhood-onset asthma were not seen in them.

CONCLUSION

Asthma with onset in childhood or young adulthood was associated with reduced lung function, narrower airways, and among asthmatic patients who smoked, greater %LAA in later life.