Quantitative CT-based structural alterations of segmental airways in cement dust-exposed subjects

Structural and functional alterations of subjects with cement dust exposure: A longitudinal quantitative computed tomography-based study

Cement dust exposure (CDE) can be a risk factor for pulmonary disease, causing changes in segmental airways and parenchymal lungs. This study investigates longitudinal alterations in quantitative computed tomography (CT)-based metrics due to CDE. We obtained CT-based airway structural and lung functional metrics from CDE subjects with baseline CT and follow-up CT scans performed three years later. From the CT, we extracted wall thickness (WT) and bifurcation angle (θ) at total lung capacity (TLC) and functional residual capacity (FRC), respectively. We also computed air volume (V_air), tissue volume (V_tissue), global lung shape, percentage of emphysema (Emph%), and more. Clinical measures were used to associate with CT-based metrics. Three years after their baseline, the pulmonary function tests of CDE subjects were similar or improved, but there were significant alterations in the CT-based structural and functional metrics. The follow-up CT scans showed changes in θ at most of the central airways; increased WT at the subgroup bronchi; smaller V_air at TLC at all except the right upper and lower lobes; smaller V_tissue at all lobes in TLC and FRC except for the upper lobes in FRC; smaller global lung shape; and greater Emph% at the right upper and lower lobes. CT-based structural and functional variables are more sensitive to the early identification of CDE subjects, while most clinical lung function changes were not noticeable. We speculate that the significant long-term changes in CT are uniquely observed in CDE subjects, different from smoking-induced structural changes.

Quantitative computed tomography imaging-based classification of cement dust-exposed subjects with an artificial neural network technique

BACKGROUND AND OBJECTIVE

Cement dust exposure is likely to affect the structural and functional alterations in segmental airways and parenchymal lungs. This study develops an artificial neural network (ANN) model for identifying cement dust-exposed (CDE) subjects using quantitative computed tomography-based airway structural and functional features.

METHODS

We obtained the airway features in five central and five sub-grouped segmental regions and the lung features in five lobar regions and one total lung region from 311 CDE and 298 non-CDE (NCDE) subjects. The five-fold cross-validation method was adopted to train the following classification models:ANN, support vector machine (SVM), logistic regression (LR), and decision tree (DT). For all the classification models, linear discriminant analysis (LDA) and genetic algorithm (GA) were applied for dimensional reduction and hyperparameterization, respectively. The ANN model without LDA was also optimized by the GA method to observe the effect of the dimensional reduction.

RESULTS

The genetically optimized ANN model without the LDA method was the best in terms of the classification accuracy. The accuracy, sensitivity, and specificity of the GA-ANN model with four layers were greater than those of the other classification models (i.e., ANN, SVM, LR, and DT using LDA and GA methods) in the five-fold cross-validation. The average values of accuracy, sensitivity, and specificity for the five-fold cross-validation were 97.0%, 98.7%, and 98.6%, respectively.

CONCLUSIONS

We demonstrated herein that a quantitative computed tomography-based ANN model could more effectively detect CDE subjects when compared to their counterpart models. By employing the model, the CDE subjects may be identified early for therapeutic intervention.

Elastin in healthy and diseased lung

Elastic fibers are an essential part of the pulmonary extracellular matrix (ECM). Intact elastin is required for normal function and its damage contributes profoundly to the etiology and pathology of lung disease. This highlights the need for novel lung-specific imaging methodology that enables high-resolution 3D visualization of the ECM. We consider elastin's involvement in chronic respiratory disease and examine recent methods for imaging and modeling of the lung in the context of advances in lung tissue engineering for research and clinical application.

Inhalation dosimetry of nasally inhaled respiratory aerosols in the human respiratory tract with locally remodeled conducting lungs

Objective: Respiratory diseases are often accompanied by alterations to airway morphology. However, inhalation dosimetry data in remodeled airways are scarce due to the challenges in reconstructing diseased respiratory morphologies. This study aims to study the airway remodeling effects on the inhalation dosimetry of nasally inhaled nanoparticles in a nose-lung geometry that extends to G9 (ninth generation).Materials and methods: Statistical shape modeling was used to develop four diseased lung models with varying levels of bronchiolar dilation/constriction in the left-lower (LL) lobe (i.e. M1-M4). Respiratory airflow and particle deposition were simulated using a low Reynolds number k-ω turbulence model and a Lagrangian tracking approach.Results: Significant discrepancies were observed in the flow partitions between the left and right lungs, as well as between the lower and upper lobes of the left lung, which changed by 10-fold between the most dilated and constricted models.Much lower doses were predicted on the surface of the constricted LL bronchioles G4-G9, as well as into the peripheral airways beyond G9 of the LL lung. However, the LL lobar remodeling had little effect on the dosimetry in the nasopharynx, as well as on the total dosimetry in the nose-lung geometry (up to G9).Conclusion: It is suggested that airway remodeling may pose a higher viral infection risk to the host by redistributing the inhaled viruses to healthy lung lobes. Airway remodeling effects should also be considered in the treatment planning of inhalation therapies, not only because of the dosimetry variation from altered lung morphology but also its evolution as the disease progresses.

A comparative study of chest CT findings regarding the effects of regional dust exposure on patients with COPD living in urban areas and rural areas near cement plants

Background

The clinical and radiological presentation of chronic obstructive pulmonary disease (COPD) is heterogenous depending on the characterized sources of inflammation. This study aimed to evaluate COPD phenotypes associated with specific dust exposure.

Methods

This study was designed to compare the characteristics, clinical outcomes and radiological findings between two prospective COPD cohorts representing two distinguishing regions in the Republic of Korea; COPD in Dusty Area (CODA) and the Korean Obstructive Lung Disease (KOLD) cohort. A total of 733 participants (n = 186 for CODA, and n = 547 for KOLD) were included finally. A multivariate analysis to compare lung function and computed tomography (CT) measurements of both cohort studies after adjusting for age, sex, education, body mass index, smoking status, and pack-year, Charlson comorbidity index, and frequency of exacerbation were performed by entering the level of FEV1(%), biomass exposure and COPD medication into the model in stepwise.

Results

The mean wall area (MWA, %) became significantly lower in COPD patients in KOLD from urban and metropolitan area than those in CODA cohort from cement dust area (mean ± standard deviation [SD]; 70.2 ± 1.21% in CODA vs. 66.8 ± 0.88% in KOLD, p = 0.028) after including FEV1 in the model. COPD subjects in KOLD cohort had higher CT-emphysema index (EI, 6.07 ± 3.06 in CODA vs. 20.0 ± 2.21 in KOLD, p < 0.001, respectively). The difference in the EI (%) was consistently significant even after further adjustment of FEV1 (6.12 ± 2.88% in CODA vs. 17.3 ± 2.10% in KOLD, p = 0.002, respectively). However, there was no difference in the ratio of mean lung density (MLD) between the two cohorts (p = 0.077). Additional adjustment for biomass parameters and medication for COPD did not alter the statistical significance after entering into the analysis with COPD medication.

Conclusions

Higher MWA and lower EI were observed in COPD patients from the region with dust exposure. These results suggest that the imaging phenotype of COPD is influenced by specific environmental exposure.