Subtyping COPD by Using Visual and Quantitative CT Imaging Features

How anatomical impairments found on CT affect perfusion percentage assessed by SPECT/CT scan?

AIM

CT images can identify structural and opacity alterations of the lungs while nuclear medicine's lung perfusion studies show the homogeneity (or lack of) of blood perfusion on the organ. Therefore, the use of SPECT/CT in lung perfusion scintigraphies can help physicians to assess anatomical and functional alterations of the lungs and to differentiate between acute and chronic disease.

OBJECTIVE

To develop a computer-aided methodology to quantify the total global perfusion of the lungs via SPECT/CT images and to compare these results with parenchymal alterations obtained in CT images.

METHODS

39 perfusion SPECT/CT images collected retrospectively from the Nuclear Medicine Facility of Botucatu Medical School's Clinics Hospital in São Paulo, Brazil, were analyzed. Anatomical lung impairments (emphysema, collapsed and infiltrated tissue) and the functional percentage of the lungs (blood perfusion) were quantified from CT and SPECT images, with the aid of the free, open-source software 3D Slicer. The results obtained with 3D Slicer (3D-TGP) were also compared to the total global perfusion of each patient's found on their medical report, obtained from visual inspection of planar images (2D-TGP).

RESULTS

This research developed a novel and practical methodology for obtaining lungs' total global perfusion from SPECT/CT images in a semiautomatic manner. 3D-TGP versus 2D-TGP showed a bias of 7% with a variation up to 67% between the two methods. Perfusion percentage showed a weak positive correlation with infiltration (p = 0.0070 and ρ = 0.43) and collapsed parenchyma (p = 0.040 and ρ = 0.33).

CONCLUSIONS

This research brings meaningful contributions to the scientific community because it used a free open-source software to quantify the lungs blood perfusion via SPECT/CT images and pointed that the relationship between parenchyma alterations and the organ's perfusion capability might not be so direct, given compensatory mechanisms.

Assessment of pulmonary physiological changes caused by aging, cigarette smoking, and COPD with hyperpolarized 129Xe magnetic resonance

OBJECTIVE

To comprehensively assess the impact of aging, cigarette smoking, and chronic obstructive pulmonary disease (COPD) on pulmonary physiology using 129Xe MR.

METHODS

A total of 90 subjects were categorized into four groups, including healthy young (HY, n = 20), age-matched control (AMC, n = 20), asymptomatic smokers (AS, n = 28), and COPD patients (n = 22). 129Xe MR was utilized to obtain pulmonary physiological parameters, including ventilation defect percent (VDP), alveolar sleeve depth (h), apparent diffusion coefficient (ADC), total septal wall thickness (d), and ratio of xenon signal from red blood cells and interstitial tissue/plasma (RBC/TP).

RESULTS

Significant differences were found in the measured VDP (p = 0.035), h (p = 0.003), and RBC/TP (p = 0.003) between the HY and AMC groups. Compared with the AMC group, higher VDP (p = 0.020) and d (p = 0.048) were found in the AS group; higher VDP (p < 0.001), d (p < 0.001) and ADC (p < 0.001), and lower h (p < 0.001) and RBC/TP (p < 0.001) were found in the COPD group. Moreover, significant differences were also found in the measured VDP (p < 0.001), h (p < 0.001), ADC (p < 0.001), d (p = 0.008), and RBC/TP (p = 0.032) between the AS and COPD groups.

CONCLUSION

Our findings indicate that pulmonary structure and functional changes caused by aging, cigarette smoking, and COPD are various, and show a progressive deterioration with the accumulation of these risk factors, including cigarette smoking and COPD.

CLINICAL RELEVANCE STATEMENT

Pathophysiological changes can be difficult to comprehensively understand due to limitations in common techniques and multifactorial etiologies. 129Xe MRI can demonstrate structural and functional changes caused by several common factors and can be used to better understand patients' underlying pathology.

KEY POINTS

Standard techniques for assessing pathophysiological lung function changes, spirometry, and chest CT come with limitations. 129Xe MR demonstrated progressive deterioration with accumulation of the investigated risk factors, without these limitations. 129Xe MR can assess lung changes related to these risk factors to stage and evaluate the etiology of the disease.

Discordant definitions of small airway dysfunction between spirometry and parametric response mapping: the HRCT-based study

OBJECTIVES

To analyze the lung structure of small airway dysfunction (SAD) defined by spirometry and parametric response mapping (PRM) using high-resolution computed tomography (HRCT), and to analyze the predictive factors for SAD.

METHODS

A prospective study was conducted with 388 participants undergoing pulmonary function test (PFT) and inspiratory-expiratory chest CT scans. The clinical data and HRCT assessments of SAD patients defined by both methods were compared. A prediction model for SAD was constructed based on logistic regression.

RESULTS

SAD was defined in 122 individuals by spirometry and 158 by PRM. In HRCT visual assessment, emphysema, tree-in-bud sign, and bronchial wall thickening have higher incidence in SAD defined by each method. (p < 0.001). Quantitative CT showed that spirometry-SAD had thicker airway walls (p < 0.001), smaller lumens (p = 0.011), fewer bronchi (p < 0.001), while PRM-SAD had slender blood vessels. Predictive factors for spirometry-SAD were age, male gender, the volume percentage of emphysema in PRM (PRMEmph), tree-in-bud sign, bronchial wall thickening, bronchial count; for PRM-SAD were age, male gender, BMI, tree-in-bud sign, emphysema, the percentage of blood vessel volume with a cross-sectional area less than 1 mm2 (BV1/TBV). The area under curve (AUC) values for the fitted predictive models were 0.855 and 0.808 respectively.

CONCLUSIONS

Compared with PRM, SAD defined by spirometry is more closely related to airway morphology, while PRM is sensitive to early pulmonary dysfunction but may be interfered by pulmonary vessels. Models combining patient information and HRCT assessment have good predictive value for SAD.

CRITICAL RELEVANCE STATEMENT

HRCT reveals lung structural differences in small airway dysfunction defined by spirometry and parametric response mapping. This insight aids in understanding methodological differences and developing radiological tools for small airways that align with pathophysiology.

KEY POINTS

Spirometry-SAD shows thickened airway walls, narrowed lumen, and reduced branch count, which are closely related to airway morphology. PRM shows good sensitivity to early pulmonary dysfunction, although its assessment of SAD based on gas trapping may be affected by the density of pulmonary vessels and other lung structures. Combining patient information and HRCT features, the fitted model has good predictive performance for SAD defined by both spirometry and PRM (AUC values are 0.855 and 0.808, respectively).

BranchLabelNet: Anatomical Human Airway Labeling Approach using a Dividing-and-Grouping Multi-Label Classification

Anatomical airway labeling is crucial for precisely identifying airways displaying symptoms such as constriction, increased wall thickness, and modified branching patterns, facilitating the diagnosis and treatment of pulmonary ailments. This study introduces an innovative airway labeling methodology, BranchLabelNet, which accounts for the fractal nature of airways and inherent hierarchical branch nomenclature. In developing this methodology, branch-related parameters, including position vectors, generation levels, branch lengths, areas, perimeters, and more, are extracted from a dataset of 1000 chest computed tomography (CT) images. To effectively manage this intricate branch data, we employ an n-ary tree structure that captures the complicated relationships within the airway tree. Subsequently, we employ a divide-and-group deep learning approach for multi-label classification, streamlining the anatomical airway branch labeling process. Additionally, we address the challenge of class imbalance in the dataset by incorporating the Tomek Links algorithm to maintain model reliability and accuracy. Our proposed airway labeling method provides robust branch designations and achieves an impressive average classification accuracy of 95.94% across fivefold cross-validation. This approach is adaptable for addressing similar complexities in general multi-label classification problems within biomedical systems.

Advancements in automated classification of chronic obstructive pulmonary disease based on computed tomography imaging features through deep learning approaches

Chronic Obstructive Pulmonary Disease (COPD) represents a global public health issue that significantly impairs patients' quality of life and overall health. As one of the primary causes of chronic respiratory diseases and global mortality, effective diagnosis and classification of COPD are crucial for clinical management. Pulmonary function tests (PFTs) are standard for diagnosing COPD, yet their accuracy is influenced by patient compliance and other factors, and they struggle to detect early disease pathologies. Furthermore, the complexity of COPD pathological changes poses additional challenges for clinical diagnosis, increasing the difficulty for physicians in practice. Recently, deep learning (DL) technologies have demonstrated significant potential in medical image analysis, particularly for the diagnosis and classification of COPD. By analyzing key radiological features such as airway alterations, emphysema, and vascular characteristics in Computed Tomography (CT) scan images, DL enhances diagnostic accuracy and efficiency, providing more precise treatment plans for COPD patients. This article reviews the latest research advancements in DL methods based on principal radiological features of COPD for its classification and discusses the advantages, challenges, and future research directions of DL in this field, aiming to provide new perspectives for the personalized management and treatment of COPD.

Using machine learning for early detection of chronic obstructive pulmonary disease: a narrative review

Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disease and ranks third in global mortality rates, imposing a significant burden on patients and society. This review looks at recent research, both domestically and abroad, on the application of machine learning (ML) for early COPD screening. The review discusses the practical application, key optimization points, and prospects of ML techniques in early COPD screening. The aim is to establish a scientific foundation and reference framework for future research and the development of screening strategies.

Association of bronchial disease on CT imaging and clinical definitions of chronic bronchitis in a single-center COPD phenotyping study

INTRODUCTION

Chronic Bronchitis (CB) represents a phenotype of chronic obstructive pulmonary disease (COPD). While several definitions have been used for diagnosis, the relationship between clinical definitions and radiologic assessment of bronchial disease (BD) has not been well studied. The aim of this study was to evaluate the relationship between three clinical definitions of CB and radiographic findings of BD in spirometry-defined COPD patients.

METHODS

A cross-sectional analysis was performed from a COPD phenotyping study. It was a prospective observational cohort. Participants had spirometry-defined COPD and available chest CT imaging. Comparison between CB definitions, Medical Research Council (CBMRC), St. George's Respiratory Questionnaire (CBSGRQ), COPD Assessment Test (CBCAT) and CT findings were performed using Cohen's Kappa, univariate and multivariate logistic regressions.

RESULTS

Of 112 participants, 83 met inclusion criteria. Demographics included age of 70.1 ± 7.0 years old, predominantly male (59.0 %), 45.8 ± 30.8 pack-year history, 21.7 % actively smoking, and mean FEV1 61.5 ± 21.1 %. With MRC, SGRQ and CAT definitions, 22.9 %, 36.6 % and 28.0 % had CB, respectively. BD was more often present in CB compared to non-CB patients; however, it did not have a statistically significant relationship between any of the CB definitions. CBSGRQ had better agreement with radiographically assessed BD compared to the other two definitions.

CONCLUSION

Identification of BD on CT was associated with the diagnoses of CB. However, agreement between imaging and definitions were not significant, suggesting radiologic findings of BD and criteria defining CB may not identify the same COPD phenotype. Research to standardize imaging and clinical methods is needed for more objective identification of COPD phenotypes.

Lung imaging in COPD and asthma

Chronic obstructive pulmonary disease (COPD) and asthma are common lung diseases with heterogeneous clinical presentations. Lung imaging allows evaluations of underlying pathophysiological changes and provides additional personalized approaches for disease management. This narrative review provides an overview of recent advances in chest imaging analysis using various modalities, such as computed tomography (CT), dynamic chest radiography, and magnetic resonance imaging (MRI). Visual CT assessment localizes emphysema subtypes and mucus plugging in the airways. Dedicated software quantifies the severity and spatial distribution of emphysema and the airway tree structure, including the central airway wall thickness, branch count and fractal dimension of the tree, and airway-to-lung size ratio. Nonrigid registration of inspiratory and expiratory CT scans quantifies small airway dysfunction, local volume changes and shape deformations in specific regions. Lung ventilation and diaphragm movement are also evaluated on dynamic chest radiography. Functional MRI detects regional oxygen transfer across the alveolus using inhaled oxygen and ventilation defects and gas diffusion into the alveolar-capillary barrier tissue and red blood cells using inhaled hyperpolarized 129Xe gas. These methods have the potential to determine local functional properties in the lungs that cannot be detected by lung function tests in patients with COPD and asthma. Further studies are needed to apply these technologies in clinical practice, particularly for early disease detection and tailor-made interventions, such as the efficient selection of patients likely to respond to biologics. Moreover, research should focus on the extension of healthy life expectancy in patients at higher risk and with established diseases.

Artificial intelligence in COPD CT images: identification, staging, and quantitation

Chronic obstructive pulmonary disease (COPD) stands as a significant global health challenge, with its intricate pathophysiological manifestations often demanding advanced diagnostic strategies. The recent applications of artificial intelligence (AI) within the realm of medical imaging, especially in computed tomography, present a promising avenue for transformative changes in COPD diagnosis and management. This review delves deep into the capabilities and advancements of AI, particularly focusing on machine learning and deep learning, and their applications in COPD identification, staging, and imaging phenotypes. Emphasis is laid on the AI-powered insights into emphysema, airway dynamics, and vascular structures. The challenges linked with data intricacies and the integration of AI in the clinical landscape are discussed. Lastly, the review casts a forward-looking perspective, highlighting emerging innovations in AI for COPD imaging and the potential of interdisciplinary collaborations, hinting at a future where AI doesn't just support but pioneers breakthroughs in COPD care. Through this review, we aim to provide a comprehensive understanding of the current state and future potential of AI in shaping the landscape of COPD diagnosis and management.

Identification of factors directly linked to incident chronic obstructive pulmonary disease: A causal graph modeling study

BACKGROUND

Beyond exposure to cigarette smoking and aging, the factors that influence lung function decline to incident chronic obstructive pulmonary disease (COPD) remain unclear. Advancements have been made in categorizing COPD into emphysema and airway predominant disease subtypes; however, predicting which healthy individuals will progress to COPD is difficult because they can exhibit profoundly different disease trajectories despite similar initial risk factors. This study aimed to identify clinical, genetic, and radiological features that are directly linked-and subsequently predict-abnormal lung function.

METHODS AND FINDINGS

We employed graph modeling on 2,643 COPDGene participants (aged 45 to 80 years, 51.25% female, 35.1% African Americans; enrollment 11/2007-4/2011) with smoking history but normal spirometry at study enrollment to identify variables that are directly linked to future lung function abnormalities. We developed logistic regression and random forest predictive models for distinguishing individuals who maintain lung function from those who decline. Of the 131 variables analyzed, 6 were identified as informative to future lung function abnormalities, namely forced expiratory flow in the middle range (FEF25-75%), average lung wall thickness in a 10 mm radius (Pi10), severe emphysema, age, sex, and height. We investigated whether these features predict individuals leaving GOLD 0 status (normal spirometry according to Global Initiative for Obstructive Lung Disease (GOLD) criteria). Linear models, trained with these features, were quite predictive (area under receiver operator characteristic curve or AUROC = 0.75). Random forest predictors performed similarly to logistic regression (AUROC = 0.7), indicating that no significant nonlinear effects were present. The results were externally validated on 150 participants from Specialized Center for Clinically Oriented Research (SCCOR) cohort (aged 45 to 80 years, 52.7% female, 4.7% African Americans; enrollment: 7/2007-12/2012) (AUROC = 0.89). The main limitation of longitudinal studies with 5- and 10-year follow-up is the introduction of mortality bias that disproportionately affects the more severe cases. However, our study focused on spirometrically normal individuals, who have a lower mortality rate. Another limitation is the use of strict criteria to define spirometrically normal individuals, which was unavoidable when studying factors associated with changes in normalized forced expiratory volume in 1 s (FEV1%predicted) or the ratio of FEV1/FVC (forced vital capacity).

CONCLUSIONS

This study took an agnostic approach to identify which baseline measurements differentiate and predict the early stages of lung function decline in individuals with previous smoking history. Our analysis suggests that emphysema affects obstruction onset, while airway predominant pathology may play a more important role in future FEV1 (%predicted) decline without obstruction, and FEF25-75% may affect both.

Smoking-Related Interstitial Lung Disease and Emphysema

Diagnosis and treatment of patients with smoking-related lung diseases often requires multidisciplinary contributions to optimize care. Imaging plays a key role in characterizing the underlying disease, quantifying its severity, identifying potential complications, and directing management. The primary goal of this article is to provide an overview of the imaging findings and distinguishing features of smoking-related lung diseases, specifically, emphysema/chronic obstructive pulmonary disease, respiratory bronchiolitis-interstitial lung disease, smoking-related interstitial fibrosis, desquamative interstitial pneumonitis, combined pulmonary fibrosis and emphysema, pulmonary Langerhans cell histiocytosis, and E-cigarette or vaping related lung injury.

Clinical utilization of airway inflammatory biomarkers in the prediction and monitoring of clinical outcomes in patients with chronic obstructive pulmonary disease

INTRODUCTION

Chronic obstructive pulmonary disease (COPD) accounts for 545 million people living with chronic respiratory disorders and is the third leading cause of morbidity and mortality around the world. COPD is a progressive disease, characterized by episodes of acute worsening of symptoms such as cough, dyspnea, and sputum production.

AREAS COVERED

Airway inflammation is a prominent feature of COPD. Chronic airway inflammation results in airway structural remodeling and emphysema. Persistent airway inflammation is a treatable trait of COPD and plays a significant role in disease development and progression. In this review, the authors summarize the current and emerging biomarkers that reveal the heterogeneity of airway inflammation subtypes, clinical outcomes, and therapeutic response in COPD.

EXPERT OPINION

Airway inflammation can be broadly categorized as eosinophilic (type 2 inflammation) and non-eosinophilic (non-type 2 inflammation) in COPD. Currently, blood eosinophil counts are incorporated in clinical practice guidelines to identify COPD patients who are at a higher risk of exacerbations and lung function decline, and who are likely to respond to inhaled corticosteroids. As new therapeutics are being developed for the chronic management of COPD, it is essential to identify biomarkers that will predict treatment response.

Differential decline of lung function in COPD patients according to structural abnormality in chest CT

Background

Different progressions or prognoses of chronic obstructive pulmonary disease (COPD) have been reported according to structural abnormalities based on chest computed tomography (CT). This study aimed to investigate whether different structural abnormalities independently affect annual lung function changes and clinical prognosis in patients with COPD.

Methods

This longitudinal multicenter observational study was conducted using the KOCOSS cohort (NCT02800499) database in Korea from January 2012 to December 2019. For COPD patients with chest CT findings at baseline enrolment and longitudinal spirometric data, annual forced expiratory volume in 1 s (FEV1) decline rate (mL/year) and clinical outcomes were compared according to structural abnormalities, including emphysema, bronchiectasis (BE), and tuberculosis-destroyed lung (TDL). We estimated the adjusted annual FEV1 changes using a mixed-effect linear regression model.

Results

Among the enrolled 237 patients, 152 showed structural abnormalities. Emphysema, BE, and TDL were observed in 119 (78.3%), 28 (18.4%), and 27 (17.8%) patients, respectively. The annual decline in FEV1 was faster in COPD patients with structural abnormalities than those without (β = -70.6 mL/year, P-value = 0.039). BE/TDL-dominant or emphysema-dominant structural abnormality contributed to an accelerated annual FEV1 decline compared to no structural abnormality (BE/TDL-dominant, β = -103.7 mL/year, P-value = 0.043; emphysema-dominant, β = -84.1 mL/year, P-value = 0.018). Structural abnormalities made no significant differences in acute exacerbation rate and mortality.

Conclusion

The lung function decline rate in COPD differed according to structural abnormalities on CT. These findings may suggest that more focus should be placed on earlier intervention or regular follow-up with spirometry in COPD patients with BE or TDL on chest CT.

Deep Learning-Based Kernel Adaptation Enhances Quantification of Emphysema on Low-Dose Chest CT for Predicting Long-Term Mortality

OBJECTIVES

The aim of this study was to ascertain the predictive value of quantifying emphysema using low-dose computed tomography (LDCT) post deep learning-based kernel adaptation on long-term mortality.

MATERIALS AND METHODS

This retrospective study investigated LDCTs obtained from asymptomatic individuals aged 60 years or older during health checkups between February 2009 and December 2016. These LDCTs were reconstructed using a 1- or 1.25-mm slice thickness alongside high-frequency kernels. A deep learning algorithm, capable of generating CT images that resemble standard-dose and low-frequency kernel images, was applied to these LDCTs. To quantify emphysema, the lung volume percentage with an attenuation value less than or equal to -950 Hounsfield units (LAA-950) was gauged before and after kernel adaptation. Low-dose chest CTs with LAA-950 exceeding 6% were deemed emphysema-positive according to the Fleischner Society statement. Survival data were sourced from the National Registry Database at the close of 2021. The risk of nonaccidental death, excluding causes such as injury or poisoning, was explored according to the emphysema quantification results using multivariate Cox proportional hazards models.

RESULTS

The study comprised 5178 participants (mean age ± SD, 66 ± 3 years; 3110 males). The median LAA-950 (18.2% vs 2.6%) and the proportion of LDCTs with LAA-950 exceeding 6% (96.3% vs 39.3%) saw a significant decline after kernel adaptation. There was no association between emphysema quantification before kernel adaptation and the risk of nonaccidental death. Nevertheless, after kernel adaptation, higher LAA-950 (hazards ratio for 1% increase, 1.01; P = 0.045) and LAA-950 exceeding 6% (hazards ratio, 1.36; P = 0.008) emerged as independent predictors of nonaccidental death, upon adjusting for age, sex, and smoking status.

CONCLUSIONS

The application of deep learning for kernel adaptation proves instrumental in quantifying pulmonary emphysema on LDCTs, establishing itself as a potential predictive tool for long-term nonaccidental mortality in asymptomatic individuals.

Centrilobular emphysema and airway dysanapsis: factors associated with low respiratory function in younger smokers

Background

Low respiratory function in young adulthood is one of the important factors in the trajectory leading to the future development of COPD, but its morphological characteristics are not well characterised.

Methods

We retrospectively enrolled 172 subjects aged 40-49 years with ≥10 pack-years smoking history who underwent lung cancer screening by computed tomography (CT) and spirometry at two Japanese hospitals. Emphysema was visually assessed according to the Fleischner Society guidelines and classified into two types: centrilobular emphysema (CLE) and paraseptal emphysema (PSE). Airway dysanapsis was assessed with the airway/lung ratio (ALR), which was calculated by the geometric mean of the lumen diameters of the 14 branching segments divided by the cube root of total lung volume on a CT scan.

Results

Among the subjects, CLE and PSE were observed in 20.9% and 30.8%, respectively. The mean ALR was 0.04 and did not differ between those with and without each type of emphysema. Multivariable regression analysis models adjusted for age, sex, body mass index and smoking status indicated that CLE and a low ALR were independently associated with lower forced expiratory volume in 1 s (FEV1)/forced vital capacity (estimate -1.64 (95% CI -2.68- -0.60) and 6.73 (95% CI 4.24-9.24), respectively) and FEV1 % pred (estimate -2.81 (95% CI -5.10- -0.52) and 10.9 (95% CI 5.36-16.4), respectively).

Conclusions

CLE and airway dysanapsis on CT were independently associated with low respiratory function in younger smokers.

Chest MRI and CT Predictors of 10-Year All-Cause Mortality in COPD

Pulmonary imaging measurements using magnetic resonance imaging (MRI) and computed tomography (CT) have the potential to deepen our understanding of chronic obstructive pulmonary disease (COPD) by measuring airway and parenchymal pathologic information that cannot be provided by spirometry. Currently, MRI and CT measurements are not included in mortality risk predictions, diagnosis, or COPD staging. We evaluated baseline pulmonary function, MRI and CT measurements alongside imaging texture-features to predict 10-year all-cause mortality in ex-smokers with (n = 93; 31 females; 70 ± 9years) and without (n = 69; 29 females, 69 ± 9years) COPD. CT airway and vessel measurements, helium-3 (3He) MRI ventilation defect percent (VDP) and apparent diffusion coefficients (ADC) were quantified. MRI and CT texture-features were extracted using PyRadiomics (version2.2.0). Associations between 10-year all-cause mortality and all clinical and imaging measurements were evaluated using multivariable regression model odds-ratios. Machine-learning predictive models for 10-year all-cause mortality were evaluated using area-under-receiver-operator-characteristic-curve (AUC), sensitivity and specificity analyses. DLCO (%pred) (HR = 0.955, 95%CI: 0.934-0.976, p < 0.001), MRI ADC (HR = 1.843, 95%CI: 1.260-2.871, p < 0.001), and CT informational-measure-of-correlation (HR = 3.546, 95% CI: 1.660-7.573, p = 0.001) were the strongest predictors of 10-year mortality. A machine-learning model trained on clinical, imaging, and imaging textures was the best predictive model (AUC = 0.82, sensitivity = 83%, specificity = 84%) and outperformed the solely clinical model (AUC = 0.76, sensitivity = 77%, specificity = 79%). In ex-smokers, regardless of COPD status, addition of CT and MR imaging texture measurements to clinical models provided unique prognostic information of mortality risk that can allow for better clinical management.Clinical Trial Registration: www.clinicaltrials.gov NCT02279329.

Quantitative Computed Tomography Analysis of the Longitudinal Change Between Centrilobular and Paraseptal Emphysema Subtypes: A Retrospective Study

OBJECTIVE

This study aimed to investigate the difference between the extent of centrilobular emphysema (CLE) and paraseptal emphysema (PSE) on follow-up chest CT scans and their relationship to the cross-sectional area (CSA) of small pulmonary vessels.

METHODS

Sixty-two patients (36 CLE and 26 PSE) who underwent 2 chest CT scans were enrolled in this study. The percentage of low attenuation volume (%LAV) and total CSA of the small pulmonary vessels <5 mm 2 (%CSA < 5) were measured at the 2 time points. Analysis of the initial %CSA < 5 and the change in the %LAV and %CSA < 5 on follow-up imaging was performed.

RESULTS

The initial %CSA < 5 was not significantly different between the CLE and the PSE groups (CLE, 0.66 vs. PSE, 0.71; P = 0.78). There was no significant difference in the longitudinal change in the %LAV between the 2 groups (CLE, -0.048% vs. PSE, 0.005%; P = 0.26). The longitudinal change in the %CSA < 5 in patients with PSE significantly decreased compared with those with CLE (CLE, 0.025% vs. PSE, -0.018%; P = 0.02).

CONCLUSIONS

The longitudinal change in the %CSA < 5 was significantly different for patients with CLE and PSE, demonstrating an important pathophysiological difference between the subtypes.

L-Arginine, as an essential amino acid, is a potential substitute for treating COPD via regulation of ROS/NLRP3/NF-κB signaling pathway

BACKGROUNDS

Chronic obstructive pulmonary disease (COPD) is a frequent and common disease in clinical respiratory medicine and its mechanism is unclear. The purpose of this study was to find the new biomarkers of COPD and elucidate its role in the pathogenesis of COPD. Analysis of metabolites in plasma of COPD patients were performed by ultra-high performance liquid chromatography (UPLC) and quadrupole time-of-flight mass spectrometry (TOF-MS). The differential metabolites were analyzed and identified by multivariate analysis between COPD patients and healthy people. The role and mechanisms of the differential biomarkers in COPD were verified with COPD rats, arginosuccinate synthetase 1 (ASS-l) KO mice and bronchial epithelial cells (BECs). Meanwhile, whether the differential biomarkers can be the potential treatment targets for COPD was also investigated. 85 differentials metabolites were identified between COPD patients and healthy people by metabonomic.

RESULTS

L-Arginine (LA) was the most obvious differential metabolite among the 85 metabolites. Compare with healthy people, the level of LA was markedly decreased in serum of COPD patients. It was found that LA had protective effects on COPD with in vivo and in vitro experiments. Silencing Ass-1, which regulates LA metabolism, and α-methy-DL-aspartic (NHLA), an Ass-1 inhibitor, canceled the protective effect of LA on COPD. The mechanism of LA in COPD was related to the inhibition of ROS/NLRP3/NF-κB signaling pathway. It was also found that exogenous LA significantly improved COPD via regulation of ROS/NLRP3/NF-κB signaling pathway. L-Arginine (LA) as a key metabolic marker is identified in COPD patients and has a protective effect on COPD via regulation of ROS/NLRP3/NF-κB signaling pathway.

CONCLUSION

LA may be a novel target for the treatment of COPD and also a potential substitute for treating COPD.

An Update on Outcomes for COPD Pharmacological Trials: A COPD Investigators Report - Reassessment of the 2008 American Thoracic Society/European Respiratory Society Statement on Outcomes for COPD Pharmacological Trials

Background: In 2008, a dedicated American Thoracic Society/European Respiratory Society task force published a paper on the possible use and limitations of clinical outcomes and biomarkers to evaluate the impact of pharmacological therapy in patients with chronic obstructive pulmonary disease. Since then, our scientific understanding of chronic obstructive pulmonary disease has increased considerably; there has been a progressive shift from a one-size-fits-all diagnostic and therapeutic approach to a personalized approach; and many new treatments currently in development will require new endpoints to evaluate their efficacy adequately. Objectives: The emergence of several new relevant outcome measures motivated the authors to review advances in the field and highlight the need to update the content of the original report. Methods: The authors separately created search strategies for the literature, primarily based on their opinions and assessments supported by carefully chosen references. No centralized examination of the literature or uniform criteria for including or excluding evidence were used. Measurements and Main Results: Endpoints, outcomes, and biomarkers have been revisited. The limitations of some of those reported in the American Thoracic Society/European Respiratory Society task force document have been highlighted. In addition, new tools that may be useful, especially in evaluating personalized therapy, have been described. Conclusions: Because the "label-free" treatable traits approach is becoming an important step toward precision medicine, future clinical trials should focus on highly prevalent treatable traits, and this will influence the choice of outcomes and markers to be considered. The use of the new tools, particularly combination endpoints, could help better identify the right patients to be treated with the new drugs.

Stronger Associations of Centrilobular Than Paraseptal Emphysema With Longitudinal Changes in Diffusing Capacity and Mortality in COPD

BACKGROUND

The factors associated with longitudinal changes in diffusing capacity remain unclear among patients with COPD. Centrilobular emphysema (CLE) and paraseptal emphysema (PSE) are major emphysema subtypes that may have distinct clinical-physiological impacts in these patients.

RESEARCH QUESTION

Are CLE and PSE differently associated with longitudinal changes in diffusing capacity and mortality in patients with COPD?

STUDY DESIGN AND METHODS

This pooled analysis included 399 patients with COPD from two prospective observational COPD cohorts. CLE and PSE were visually assessed on CT scan according to the Fleischner Society statement. The diffusing capacity and transfer coefficient of the lung for carbon monoxide (Dlco and KCO) and FEV1 were evaluated at least annually over a 5-year period. Mortality was recorded over 10 years. Longitudinal changes in FEV1, Dlco, and KCO and mortality were compared between mild or less severe and moderate or more severe CLE and between present and absent PSE in each Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage.

RESULTS

The Dlco and KCO decline was weakly associated with FEV1 and greater in GOLD stage 3 or higher than in GOLD stages 1 and 2. Furthermore, moderate or more severe CLE, but not present PSE, was associated with steeper declines in Dlco for GOLD stages 1 and 3 or higher and KCO for all GOLD stages independent of age, sex, height, and smoking history. The moderate or more severe CLE, but not present PSE, was associated with additional FEV1 decline and higher 10-year mortality among patients with GOLD stage 3 or higher.

INTERPRETATION

A CT scan finding of moderate or more severe CLE, but not PSE, was associated with a subsequent accelerated impairment in diffusing capacity and higher long-term mortality in severe GOLD stage among patients with COPD.

Identification of frequent acute exacerbations phenotype in COPD patients based on imaging and clinical characteristics

BACKGROUND

Chronic obstructive pulmonary disease (COPD) is a common disease with high morbidity, with acute exacerbations manifesting as a worsening of respiratory symptoms. This study aimed to identify the frequent acute exacerbation phenotype in patients with COPD based on imaging and clinical characteristics.

METHODS

Patients with COPD (n = 201) were monitored for acute exacerbations one year after their initial hospital admission and further divided into frequent and non-frequent exacerbation groups according to the frequency and severity of acute exacerbations. All patients underwent high resolution CT scans and low attenuation area less than -950Hu (LAA-950) in the whole lung was measured. Differences in visual subtypes, LAA-950, and clinical basic characteristics were compared between groups. The clinical factors influencing frequent exacerbation were determined using binary logistic regression. Finally, based on imaging and clinical factors, the receiver operating characteristic curve was used to identify the phenotype of COPD with frequent acute exacerbations.

RESULTS

Patients with frequent exacerbations had a larger LAA-950 than those non-frequent exacerbations patients (p＜0.001). Frequent acute exacerbations were associated with worsening visual subtypes. Multivariate binary logistic regression illustrated that age, smoking status, BMI, FEV1 pred, and LAA-950 were associated with frequent exacerbations of COPD. The area under the receiver operating characteristic curve for predicting frequent exacerbations based on age, smoking status, BMI, FEV1 pred, and LAA-950 was 0.907 (p＜0.001).

CONCLUSION

The combination of imaging and clinical characteristics reached high diagnostic efficacy in the identification of frequent acute exacerbations in patients with COPD.

Subtyping emphysematous COPD by respiratory volume change distributions on CT

BACKGROUND

There is considerable heterogeneity among patients with emphysematous chronic obstructive pulmonary disease (COPD). We hypothesised that in addition to emphysema severity, ventilation distribution in emphysematous regions would be associated with clinical-physiological impairments in these patients.

OBJECTIVE

To evaluate whether the discordance between respiratory volume change distributions (from expiration to inspiration) in emphysematous and non-emphysematous regions affects COPD outcomes using two cohorts.

METHODS

Emphysema was quantified using a low attenuation volume percentage on inspiratory CT (iLAV%). Local respiratory volume changes were calculated using non-rigidly registered expiratory/inspiratory CT. The Ventilation Discordance Index (VDI) represented the log-transformed Wasserstein distance quantifying discordance between respiratory volume change distributions in emphysematous and non-emphysematous regions.

RESULTS

Patients with COPD in the first cohort (n=221) were classified into minimal emphysema (iLAV% <10%; n=113) and established emphysema with high VDI and low VDI groups (n=46 and 62, respectively). Forced expiratory volume in 1 s (FEV₁) was lower in the low VDI group than in the other groups, with no difference between the high VDI and minimal emphysema groups. Higher iLAV%, more severe airway disease and hyperventilated emphysematous regions in the upper-middle lobes were independently associated with lower VDI. The second cohort analyses (n=93) confirmed these findings and showed greater annual FEV₁ decline and higher mortality in the low VDI group than in the high VDI group independent of iLAV% and airway disease on CT.

CONCLUSION

Lower VDI is associated with severe airflow limitation and higher mortality independent of emphysema severity and airway morphological changes in patients with emphysematous COPD.

Culture independent markers of nontuberculous mycobacterial (NTM) lung infection and disease in the cystic fibrosis airway

Nontuberculous mycobacteria (NTM) are opportunistic pathogens that affect a relatively small but significant portion of the people with cystic fibrosis (CF), and may cause increased morbidity and mortality in this population. Cultures from the airway are the only test currently in clinical use for detecting NTM. Culture techniques used in clinical laboratories are insensitive and poorly suited for population screening or to follow progression of disease or treatment response. The lack of sensitive and quantitative markers of NTM in the airway impedes patient care and clinical trial design, and has limited our understanding of patterns of acquisition, latency and pathogenesis of disease. Culture-independent markers of NTM infection have the potential to overcome many of the limitations of standard NTM cultures, especially the very slow growth, inability to quantitate bacterial burden, and low sensitivity due to required decontamination procedures. A range of markers have been identified in sputum, saliva, breath, blood, urine, as well as radiographic studies. Proposed markers to detect presence of NTM or transition to NTM disease include bacterial cell wall products and DNA, as well as markers of host immune response such as immunoglobulins and the gene expression of circulating leukocytes. In all cases the sensitivity of culture-independent markers is greater than standard cultures; however, most do not discriminate between various NTM species. Thus, each marker may be best suited for a specific clinical application, or combined with other markers and traditional cultures to improve diagnosis and monitoring of treatment response.

Respiratory frequency-tunable dynamic imaging for lung function: New exam method using chest X-ray cine imaging considering various respiratory diseases

Objectives

A convenient way to conduct pulmonary function tests while preventing infectious diseases was proposed, together with countermeasures for severe coronavirus disease 2019 (COVID-19). The correlation between diagnosis result and diagnosis result was examined for patients with mild chronic obstructive pulmonary disease (COPD) of the most abounding as a subject of spirometry, and the possibility of using this method as an alternative to spirometry was examined.

Setting

This study was conducted in Kanagawa, Japan.

Participants

Ten normal volunteers and 15 volunteers with mild COPD participated in this study.

Outcome measures

All images were taken by EXAVISTA (Hitachi, Japan) between October 2019 and February 2020. Continuous fluoroscopic images were taken in 12.5 frames per second for 10–20 s per subject. Images that do not adopt the automatic image processing of the equipment and only carry out the signal correction of each pixel were used for the analysis.

Results

The mean total dose for all volunteers was 0.2 mGy. There was no major discrepancy in the detection of lung field geometry, and no diagnostic problems were noted by the radiologist and physician.

Conclusions

Existing X-ray cine imaging was used to extract frequency-tunable imaging. It is possible to identify abnormal regions on the images compared to spirometry, and it does not require maximum effort respiration; therefore, it is possible to perform a stable examination because the patient’s physical condition and the ability of laboratory technicians on the day are less affected. This can also be used as a countermeasure in examining patients with infectious diseases.

Trial registration

UMIN UMIN000043868.

Physiology and pathophysiology of human airway mucus

The mucus clearance system is the dominant mechanical host defense system of the human lung. Mucus is cleared from the lung by cilia and airflow, including both two-phase gas-liquid pumping and cough-dependent mechanisms, and mucus transport rates are heavily dependent on mucus concentration. Importantly, mucus transport rates are accurately predicted by the gel-on-brush model of the mucociliary apparatus from the relative osmotic moduli of the mucus and periciliary-glycocalyceal (PCL-G) layers. The fluid available to hydrate mucus is generated by transepithelial fluid transport. Feedback interactions between mucus concentrations and cilia beating, via purinergic signaling, coordinate Na+ absorptive vs Cl- secretory rates to maintain mucus hydration in health. In disease, mucus becomes hyperconcentrated (dehydrated). Multiple mechanisms derange the ion transport pathways that normally hydrate mucus in muco-obstructive lung diseases, e.g., cystic fibrosis (CF), chronic obstructive pulmonary disease (COPD), non-CF bronchiectasis (NCFB), and primary ciliary dyskinesia (PCD). A key step in muco-obstructive disease pathogenesis is the osmotic compression of the mucus layer onto the airway surface with the formation of adherent mucus plaques and plugs, particularly in distal airways. Mucus plaques create locally hypoxic conditions and produce airflow obstruction, inflammation, infection, and, ultimately, airway wall damage. Therapies to clear adherent mucus with hydrating and mucolytic agents are rational, and strategies to develop these agents are reviewed.

Emphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study

Background Visual assessment remains the standard for evaluating emphysema at CT; however, it is time consuming, is subjective, requires training, and is affected by variability that may limit sensitivity to longitudinal change. Purpose To evaluate the clinical and imaging significance of increasing emphysema severity as graded by a deep learning algorithm on sequential CT scans in cigarette smokers. Materials and Methods A secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study participants was performed and included baseline and 5-year follow-up CT scans from 2007 to 2017. Emphysema was classified automatically according to the Fleischner emphysema grading system at baseline and 5-year follow-up using a deep learning model. Baseline and change in clinical and imaging parameters at 5-year follow-up were compared in participants whose emphysema progressed versus those who did not. Kaplan-Meier analysis and multivariable Cox regression were used to assess the relationship between emphysema score progression and mortality. Results A total of 5056 participants (mean age, 60 years ± 9 [SD]; 2566 men) were evaluated. At 5-year follow-up, 1293 of the 5056 participants (26%) had emphysema progression according to the Fleischner grading system. This group demonstrated progressive airflow obstruction (forced expiratory volume in 1 second [percent predicted]: -3.4 vs -1.8), a greater decline in 6-minute walk distance (-177 m vs -124 m), and greater progression in quantitative emphysema extent (adjusted lung density: -1.4 g/L vs 0.5 g/L; percentage of lung voxels with CT attenuation less than -950 HU: 0.6 vs 0.2) than those with nonprogressive emphysema (P < .001 for each). Multivariable Cox regression analysis showed a higher mortality rate in the group with emphysema progression, with an estimated hazard ratio of 1.5 (95% CI: 1.2, 1.8; P < .001). Conclusion An increase in Fleischner emphysema grade on sequential CT scans using an automated deep learning algorithm was associated with increased functional impairment and increased risk of mortality. ClinicalTrials.gov registration no. NCT00608764 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Grenier in this issue.

Deep Learning-Based Computed Tomography Features in Evaluating Early Screening and Risk Factors for Chronic Obstructive Pulmonary Disease

This research aimed to investigate the diagnostic effect of computed tomography (CT) images based on a deep learning double residual convolution neural network (DRCNN) model on chronic obstructive pulmonary disease (COPD) and the related risk factors for COPD. The questionnaire survey was conducted among 980 permanent residents aged ≥ 40 years old. Among them, 84 patients who were diagnosed with COPD and volunteered to participate in the experiment and 25 healthy people were selected as the research subjects, and all of them underwent CT imaging scans. At the same time, an image noise reduction model based on the DRCNN was proposed to process CT images. The results showed that 84 of 980 subjects were diagnosed with COPD, and the overall prevalence of COPD in this epidemiological survey was 8.57%. Multivariate logistic regression model analysis showed that the regression coefficients of COPD with age, family history of COPD, and smoking were 0.557, 0.513, and 0.717, respectively (P < 0.05). The diagnostic sensitivity, specificity, and accuracy of DRCNN-based CT for COPD were greatly superior to those of single CT and the difference was considerable (P < 0.05). In summary, advanced age, family history of COPD, and smoking were independent risk factors for COPD. CT based on the DRCNN model can improve the diagnostic accuracy of simple CT images for COPD and has good performance in the early screening of COPD.

Quantitative Evaluation of Chronic Obstructive Pulmonary Disease and Risk Prediction of Acute Exacerbation by High-Resolution Computed Tomography

Objective

It is imperative to popularize the tertiary prevention of chronic obstructive pulmonary disease (COPD) and to improve the diagnosis and treatment.

Methods

COPD patients were divided into mild (n = 18), moderate (n = 20), severe (n = 24), and extremely severe (n = 22) groups for performing high-resolution computed tomography (HRCT) and pulmonary function test. Serum procalcitonin (PCT) and high-sensitivity C-reactive protein (hs-CRP) were detected, and the occurrence rate of acute exacerbation COPD (AECOPD) was recorded during a 12-months follow-up period.

Results

With an increase in the severity grade, the HRCT indexes, including emphysema index (EI), 1st and 15th percentile of inspiratory attenuation distribution (Perc1 and Perc15), ratio of expiratory/inspiratory mean lung density (MLD_ex/in) and lung volume (LV_ex/in), and ratio of the wall thickness to the outer diameter of the lumen (TDR), as well as percentage of the wall area to the total cross-sectional area (WA%) were increased with a decreased change in relative lung volume with attenuation values between −860 and −950 HU (RVC_{−860to −950}) and lumen area (A_i). These were correlated with the ratio of forced expiratory volume in 1 sec (FEV1) over forced vital capacity (FVC) (FEV1/FVC), the percentage of FEV1 the predicted value (FEV1%), and ratio of residual volume to total lung volume (RV/TLC). Body mass index, MLD_ex/in, FEV1%, FEV1/FVC, and PCT had a predictive value to AECOPD, with the combined AUC of 0.812.

Conclusions

HRCT imaging effectively classifies the severity of COPD, which combined with BMI, PFT, and serum PCT can predict the risk of AECOPD.

[Alpha 1-antitrypsin deficiency]

INTRODUCTION

Pulmonary emphysema and liver disease are the clinical expressions of alpha 1-antitrypsin deficiency, an autosomal recessive genetic disease.

STATE OF THE ART

Alpha 1-antitrypsin deficiency is usually associated with the homozygous Z variant of the SERPINA1 gene. Its clinical expression always consists in a substantial reduction of alpha 1-antitrypsin serum concentration and its variants are analyzed by isoelectric focalization or molecular techniques. Assessed by CO transfer alteration and CT scan, risk of pulmonary emphysema is increased by tobacco consumption. Assessed by transient elastography and liver ultrasound, risk of liver disease is increased by alcohol consumption or obesity. Treatment of COPD-associated alpha 1-antitrypsin deficiency does not differ from that of other forms of COPD. In patients presenting with severe deficiency, augmentation therapy with plasma-derived alpha 1-antitrypsin reduces the progression of emphysema, as shown in terms of CT-based lung density metrics. Patients with alpha 1-antitrypsin deficiency with a ZZ genotype should refrain from alcohol or tobacco consumption, and watch their weight; so should their close relatives.

PERSPECTIVES

Modulation of alpha 1-antitrypsin liver production offers an interesting new therapeutic perspective.

CONCLUSION

Homozygous (Z) variants of the SERPINA1 gene confer an increased risk of pulmonary emphysema and liver disease, particularly among smokers, drinkers and obese persons.

The prevalence and physiological impacts of centrilobular and paraseptal emphysema on CT in smokers with Preserved Ratio Impaired Spirometry

Centrilobular emphysema (CLE) and paraseptal emphysema (PSE) are observed in smokers with Preserved Ratio Impaired Spirometry (PRISm, defined as the ratio of forced expiratory volume in 1 s (FEV1) to forced vital capacity (FVC)≥0.7 and FEV1<80%), but their prevalence and physiological impacts remain unestablished. This multicenter study aimed to investigate its prevalence and to test whether emphysema subtypes are differently associated with physiological impairments in smokers with PRISm.

Both never and ever smokers aged at ≥40 years who underwent CT for lung cancer screening and spirometry were retrospectively and consecutively enrolled at three hospitals and a clinic. Emphysema subtypes were visually classified according to the Fleischner system. Air-trapping was assessed as the ratio of FVC to total lung capacity on CT (FVC/TLCCT).

Of 1046 never-smokers and 772 smokers with >10 pack-years, the prevalence of PRISm was 8.2% and 11.3%, respectively. The prevalence of PSE and CLE in smokers with PRISm was comparable to that in smokers with normal spirometry (PSE 43.7% versus 36.2%, p=1.00, CLE 46.0% versus 31.8%, p=0.21), but higher than that in never-smokers with PRISm (PSE, versus 1.2%, p<0.01, CLE, versus 4.7%, p<0.01) and lower than that in smokers with airflow limitation (PSE, versus 71.0%, p<0.01, CLE, versus 79.3%, p<0.01). The presence of CLE but not PSE was independently associated with reduced FVC/TLCCT in smokers with PRISm.

Both PSE and CLE were common, but only CLE was associated with air-trapping in smokers with PRISm, suggesting different physiological roles of these emphysema subtypes.

Strain Analysis in Patients at High-Risk for COPD Using Four-Dimensional Dynamic-Ventilation CT

Purpose

To quantitatively identify abnormal lung motion in chronic obstructive pulmonary disease (COPD) using strain analysis, and further clarify the potential differences of deformation in COPD with different severity of airflow limitation.

Materials and Methods

Totally, 53 patients at high-risk for COPD were enrolled in this study. All CT examinations were performed on a 320-row MDCT scanner, and strain measurement based on dynamic-ventilation CT data was performed with a computational fluid dynamics analysis software (Micro Vec V3.6.2). The strain-related parameters derived from the whole expiration phase (PSmax-all, PSmean-all, Speedmax-all ), the first 2s of expiration phase (PSmax2s, PSmean2s, Speedmax2s ) were divided respectively by the changes in lung volume to adjust for the degree of expiration. Spearman rank correlation analysis was used to evaluate associations between the strain-related parameters and various spirometric parameters. Comparisons of the strain-related parameters between COPD and non-COPD patients, between GOLD I (mild airflow restriction) and GOLD II-IV (moderate to severe airflow restriction) were made using the Mann-Whitney U-test. Receiver-operating characteristic (ROC) analysis was performed to evaluate the diagnostic performance of the strain-related parameters for COPD. P <0.05 was considered statistically significant.

Results

Strain-related parameters demonstrated positive correlations with spirometric parameters (ρ=0.275~0.687, P<0.05), suggesting that heterogeneity in lung motion was related to abnormal spirometric results. Strain-related parameters can quantitatively distinguish COPD from non-COPD patients with moderate diagnostic significance with the AUC values ranged from 0.821 to 0.894. Furthermore, parameters of the whole expiration phase (PSmax-all, Speedmax-all) demonstrated significant differences (P=0.005; P=0.04) between COPD patients with mild and moderate to severe airflow limitation.

Conclusion

Strain-related parameters derived from dynamic-ventilation CT data covering the whole lung associated with lung function changes in COPD, reflecting the severity of airflow limitation in some degree, even though its utility in severe COPD patients remains to be investigated.

CT-Based Commercial Software Applications: Improving Patient Care Through Accurate COPD Subtyping

Chronic obstructive pulmonary disease (COPD) is heterogenous in its clinical manifestations and disease progression. Patients often have disease courses that are difficult to predict with readily available data, such as lung function testing. The ability to better classify COPD into well-defined groups will allow researchers and clinicians to tailor novel therapies, monitor their effects, and improve patient-centered outcomes. Different modalities of assessing these COPD phenotypes are actively being studied, and an area of great promise includes the use of quantitative computed tomography (QCT) techniques focused on key features such as airway anatomy, lung density, and vascular morphology. Over the last few decades, companies around the world have commercialized automated CT software packages that have proven immensely useful in these endeavors. This article reviews the key features of several commercial platforms, including the technologies they are based on, the metrics they can generate, and their clinical correlations and applications. While such tools are increasingly being used in research and clinical settings, they have yet to be consistently adopted for diagnostic work-up and treatment planning, and their full potential remains to be explored.

Diagnostic efficacy of visual subtypes and low attenuation area based on HRCT in the diagnosis of COPD

Background

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease. Current gold standard criteria, pulmonary function tests (PFTs) may result in underdiagnosis of potential COPD patients. Therefore, we hypothesize that the combination of high-resolution computed tomography (HRCT) and clinical basic characteristics will enable the identification of more COPD patients.

Methods

A total of 284 patients with respiratory symptoms who were current or former smokers were included in the study, and were further divided into 5 groups of GOLD grade I–IV and non-COPD according to PFTs. All patients underwent inspiratory HRCT scanning and low attenuation area (LAA) was measured. Then they were divided into seven visual subtypes according to the Fleischner Society classification system. Non-parametric tests were used for exploring differences in basic characteristics and PFTs between different groups of enrolled patients and visual subtypes. Binary logistic regression was to find the influencing factors that affected the patients’ outcome (non-COPD vs GOLD I-IV). The area under the receiver operating characteristic curve (AUC-ROC) was to explore the diagnostic efficacy of LAA, visual subtypes, and combined basic characteristics related to COPD for COPD diagnosis. Finally, based on the cut-off values of ROC analysis, exploring HRCT features in patients who do not meet the diagnostic criteria but clinically suspected COPD.

Results

With the worsening severity of COPD, the visual subtypes gradually progressed (p < 0.01). There was a significant difference in LAA between GOLD II–IV and non-COPD (p < 0.0001). The diagnostic efficacy of LAA, visual subtypes, and LAA combined with visual subtypes for COPD were 0.742, 0.682 and 0.730 respectively. The diagnostic efficacy increased to 0.923–0.943 when basic characteristics were added (all p < 0.001). Based on the cut-off value of ROC analysis, LAA greater than 5.6, worsening of visual subtypes, combined with positive basic characteristics can help identify some potential COPD patients.

Conclusion

The heterogeneous phenotype of COPD requires a combination of multiple evaluation methods. The diagnostic efficacy of combining LAA, visual subtypes, and basic characteristics achieves good consistency with current diagnostic criteria.

Chronic obstructive pulmonary disease risk assessment tools: is one better than the others?

PURPOSE OF REVIEW

Risk assessment tools are essential in COPD care to help clinicians identify patients at higher risk of accelerated lung function decline, respiratory exacerbations, hospitalizations, and death.

RECENT FINDINGS

Conventional methods of assessing risk have focused on spirometry, patient-reported symptoms, functional status, and a combination of these tools in composite indices. More recently, qualitatively and quantitatively assessed chest imaging findings, such as emphysema, large and small airways disease, and pulmonary vascular abnormalities have been associated with poor long-term outcomes in COPD patients. Although several blood and sputum biomarkers have been investigated for risk assessment in COPD, most still warrant further validation. Finally, novel remote digital monitoring technologies may be valuable to predict exacerbations but their large-scale performance, ease of implementation, and cost effectiveness remain to be determined.

SUMMARY

Given the complex heterogeneity of COPD, any single metric is unlikely to fully capture the risk of poor long-term outcomes. Therefore, clinicians should review all available clinical data, including spirometry, symptom severity, functional status, chest imaging, and bloodwork, to guide personalized preventive care of COPD patients. The potential of machine learning tools and remote monitoring technologies to refine COPD risk assessment is promising but remains largely untapped pending further investigation.

Precision medicine in chronic obstructive pulmonary disease: how far have we come?

PURPOSE OF REVIEW

In this review, we will discuss the current status and recent developments in precision medicine in chronic obstructive pulmonary disease (COPD) through the lens of treatable traits.

RECENT FINDINGS

Although the term 'treatable traits' in the treatment of COPD is relatively recent, this concept has been used for many years if one considers interventions such as long-term oxygen therapy or alpha-1 antitrypsin replacement therapy. Recent advances have included expanding the definition of COPD to include a broader population of people with lower respiratory disease but not meeting the strict criteria for obstruction, advances in imaging to aid in the diagnosis and treatment of COPD, advances in understanding symptoms and exacerbations to define severity, using biomarkers to guide therapy and better understanding and addressing polymorbidity and frailty. In addition, there is a concerted effort to use these concepts to identify COPD patients earlier in the disease process wherein disease modification may be possible.

SUMMARY

Focusing on subsets of patients with COPD with certain characteristics should lead to better outcomes and fewer adverse effects from treatment.

VIDEO ABSTRACT

http://links.lww.com/COPM/A30.

Detection and staging of chronic obstructive pulmonary disease using a computed tomography-based weakly supervised deep learning approach

OBJECTIVES

Chronic obstructive pulmonary disease (COPD) is underdiagnosed globally. The present study aimed to develop weakly supervised deep learning (DL) models that utilize computed tomography (CT) image data for the automated detection and staging of spirometry-defined COPD.

METHODS

A large, highly heterogeneous dataset was established, consisting of 1393 participants retrospectively recruited from outpatient, inpatient, and physical examination center settings of four large public hospitals in China. All participants underwent both inspiratory chest CT scans and pulmonary function tests. CT images, spirometry data, demographic information, and clinical information of each participant were collected. An attention-based multi-instance learning (MIL) model for COPD detection was trained using CT scans from 837 participants. External validation of the COPD detection was performed with 620 low-dose CT (LDCT) scans acquired from the National Lung Screening Trial (NLST) cohort. A multi-channel 3D residual network was further developed to categorize GOLD stages among confirmed COPD patients.

RESULTS

The attention-based MIL model used for COPD detection achieved an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI: 0.903, 0.961) on the internal test set and 0.866 (95% CI: 0.805, 0.928) on the LDCT subset acquired from the NLST. The multi-channel 3D residual network was able to correctly grade 76.4% of COPD patients in the test set (423/553) using the GOLD scale.

CONCLUSIONS

The proposed chest CT-DL approach can automatically identify spirometry-defined COPD and categorize patients according to the GOLD scale. As such, this approach may be an effective case-finding tool for COPD diagnosis and staging.

KEY POINTS

• Chronic obstructive pulmonary disease is underdiagnosed globally, particularly in developing countries. • The proposed chest computed tomography (CT)-based deep learning (DL) approaches could accurately identify spirometry-defined COPD and categorize patients according to the GOLD scale. • The chest CT-DL approach may be an alternative case-finding tool for COPD identification and evaluation.

Quantitative assessment the longitudinal changes of pulmonary vascular counts in chronic obstructive pulmonary disease

BACKGROUND

Chest computed tomography (CT) is a widely used method to assess morphological and dynamic abnormalities in chronic obstructive pulmonary disease (COPD). The small pulmonary vascular cross-section (CSA), quantitatively extracted from volumetric CT, is a reliable indicator for predicting pulmonary vascular changes. CSA is associated with the severity of symptoms, pulmonary function tests (PFT) and emphysema and in COPD patients the severity increases over time. We analyzed the correlation longitudinal changes in pulmonary vascular parameters with clinical parameters in COPD patients.

MATERIALS AND METHODS

A total of 288 subjects with COPD were investigated during follow up period up to 6 years. CT images were classified into five subtypes from normal to severe emphysema according to percentage of low-attenuation areas less than -950 and -856 Hounsfield units (HU) on inspiratory and expiratory CT (LAA-950, LAA-856exp). Total number of vessels (Ntotal) and total number of vessels with area less than 5 mm2 (N<5 mm) per 1 cm2 of lung surface area (LSA) were measured at 6 mm from the pleural surface.

RESULTS

Ntotal/LSA and N<5 mm/LSA changed from 1.16 ± 0.27 to 0.87 ± 0.2 and from 1.02 ± 0.22 to 0.78 ± 0.22, respectively, during Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage progression. Both parameters changed from normal to severe emphysema according to CT subtype from 1.39 ± 0.21 to 0.74 ± 0.17 and from 1.18 ± 0.19 to 0.67 ± 0.15, respectively. LAA-950 and LAA-856exp were negatively correlated with Ntotal/LSA (r = - 0.738, - 0.529) and N<5 mm /LSA (r = - 0.729, -- .497). On the other hand, pulmonary function test (PFT) results showed a weak correlation with Ntotal/LSA and N<5 mm/LSA (r = 0.205, 0.210). The depth in CT subtypes for longitudinal change both Ntotal/LSA and N<5 mm/LSA was (- 0.032, - 0.023) and (- 0.027) in normal and SAD, respectively.

CONCLUSIONS

Quantitative computed tomography features faithfully reflected pulmonary vessel alterations, showing in particular that pulmonary vascular alteration started.

Recommendations for COPD management in Central and Eastern Europe

INTRODUCTION

The Global Initiative for Chronic Obstructive Lung Disease (GOLD) strategy report provides guidance on effective management of chronic obstructive pulmonary disease (COPD) according to local healthcare systems. However, COPD is a heterogenous disease and certain aspects, including prevalence, disease-time course and phenotype distribution, can differ between countries. Moreover, features of clinical practice and healthcare systems for COPD patients can vary widely, even in geographically close and economically similar countries.

AREAS COVERED

Based on an initial workshop of respiratory physicians from eleven countries across Central and Eastern Europe (CEE) in December 2018 and subsequent discussions, this article offers region-specific insights from clinical practice and healthcare systems in CEE. Taking GOLD 2020 recommendations into account, we suggest approaches to adapt these into national clinical guidelines for COPD management in CEE.

EXPERT OPINION

Several factors should be considered when optimizing management of COPD in CEE compared with other regions, including differences in smoking status, vaccination uptake, prevalence of tuberculosis and nontuberculous mycobacteria, and variations in healthcare systems. We provide guidance and algorithms for pharmacologic and non-pharmacologic management of COPD for the following scenarios: initial and follow-up treatment, treatment of patients with frequent exacerbations, and withdrawal of inhaled corticosteroids where appropriate.

Interstitial Lung Abnormalities, Emphysema and Spirometry in Smokers

BACKGROUND

Most pulmonary conditions reduce forced vital capacity (FVC), but studies of patients with combined pulmonary fibrosis and emphysema demonstrate that reductions in FVC are less than expected when these two conditions coexist clinically.

RESEARCH QUESTION

Do interstitial lung abnormalities (ILA), chest computed tomography (CT) imaging findings that may suggest an early stage of pulmonary fibrosis in undiagnosed individuals, affect the association between emphysema and FVC?

STUDY DESIGN AND METHODS

Measures of ILA and emphysema were available in 9579 and 5277 participants from phases 1 (2007-2011) and 2 (2012-2016) of COPDGene, respectively. ILA were defined by Fleischner Society guidelines. Adjusted linear regression models were used to assess the associations and interactions between ILA, emphysema, measures of spirometry and lung function.

RESULTS

ILA were present in 528 (6%), and 580 (11%), of participants in phases 1 and 2 of COPDGene, respectively. ILA modified the association between emphysema and FVC (P<0.0001 for interaction) in both phases. In phase 1, in those without ILA, a 5% increase in emphysema was associated with a reduction in FVC (-110 cc, 95% confidence interval [CI] -121, -100; P<0.0001) however, in those with ILA it was not (-11cc, 95% CI -53,31; P=0.59). In contrast, there was no interaction between ILA and emphysema on total lung capacity (TLC) nor on diffusing capacity of carbon monoxide (DLCO).

INTERPRETATION

The presence of ILA attenuates the reduction in FVC associated with emphysema.

Recent advances in airway imaging using micro-computed tomography and computed tomography for chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a complex lung disease characterized by a combination of airway disease and emphysema. Emphysema is classified as centrilobular emphysema (CLE), paraseptal emphysema (PSE), or panlobular emphysema (PLE), and airway disease extends from the respiratory, terminal, and preterminal bronchioles to the central segmental airways. Although clinical computed tomography (CT) cannot be used to visualize the small airways, micro-CT has shown that terminal bronchiole disease is more severe in CLE than in PSE and PLE, and micro-CT findings suggest that the loss and luminal narrowing of terminal bronchioles is an early pathological change in CLE. Furthermore, the introduction of ultra-high-resolution CT has enabled direct evaluation of the proximal small (1 to 2-mm diameter) airways, and new CT analytical methods have enabled estimation of small airway disease and prediction of future COPD onset and lung function decline in smokers with and without COPD. This review discusses the literature on micro-CT and the technical advancements in clinical CT analysis for COPD. Hopefully, novel micro-CT findings will improve our understanding of the distinct pathogeneses of the emphysema subtypes to enable exploration of new therapeutic targets, and sophisticated CT imaging methods will be integrated into clinical practice to achieve more personalized management.

Lung Features in Individuals with Biomass Smoke Exposure Characterized by CT Scan and Changes in Pulmonary Function

Background and Objective

To determine the effects of BSE (biomass smoke exposure) on pulmonary and non-pulmonary changes in patients with COPD compared with normal individuals.

Methods

Using a cohort, we recruited 16 healthy individuals with BSE (BSE normal), 19 patients with BSE+COPD, 13 healthy individuals with cigarette smoke exposure (CSE normal), 25 patients with CSE+COPD, and 25 healthy controls. Patients with GOLD stage I and II COPD were included. Baseline data (demographic data, BSE or CSE, lung function, and CT findings) and follow-up lung function data were collected. CT parameters of emphysema, pulmonary small vessels, airway remodeling, pectoralis muscles, and erector spinae muscle were measured.

Results

Individuals with BSE were mainly women (32/35, 91.43%). Compared with the CSE+COPD group, the BSE+COPD group demonstrated slower lung function decline, increased lower lung emphysema, narrower airway lumen dimensions and increased airway wall thickening in the moderate and small airways (all P<0.05). Compared with healthy controls, the CSE normal and BSE normal groups exhibited significant reductions in pulmonary small vessel area and obvious airway remodeling in small airways (P<0.05). Compared with the BSE normal group, the BSE+COPD group showed significantly more severe emphysema and airway remodeling, as well as reduced left pectoralis major muscle area (all P<0.05).

Conclusion

Healthy individuals with BSE had reduced pulmonary small vessel area and evidence of airway remodeling; patients with BSE and COPD showed more severe emphysema, airway remodeling, and reductions in pectoralis major muscle area.

Clinical Trial Registration

ChiCTR-OO-14004264.

Quantitative 18F-fluorodeoxyglucose positron emission tomography/computed tomography to assess pulmonary inflammation in COPD

Rationale

COPD and smoking are characterised by pulmonary inflammation. ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) imaging may improve knowledge of pulmonary inflammation in COPD patients and aid early development of novel therapies as an imaging biomarker.

Objectives

To evaluate pulmonary inflammation, assessed by FDG uptake, in whole and regional lung in “usual” (smoking-related) COPD patients, alpha-1 antitrypsin deficiency (α₁ATD) COPD patients, smokers without COPD and never-smokers using FDG PET/CT. Secondly, to explore cross-sectional associations between FDG PET/CT and systemic inflammatory markers in COPD patients and repeatability of the technique in COPD patients.

Methods

Data from two imaging studies were evaluated. Pulmonary FDG uptake (normalised K_i; nK_i) was measured by Patlak graphical analysis in four subject groups: 84 COPD patients, 11 α₁ATD-COPD patients, 12 smokers and 10 never-smokers. Within the COPD group, associations between nK_i and systemic markers of inflammation were assessed. Repeatability was evaluated in 32 COPD patients comparing nK_i values at baseline and at 4-month follow-up.

Results

COPD patients, α₁ATD-COPD patients and smokers had increased whole lung FDG uptake (nK_i) compared with never-smokers (0.0037±0.001, 0.0040±0.001, 0.0040±0.001 versus 0.0028±0.001 mL·cm⁻³·min⁻¹, respectively, p<0.05 for all). Similar results were observed in upper and middle lung regions. In COPD participants, plasma fibrinogen was associated with whole lung nK_i (β=0.30, p=0.02) in multivariate analysis adjusted for current smoking, forced expiratory volume in 1 s % predicted, systemic neutrophils and C-reactive protein levels. Mean percentage difference in nK_i between the baseline and follow-up was 3.2%, and the within subject coefficient of variability was 7.7%.

Conclusions

FDG PET/CT has potential as a noninvasive tool to enable whole lung and regional quantification of FDG uptake to assess smoking- and COPD-related pulmonary inflammation.

FDG PET/CT has potential utility to noninvasively evaluate pulmonary inflammation in COPD. Pulmonary FDG uptake is increased in COPD patients, positively associated with systemic inflammatory markers and shows low inter-occasion variability.https://bit.ly/3dELYAW

Computerized Tomography Image Feature under Convolutional Neural Network Algorithm Evaluated for Therapeutic Effect of Clarithromycin Combined with Salmeterol/Fluticasone on Chronic Obstructive Pulmonary Disease

This study was to explore the use of convolutional neural network (CNN) for the classification and recognition of computerized tomography (CT) images of chronic obstructive pulmonary disease (COPD) and the therapeutic effect of clarithromycin combined with salmeterol/fluticasone. First, the clinical data of COPD patients treated in hospital from September 2018 to December 2020 were collected, and CT and X-ray images were also collected. CT-CNN and X ray-CNN single modal models were constructed based on the LeNet-5 model. The randomized fusion algorithm was introduced to construct a fused CNN model for the diagnosis of COPD patients, and the recognition effect of the model was verified. Subsequently, the three-dimensional reconstruction of the patient's bronchus was performed using the classified CT images, and the changes of CT quantitative parameters in COPD patients were compared and analyzed. Finally, COPD patients were treated with salmeterol/fluticasone (COPD-C) and combined with clarithromycin (COPD-T). In addition, the differences between patients' lung function indexes, blood gas indexes, St. George respiratory questionnaire (SGRQ) scores, and the number of acute exacerbations (AECOPD) before and after treatment were evaluated. The results showed that the randomized fusion model under different iteration times and batch sizes always had the highest recognition rate, sensitivity, and specificity compared to the two single modal CNN models, but it also had longer training time. After CT images were used to quantitatively evaluate the changes of the patient's bronchus, it was found that the area of the upper and lower lung lobes of the affected side of COPD patients and the ratio of the area of the tube wall to the bronchus were significantly changed. The lung function, blood gas index, and SGRQ score of COPD-T patients were significantly improved compared with the COPD-C group (P < 0.05), but there was no considerable difference in AECOPD (P > 0.05). In summary, the randomized fusion-based CNN model can improve the recognition rate of COPD, and salmeterol/fluticasone combined with clarithromycin therapy can significantly improve the clinical treatment effect of COPD patients.

A pilot study: Quantify lung volume and emphysema extent directly from two-dimensional scout images

PURPOSE

The potential to compute volume metrics of emphysema from planar scout images was investigated in this study. The successful implementation of this concept will have a wide impact in different fields, and specifically, maximize the diagnostic potential of the planar medical images.

METHODS

We investigated our premise using a well-characterized chronic obstructive pulmonary disease (COPD) cohort. In this cohort, planar scout images from computed tomography (CT) scans were used to compute lung volume and percentage of emphysema. Lung volume and percentage of emphysema were quantified on the volumetric CT images and used as the "ground truth" for developing the models to compute the variables from the corresponding scout images. We trained two classical convolutional neural networks (CNNs), including VGG19 and InceptionV3, to compute lung volume and the percentage of emphysema from the scout images. The scout images (n = 1,446) were split into three subgroups: (1) training (n = 1,235), (2) internal validation (n = 99), and (3) independent test (n = 112) at the subject level in a ratio of 8:1:1. The mean absolute difference (MAD) and R-square (R2) were the performance metrics to evaluate the prediction performance of the developed models.

RESULTS

The lung volumes and percentages of emphysema computed from a single planar scout image were significantly linear correlated with the measures quantified using volumetric CT images (VGG19: R2 = 0.934 for lung volume and R2 = 0.751 for emphysema percentage, and InceptionV3: R2 = 0.977 for lung volume and R2 = 0.775 for emphysema percentage). The mean absolute differences (MADs) for lung volume and percentage of emphysema were 0.302 ± 0.247L and 2.89 ± 2.58%, respectively, for VGG19, and 0.366 ± 0.287L and 3.19 ± 2.14, respectively, for InceptionV3.

CONCLUSIONS

Our promising results demonstrated the feasibility of inferring volume metrics from planar images using CNNs.