Lobar Emphysema Distribution Is Associated With 5-Year Radiological Disease Progression

Novel Lobe-based Transformer model (LobTe) to predict emphysema progression in Alpha-1 Antitrypsin Deficiency

Emphysema, marked by irreversible lung tissue destruction, poses challenges in progression prediction due to its heterogeneity. Early detection is particularly critical for patients with Alpha-1 Antitrypsin Deficiency (AATD), a genetic disorder reducing ATT protein levels. Heterozygous carriers (PiMS and PiMZ) have variable AAT levels thus complicating their prognosis. This study introduces a novel prognostic model, the Lobe-based Transformer encoder (LobTe), designed to predict the annual change in lung density (ΔALD [g/L-yr]) using CT scans. Utilizing a global self-attention mechanism, LobTe specifically analyzes lobar tissue destruction to forecast disease progression. In parallel, we developed and compared a second model utilizing an LSTM architecture that implements a local subject-specific attention mechanism. Our methodology was validated on a cohort of 2,019 participants from the COPDGene study. The LobTe model demonstrated a small root mean squared error (RMSE=1.73 g/L-yr) and a notable correlation coefficient (ρ=0.61), explaining over 35% of the variability in ΔALD (R2= 0.36). Notably, it achieved a higher correlation coefficient of 0.68 for PiMZ heterozygous carriers, indicating its effectiveness in detecting early emphysema progression among smokers with mild to moderate AAT deficiency. The presented models could serve as a tool for monitoring disease progression and informing treatment strategies in carriers and subjects with AATD. Our code is available at github.com/acil-bwh/LobTe.

GOLD grade-specific characterization of COPD in the COSYCONET multi-center trial: comparison of semiquantitative MRI and quantitative CT

OBJECTIVES

We hypothesized that semiquantitative visual scoring of lung MRI is suitable for GOLD-grade specific characterization of parenchymal and airway disease in COPD and that MRI scores correlate with quantitative CT (QCT) and pulmonary function test (PFT) parameters.

METHODS

Five hundred ninety-eight subjects from the COSYCONET study (median age = 67 (60-72)) at risk for COPD or with GOLD1-4 underwent PFT, same-day paired inspiratory/expiratory CT, and structural and contrast-enhanced MRI. QCT assessed total lung volume (TLV), emphysema, and air trapping by parametric response mapping (PRMEmph, PRMfSAD) and airway disease by wall percentage (WP). MRI was analyzed using a semiquantitative visual scoring system for parenchymal defects, perfusion defects, and airway abnormalities. Descriptive statistics, Spearman correlations, and ANOVA analyses were performed.

RESULTS

TLV, PRMEmph, and MRI scores for parenchymal and perfusion defects were all higher with each GOLD grade, reflecting the extension of emphysema (all p < 0.001). Airway analysis showed the same trends with higher WP and higher MRI large airway disease scores in GOLD3 and lower WP and MRI scores in GOLD4 (p = 0.236 and p < 0.001). Regional heterogeneity was less evident on MRI, while PRMEmph and MRI perfusion defect scores were higher in the upper lobes, and WP and MRI large airway disease scores were higher in the lower lobes. MRI parenchymal and perfusion scores correlated moderately with PRMEmph (r = 0.61 and r = 0.60) and moderately with FEV1/FVC (r = -0.56).

CONCLUSION

Multi-center semiquantitative MRI assessments of parenchymal and airway disease in COPD matched GOLD grade-specific imaging features on QCT and detected regional disease heterogeneity. MRI parenchymal disease scores were correlated with QCT and lung function parameters.

KEY POINTS

Question Do MRI-based scores correlate with QCT and PFT parameters for GOLD-grade specific disease characterization of COPD? Findings MRI can visualize the parenchymal and airway disease features of COPD. Clinical relevance Lung MRI is suitable for GOLD-grade specific disease characterization of COPD and may serve as a radiation-free imaging modality in scientific and clinical settings, given careful consideration of its potential and limitations.

CT Radiomics Features Predict Change in Lung Density and Rate of Emphysema Progression

Rationale Emphysema progression is heterogeneous. Predicting temporal changes in lung density and detecting rapid progressors may facilitate selection of individuals for targeted therapies. Objective To test whether computed tomography (CT) radiomics can be used to predict changes in lung density and detect rapid progressors. Methods We extracted radiomics features from inspiratory chest CT in 4,575 subjects with and without airflow obstruction at enrollment, who completed a follow-up visit at approximately 5 years. We quantified emphysema using adjusted lung density (ALD) and estimated emphysema progression as the annualized change in ALD (∆ALD/year) between visits. We categorized participants into rapid progressors (>1% ∆ALD/year) and stable disease (≤1% ∆ALD/year). A gradient boosting model was used (1) to predict ALD at 5-years and (2) to identify rapid progressors. Four models using demographics (base clinical model); CT density; radiomics; and combined features (clinical, radiomics, and CT density) were evaluated and tested. Results There were 1,773 (38.8%) rapid progressors. For predicting ALD at 5-years in the 20% held-out data, the base model explained 31% of the variance (adjusted R2 = 0.31) whereas R2 was 0.74 for the CT density model, 0.66 for the radiomics-only model, and 0.77 for the combined features model. For detecting rapid progressors, the base model (AUC = 0.57, 95%CI 0.53-0.61) was outperformed by the radiomics-only model (AUC = 0.73, 95%CI 0.69-0.76, ∆ =0.0003, p < 0.001) and the combined model (AUC = 0.74, 95%CI 0.71-0.77, ∆ = 0.0003, p < 0.001). Conclusions Parenchymal and airway radiomics features derived from inspiratory scans can be used to predict temporal changes in lung density and help identify rapid progressors.

CT Chest Imaging Using Normalized Join-Count: Predicting Emphysema Progression in the CanCOLD Study

Background Pre-existing emphysema is recognized as an indicator of future worsening in patients with chronic obstructive pulmonary disease (COPD) when observed through CT imaging. However, it remains uncertain whether additional factors, such as the spatial compactness of CT emphysema, might also serve as predictors of disease progression. Purpose To evaluate the relationship between the compactness of CT emphysema voxels and emphysema progression. Materials and Methods This secondary analysis uses data from the prospective Canadian Cohort Obstructive Lung Disease (CanCOLD) study, examining CT images obtained in participants with and without COPD at baseline and a 3-year follow-up time point (November 2009 to November 2018). Measurements of forced expiratory volume in first second of expiration (FEV1) and diffusing capacity of lung for carbon monoxide (DLco) were collected. The normalized join-count (NJC) measurement from baseline CT images and lung density (LD) changes were analyzed. Emphysema progression was defined as an annualized LD change of less than half an SD below the mean of the participants without COPD with no smoking history. Multivariable linear and logistic regression models were used to assess the association between baseline CT NJC measurements and the annualized change in LD, FEV1, DLco, and emphysema progression versus nonprogression. Results A total of 524 participants (mean age, 66 years ± 10 [SD]; 293 male) (FEV1 percent predicted, 88% ± 19; FEV1/FVC, 67% ± 9; DLco percent predicted, 105% ± 25) were analyzed, 187 (36%) of whom had COPD. CT NJC was associated with the annualized change in LD (P < .001), FEV1 (P = .02), and DLco (P = .01). Additionally, CT NJC predicted emphysema progression versus nonprogression (odds ratio, 2.24; 95% CI: 1.37, 3.50; P < .001). Conclusion The spatial distribution, or "compactness," of CT emphysema voxels predicted emphysema progression in individuals with and without COPD. ClinicalTrials.gov Identifier: NCT00920348 © RSNA, 2024 Supplemental material is available for this article.

Computed tomography (CT) quantitative assessment of single lobe emphysema correlates with chronic obstructive pulmonary disease (COPD) severity: a cross-sectional study with retrospective data collection

Background

In the past, many researchers have studied the correlation between quantitative parameters of computed tomography (CT) and parameters of pulmonary function test (PFT) in patients with chronic obstructive pulmonary disease (COPD) with good results. Most of these studies have focused on the whole-lung level. In this study, we analyzed the biphasic CT lung volume parameters and the percentage of emphysema volume in different lobes of the lungs of patients with different grades of COPD and assessed their relationship with different lung function indices.

Methods

We retrospectively collected patients who underwent PFTs at The First Affiliated Hospital of Guangzhou Medical University from 1 July 2019 to 27 January 2020, and underwent chest respiratory dual-phase CT scans within 1 week, including 112 non-COPD patients and 297 COPD patients. We quantified the biphasic CT lung volume parameters and the percentage of emphysema volume in different lobes using a pulmonary image analysis tool. One-way analysis of variance (ANOVA) and Kruskal-Wallis H method were used to compare the quantitative CT parameters of each lung lobe in different groups. The correlation between quantitative CT parameters of different lung lobes and lung function indices was assessed using multiple linear regression.

Results

Among the 3 biphasic CT lung volume parameters, only volume change/inspiratory lung volume (∆LV/LVin) in the non-COPD control, mildly to moderately severe, and severe to extremely severe groups had statistical differences in each lobe level (all P<0.05). Correlation was significant between LVin and different lung function indices and between low attenuation areas percent below the threshold of -950 in the inspiratory phase [low attenuation area below -950 in the inspiratory phase (%LAA-950in)] and lung function indices in the left lower lobe (all P<0.05). There was statistically significant correlation between expiratory lung volume and ∆LV/LVin and lung function indices in the right lower lung (all P≤0.001). In the remaining lobes, LVin, expiratory lung volume, ∆LV/LVin, and %LAA-950in correlated with only some of the lung function indices.

Conclusions

The percentage of emphysema volume did not differ between lobes in the non-COPD control and severe to extremely severe COPD populations. LVin and %LAA-950in in the left upper lobe, expiratory lung volume and ∆LV/LVin in the right lower lobe were more reflective of the changes in lung function indices of the patients, whereas the correlation of the 3 biphasic CT lung volume parameters and the percentage of emphysema volume in the upper lobes of both lungs and the right middle lung with lung function indices was unclear.

Deep Learning Integration of Chest Computed Tomography Imaging and Gene Expression Identifies Novel Aspects of COPD

Rationale

Chronic obstructive pulmonary disease (COPD) is characterized by pathologic changes in the airways, lung parenchyma, and persistent inflammation, but the links between lung structural changes and blood transcriptome patterns have not been fully described.

Objections

The objective of this study was to identify novel relationships between lung structural changes measured by chest computed tomography (CT) and blood transcriptome patterns measured by blood RNA sequencing (RNA-seq).

Methods

CT scan images and blood RNA-seq gene expression from 1223 participants in the COPD Genetic Epidemiology (COPDGene®) study were jointly analyzed using deep learning to identify shared aspects of inflammation and lung structural changes that we labeled image-expression axes (IEAs). We related IEAs to COPD-related measurements and prospective health outcomes through regression and Cox proportional hazards models and tested them for biological pathway enrichment.

Results

We identified 2 distinct IEAs: IEAemph which captures an emphysema-predominant process with a strong positive correlation to CT emphysema and a negative correlation to forced expiratory volume in 1 second and body mass index (BMI); and IEAairway which captures an airway-predominant process with a positive correlation to BMI and airway wall thickness and a negative correlation to emphysema. Pathway enrichment analysis identified 29 and 13 pathways significantly associated with IEAemph and IEAairway, respectively (adjusted p<0.001).

Conclusions

Integration of CT scans and blood RNA-seq data identified 2 IEAs that capture distinct inflammatory processes associated with emphysema and airway-predominant COPD.

Heterogeneity and Progression of Chronic Obstructive Pulmonary Disease: Emphysema-Predominant and Non-Emphysema-Predominant Disease

While variation in emphysema severity between patients with chronic obstructive pulmonary disease (COPD) is well-recognized, clinically applicable definitions of the emphysema-predominant disease (EPD) and non-emphysema-predominant disease (NEPD) subtypes have not been established. To study the clinical relevance of the EPD and NEPD subtypes, we tested the association of these subtypes with prospective decline in forced expiratory volume in 1 second (FEV1) and mortality among 3,427 subjects with Global Initiative for Chronic Obstructive Lung Disease (GOLD) spirometric grade 2-4 COPD at baseline in the Genetic Epidemiology of COPD (COPDGene) Study, an ongoing national multicenter study that started in 2007. NEPD was defined as airflow obstruction with less than 5% computed tomography (CT) quantitative densitometric emphysema at -950 Hounsfield units, and EPD was defined as airflow obstruction with 10% or greater CT emphysema. Mixed-effects models for FEV1 demonstrated larger average annual FEV1 loss in EPD subjects than in NEPD subjects (-10.2 mL/year; P < 0.001), and subtype-specific associations with FEV1 decline were identified. Cox proportional hazards models showed higher risk of mortality among EPD patients versus NEPD patients (hazard ratio = 1.46, 95% confidence interval: 1.34, 1.60; P < 0.001). To determine whether the NEPD/EPD dichotomy is captured by previously described COPDGene subtypes, we used logistic regression and receiver operating characteristic (ROC) curve analysis to predict NEPD/EPD membership using these previous subtype definitions. The analysis generally showed excellent discrimination, with areas under the ROC curve greater than 0.9. The NEPD and EPD COPD subtypes capture important aspects of COPD heterogeneity and are associated with different rates of disease progression and mortality.

Quantifying the spatial clustering characteristics of radiographic emphysema explains variability in pulmonary function

Quantitative assessment of emphysema in CT scans has mostly focused on calculating the percentage of lung tissue that is deemed abnormal based on a density thresholding strategy. However, this overall measure of disease burden discards virtually all the spatial information encoded in the scan that is implicitly utilized in a visual assessment. This simplification is likely grouping heterogenous disease patterns and is potentially obscuring clinical phenotypes and variable disease outcomes. To overcome this, several methods that attempt to quantify heterogeneity in emphysema distribution have been proposed. Here, we compare three of those: one based on estimating a power law for the size distribution of contiguous emphysema clusters, a second that looks at the number of emphysema-to-emphysema voxel adjacencies, and a third that applies a parametric spatial point process model to the emphysema voxel locations. This was done using data from 587 individuals from Phase 1 of COPDGene that had an inspiratory CT scan and plasma protein abundance measurements. The associations between these imaging metrics and visual assessment with clinical measures (FEV[Formula: see text], FEV[Formula: see text]-FVC ratio, etc.) and plasma protein biomarker levels were evaluated using a variety of regression models. Our results showed that a selection of spatial measures had the ability to discern heterogeneous patterns among CTs that had similar emphysema burdens. The most informative quantitative measure, average cluster size from the point process model, showed much stronger associations with nearly every clinical outcome examined than existing CT-derived emphysema metrics and visual assessment. Moreover, approximately 75% more plasma biomarkers were found to be associated with an emphysema heterogeneity phenotype when accounting for spatial clustering measures than when they were excluded.

GOLD stage-specific phenotyping of emphysema and airway disease using quantitative computed tomography

Background

In chronic obstructive pulmonary disease (COPD) abnormal lung function is related to emphysema and airway obstruction, but their relative contribution in each GOLD-stage is not fully understood. In this study, we used quantitative computed tomography (QCT) parameters for phenotyping of emphysema and airway abnormalities, and to investigate the relative contribution of QCT emphysema and airway parameters to airflow limitation specifically in each GOLD stage.

Methods

Non-contrast computed tomography (CT) of 492 patients with COPD former GOLD 0 COPD and COPD stages GOLD 1-4 were evaluated using fully automated software for quantitative CT. Total lung volume (TLV), emphysema index (EI), mean lung density (MLD), and airway wall thickness (WT), total diameter (TD), lumen area (LA), and wall percentage (WP) were calculated for the entire lung, as well as for all lung lobes separately. Results from the 3rd-8th airway generation were aggregated (WT3-8, TD3-8, LA3-8, WP3-8). All subjects underwent whole-body plethysmography (FEV1%pred, VC, RV, TLC).

Results

EI was higher with increasing GOLD stages with 1.0 ± 1.8% in GOLD 0, 4.5 ± 9.9% in GOLD 1, 19.4 ± 15.8% in GOLD 2, 32.7 ± 13.4% in GOLD 3 and 41.4 ± 10.0% in GOLD 4 subjects (p < 0.001). WP3-8 showed no essential differences between GOLD 0 and GOLD 1, tended to be higher in GOLD 2 with 52.4 ± 7.2%, and was lower in GOLD 4 with 50.6 ± 5.9% (p = 0.010 - p = 0.960). In the upper lobes WP3-8 showed no significant differences between the GOLD stages (p = 0.824), while in the lower lobes the lowest WP3-8 was found in GOLD 0/1 with 49.9 ± 6.5%, while higher values were detected in GOLD 2 with 51.9 ± 6.4% and in GOLD 3/4 with 51.0 ± 6.0% (p < 0.05). In a multilinear regression analysis, the dependent variable FEV1%pred can be predicted by a combination of both the independent variables EI (p < 0.001) and WP3-8 (p < 0.001).

Conclusion

QCT parameters showed a significant increase of emphysema from GOLD 0-4 COPD. Airway changes showed a different spatial pattern with higher values of relative wall thickness in the lower lobes until GOLD 2 and subsequent lower values in GOLD3/4, whereas there were no significant differences in the upper lobes. Both, EI and WP5-8 are independently correlated with lung function decline.

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.

Basilar Predominant Emphysema: Thinking beyond Alpha-1-Antitrypsin Deficiency

Basilar predominant emphysema, or disproportionate emphysematous involvement of the lung bases compared to the apices, is an uncommon radiographic pattern of emphysema traditionally associated with alpha-1-antitrypsin deficiency (AATD). We present a case of a 59-year-old female with 41 pack-year tobacco use, Stage IV COPD with supplemental oxygen, and bibasilar predominant emphysema who successfully underwent bronchoscopic lung volume reduction. She presented with recurrent hospitalizations for frequent exacerbations. After lung reduction, the patient displayed improvement in functional status without hospitalizations at the 15-month follow-up. Careful history taking is essential for any patients diagnosed with lower lobe emphysema to elucidate the underlying etiology. This case challenges the notion that basilar emphysema is sensitive or specific for AATD and emphasizes that this pattern of emphysema has a broad differential diagnosis and alternative etiologies should be considered. Our patient was ultimately diagnosed with smoking-related emphysema, with atypical bibasilar involvement. Furthermore, basilar predominant emphysema should be considered a separate entity from its apical predominant counterpart.

Unsupervised Learning Identifies Computed Tomographic Measurements as Primary Drivers of Progression, Exacerbation, and Mortality in Chronic Obstructive Pulmonary Disease

Rationale: Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome with phenotypic manifestations that tend to be distributed along a continuum. Unsupervised machine learning based on broad selection of imaging and clinical phenotypes may be used to identify primary variables that define disease axes and stratify patients with COPD. Objectives: To identify primary variables driving COPD heterogeneity using principal component analysis and to define disease axes and assess the prognostic value of these axes across three outcomes: progression, exacerbation, and mortality. Methods: We included 7,331 patients between 39 and 85 years old, of whom 40.3% were Black and 45.8% were female smokers with a mean of 44.6 pack-years, from the COPDGene (Genetic Epidemiology of COPD) phase I cohort (2008-2011) in our analysis. Out of a total of 916 phenotypes, 147 continuous clinical, spirometric, and computed tomography (CT) features were selected. For each principal component (PC), we computed a PC score based on feature weights. We used PC score distributions to define disease axes along which we divided the patients into quartiles. To assess the prognostic value of these axes, we applied logistic regression analyses to estimate 5-year (n = 4,159) and 10-year (n = 1,487) odds of progression. Cox regression and Kaplan-Meier analyses were performed to estimate 5-year and 10-year risk of exacerbation (n = 6,532) and all-cause mortality (n = 7,331). Results: The first PC, accounting for 43.7% of variance, was defined by CT measures of air trapping and emphysema. The second PC, accounting for 13.7% of variance, was defined by spirometric and CT measures of vital capacity and lung volume. The third PC, accounting for 7.9% of the variance, was defined by CT measures of lung mass, airway thickening, and body habitus. Stratification of patients across each disease axis revealed up to 3.2-fold (95% confidence interval [CI] 2.4, 4.3) greater odds of 5-year progression, 5.4-fold (95% CI 4.6, 6.3) greater risk of 5-year exacerbation, and 5.0-fold (95% CI 4.2, 6.0) greater risk of 10-year mortality between the highest and lowest quartiles. Conclusions: Unsupervised learning analysis of the COPDGene cohort reveals that CT measurements may bolster patient stratification along the continuum of COPD phenotypes. Each of the disease axes also individually demonstrate prognostic potential, predictive of future forced expiratory volume in 1 second decline, exacerbation, and mortality.

Clustering-based COPD subtypes have distinct longitudinal outcomes and multi-omics biomarkers

Introduction

Chronic obstructive pulmonary disease (COPD) can progress across several domains, complicating the identification of the determinants of disease progression. In our previous work, we applied k-means clustering to spirometric and chest radiological measures to identify four COPD-related subtypes: ‘relatively resistant smokers (RRS)’, ‘mild upper lobe-predominant emphysema (ULE)’, ‘airway-predominant disease (AD)’ and ‘severe emphysema (SE)’. In the current study, we examined the associations of these subtypes to longitudinal COPD-related health measures as well as blood transcriptomic and plasma proteomic biomarkers.

Methods

We included 8266 non-Hispanic white and African-American smokers from the COPDGene study. We used linear regression to investigate cluster associations to 5-year prospective changes in spirometric and radiological measures and to gene expression and protein levels. We used Cox-proportional hazard test to test for cluster associations to prospective exacerbations, comorbidities and mortality.

Results

The RRS, ULE, AD and SE clusters represented 39%, 15%, 26% and 20% of the studied cohort at baseline, respectively. The SE cluster had the greatest 5-year FEV₁ (forced expiratory volume in 1 s) and emphysema progression, and the highest risks of exacerbations, cardiovascular disease and mortality. The AD cluster had the highest diabetes risk. After adjustments, only the SE cluster had an elevated respiratory mortality risk, while the ULE, AD and SE clusters had elevated all-cause mortality risks. These clusters also demonstrated differential protein and gene expression biomarker associations, mostly related to inflammatory and immune processes.

Conclusion

COPD k-means subtypes demonstrate varying rates of disease progression, prospective comorbidities, mortality and associations to transcriptomic and proteomic biomarkers. These findings emphasise the clinical and biological relevance of these subtypes, which call for more study for translation into clinical practice.

Trail registration number

NCT00608764.

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.

Circulating Complement C1q as a Novel Biomarker is Associated with the Occurrence and Development of COPD

Purpose

Increasing evidence has shown that the immune response interacts with the chronic inflammatory response and gives rise to the occurrence and development of COPD. Complement component 1q (C1q), as a subcomponent of the C1 complex, could be involved in innate and adaptive immunity. Our study aimed to investigate the relationship between C1q and the clinical characteristics of COPD subjects.

Patients and Methods

Serum C1q levels were measured in 203 COPD subjects and 191 non-COPD controls. Correlations between C1q and the characteristics of COPD were analyzed using Spearman’s rho. Receiver operating curve (ROC) analysis was used to evaluate the threshold value in differentiating disease status. All 203 COPD subjects were followed up for 1 year for future acute exacerbations.

Results

There were significant reductions in serum C1q levels in COPD subjects compared to non-COPD controls. Moreover, serum C1q levels were obviously positively correlated with the FEV1/FVC ratio and predicted FEV1% but had a weakly negative correlation with the %LAA-950 and the percentage of neutrophils in peripheral blood. Using a cutoff value of 137.150 mg/l as the boundary in ROC analysis, the sensitivity and specificity were 65.9% and 76.0%, respectively. The 1-year follow-up results showed that C1q levels less than 137.150 mg/l were negatively related to the time to the next severe exacerbation and the time to death.

Conclusion

Circulating C1q levels may be a novel biomarker not only related to the pulmonary function of COPD but also having great potential to predict the risk of COPD deterioration in the future. However, further prospective trials are needed to clarify the influences of C1q on the pathogenesis of COPD.

Dynamic Quantitative Magnetic Resonance Imaging Assessment of Areas of the Lung During Free-Breathing of Patients with Chronic Obstructive Pulmonary Disease

RATIONALE AND OBJECTIVES

Changes in the geometry of the chest wall due to lung hyperinflation occur in COPD. However, the quantitative assessment of impaired lung motions and its association with the clinical characteristics of COPD patients are unclear. This study aimed to investigate the respiratory kinetics of COPD patients by dynamic MRI.

MATERIALS AND METHODS

This study enrolled 22 COPD patients and 10 normal participants who underwent dynamic MRI and pulmonary function testing (PFT). Changes in the areas of the lung and mediastinum during respiration were compared between the COPD patients and the normal controls. Relationships between MRI, CT parameters, and clinical measures that included PFT results also were evaluated.

RESULTS

Asynchronous movements and decreased diaphragmatic motion were found in COPD patients. COPD patients had a larger ratio of MRI-measured lung areas at expiration to inspiration, a smaller magnitude of the peak area change ratio, and a smaller mediastinal-thoracic area ratio than the normal participants. The lung area ratio was associated with FEV₁/FVC, predicted RV%, and CT lung volume/predicted total lung capacity (pTLC). The lung area ratio of the right lower and left lower lungs was significantly correlated with emphysema of each lower lobe. The expiratory mediastinal-thoracic area ratio was associated with FEV₁% predicted and RV/TLC.

CONCLUSION

Changes in the lung areas of COPD patients as shown on MRI reflected the severity of airflow limitation, hyperinflation, and the extent of emphysema. Dynamic MRI provides essential information about respiratory kinetics in COPD.

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 Emphysema on Low-Dose CT Imaging of the Chest and Risk of Lung Cancer and Airflow Obstruction: An Analysis of the National Lung Screening Trial

BACKGROUND

Lung cancer risk prediction models do not routinely incorporate imaging metrics available on low-dose CT (LDCT) imaging of the chest ordered for lung cancer screening.

RESEARCH QUESTION

What is the association between quantitative emphysema measured on LDCT imaging and lung cancer incidence and mortality, all-cause mortality, and airflow obstruction in individuals who currently or formerly smoked and are undergoing lung cancer screening?

STUDY DESIGN AND METHODS

In 7,262 participants in the CT arm of the National Lung Screening Trial, percent low attenuation area (%LAA) was defined as the percentage of lung volume with voxels less than -950 Hounsfield units on the baseline examination. Multivariable Cox proportional hazards models, adjusting for competing risks where appropriate, were built to test for association between %LAA and lung cancer incidence, lung cancer mortality, and all-cause mortality with censoring at 6 years. In addition, multivariable logistic regression models were built to test the cross-sectional association between %LAA and airflow obstruction on spirometry, which was available in 2,700 participants.

RESULTS

The median %LAA was 0.8% (interquartile range, 0.2%-2.7%). Every 1% increase in %LAA was independently associated with higher hazards of lung cancer incidence (hazard ratio [HR], 1.02; 95% CI, 1.01-1.03; P = .004), lung cancer mortality (HR, 1.02; 95% CI, 1.00-1.05; P = .045), and all-cause mortality (HR, 1.01; 95% CI, 1.00-1.03; P = .042). Among participants with spirometry, 892 had airflow obstruction. The likelihood of airflow obstruction increased with every 1% increase in %LAA (odds ratio, 1.07; 95% CI, 1.06-1.09; P < .001). A %LAA cutoff of 1% had the best discriminative accuracy for airflow obstruction in participants aged > 65 years.

INTERPRETATION

Quantitative emphysema measured on LDCT imaging of the chest can be leveraged to improve lung cancer risk prediction and help diagnose COPD in individuals who currently or formerly smoked and are undergoing lung cancer screening.

Fleischner Society Visual Emphysema CT Patterns Help Predict Progression of Emphysema in Current and Former Smokers: Results from the COPDGene Study

Background The correlation between visual emphysema patterns and subsequent progression of disease may provide a way to enrich a study population for treatment trials of emphysema. Purpose To evaluate the potential relationship between emphysema visual subtypes and progression of emphysema and gas trapping. Materials and Methods Current and former smokers with and without chronic obstructive pulmonary disease (COPD) enrolled in the prospective Genetic Epidemiology of COPD (COPDGene) study (ClinicalTrials.gov identifier: NCT02445183) between 2008 and 2011 had their Fleischner Society visual CT scores assessed at baseline, quantitative inspiratory, and expiratory CT and at 5 years. They also underwent pulmonary function testing at baseline CT and at 5 years. The dependent variables were inspiratory lung density at 15th percentile (adjusted for lung volume) as a measure of emphysema and percentage of lung volume with attenuation less than -856 HU at expiratory CT as a measure of air trapping. Statistical analysis used a linear mixed model, adjusted for age, height, sex, race, smoking status, and scanner make. Results A total of 4166 participants (mean age, 60 years ± 9 [standard deviation]; 2091 [50%] men) were evaluated. In participants with COPD (1655 participants, 40%), those with visual presence of mild, moderate, and confluent emphysema at baseline CT showed a mean decline in lung density of 4.6 g/L ± 1.1 (P < .001), 6.7 g/L ± 1.1 (P < .001), and 6.4 g/L ± 1.2 (P < .001), respectively, compared with 2.4 g/L ± 1.3 (P < .001) for those with trace emphysema. For participants without COPD, those with visual presence of mild and moderate emphysema at baseline CT showed a mean decline in lung density of 3.6 g/L ± 1.0 (P < .001) and 3.1 g/L ± 1.6 (P < .001), respectively, compared with 1.8 g/L ± 1.0 (P < .001) for those with trace emphysema. Conclusion The pattern of parenchymal emphysema at baseline CT was an independent predictor of subsequent progression of emphysema in participants who are current or former cigarette smokers with and without chronic obstructive pulmonary disease. © RSNA, 2020 Online supplemental material is available for this article.

Advances in Chronic Obstructive Pulmonary Disease Imaging

Chest computed tomography (CT) imaging is a useful tool that provides in vivo information regarding lung structure. Imaging has contributed to a better understanding of COPD, allowing for the detection of early structural changes and the quantification of extra-pulmonary structures. Novel CT imaging techniques have provided insight into the progression of the main COPD subtypes, such as emphysema and small airway disease. This article serves as a review of new information relevant to COPD imaging. CT abnormalities, such as emphysema and loss of airways, are present even in smokers who do not meet the criteria for COPD and in those with mild-to-moderate disease. Subjects with mild-to-moderate COPD, with the highest loss of airways, also experience the highest decline in lung function. Extra-pulmonary manifestations of COPD, such as right ventricle enlargement and low muscle mass measured on CT, are associated with increased risk for all-cause mortality. CT longitudinal data has also given insight into the progression of COPD. Mechanically affected areas of lung parenchyma adjacent to emphysematous areas are associated with a greater decline in FEV₁. Subjects with the greatest percentage of small airway disease, as measured on matched inspiratory-expiratory CT scan, also present with the greatest decline in lung function.

Correlation of positron emission tomography ventilation-perfusion matching with CT densitometry in severe emphysema

Background

Emphysema severity is frequently measured on CT via densitometry. Correlation with scintigraphic and spirometric functional measures of ventilation or perfusion varies widely, and no prior study has evaluated correlation between densitometry and lobar ventilation/perfusion in patients with severe emphysema. The aim of this study was to evaluate the utility and findings of gallium-68 (⁶⁸Ga) ventilation/perfusion positron emission tomography-CT (⁶⁸Ga-VQ/PET-CT) in severe emphysema assessment.

Methods

Fourteen consecutive patients undergoing evaluation for bronchoscopic lung volume reduction between March 2015 and March 2018 underwent ⁶⁸Ga-VQ/PET-CT assessment for lobar functional lung mapping, in addition to CT densitometry. Correlations between CT densitometry and ⁶⁸Ga-VQ/PET-CT parameters for individual lobar lung function were sought.

Results

CT densitometry assessment of emphysema correlated only weakly (R² = 0.13) with lobar perfusion and was not correlated with ventilation (R² = 0.04). Densitometry was moderately (R² = 0.67) correlated with V/Q units in upper lobes, though poorly reflected physiological function in lower lobes (R² = 0.19). Emphysema severity, as measured by CT densitometry, was moderately correlated with proportion of normal V/Q units and matched V/Q defects in individual lobes.

Conclusions

Assessment of lobar pulmonary function by ⁶⁸Ga-VQ/PET-CT provides physiologic information not evident on CT densitometry such as ventilation and perfusion specifics and matched defects. Further research is needed to see if the discordant findings on ⁶⁸Ga-VQ/PET-CT provide prognostic information or can be used to modify patient management and improve outcomes.

Artificial Intelligence in COPD: New Venues to Study a Complex Disease

Chronic obstructive pulmonary disease (COPD) is a complex and heterogeneous disease that can benefit from novel approaches to understanding its evolution and divergent trajectories. Artificial intelligence (AI) has revolutionized how we can use clinical, imaging, and molecular data to understand and model complex systems. AI has shown impressive results in areas related to automated clinical decision making, radiological interpretation and prognostication. The unique nature of COPD and the accessibility to well-phenotyped populations result in an ideal scenario for AI development. This review provides an introduction to AI and deep learning and presents some recent successes in applying AI in COPD. Finally, we will discuss some of the opportunities, challenges, and limitations for AI applications in the context of COPD.

Lobar distribution of non-emphysematous gas trapping and lung hyperinflation in chronic obstructive pulmonary disease

BACKGROUND

Lung hyperinflation in chronic obstructive pulmonary disease (COPD) is closely associated with emphysema and non-emphysematous gas trapping, termed functional small airway disease (fSAD), on inspiratory and expiratory computed tomography (CT). Because the cranial-caudal emphysema distribution affects pulmonary function and fSAD may precede emphysema on CT, we tested the hypothesis that lobar fSAD distribution would affect lung hyperinflation differently in COPD with minimal and established emphysema.

METHODS

The volume percentages of fSAD and emphysema (fSAD% and Emph%) over the upper and lower lobes were measured using inspiratory and expiratory CT in 70 subjects with COPD. Subjects were divided into those with minimal and established emphysema (n = 36 and 34) using a threshold of 10% Emph% in the whole lung.

RESULTS

In the minimal emphysema group, fSAD% in the upper and lower lobes was positively correlated with functional residual capacity (FRC) and residual volume to total lung capacity ratio (RV/TLC), and the correlation of fSAD% with RV/TLC was greater in the lower lobes. Conversely, in the established emphysema group, fSAD% in the upper and lower lobes was correlated with RV/TLC, but not with FRC. In multivariate analysis, fSAD% in the lower lobes, but not in the upper lobes, was associated with RV/TLC in subjects with minimal emphysema after adjusting for age, smoking status, and bronchodilator use.

CONCLUSION

Non-emphysematous gas trapping in the upper and lower lobes has a distinct physiological effect, especially in COPD with minimal emphysema. This local evaluation might allow sensitive detection of changes in lung hyperinflation in COPD.

Subtyping COPD by Using Visual and Quantitative CT Imaging Features

BACKGROUND

Multiple studies have identified COPD subtypes by using visual or quantitative evaluation of CT images. However, there has been no systematic assessment of a combined visual and quantitative CT imaging classification. We integrated visually defined patterns of emphysema with quantitative imaging features and spirometry data to produce a set of 10 nonoverlapping CT imaging subtypes, and we assessed differences between subtypes in demographic features, physiological characteristics, longitudinal disease progression, and mortality.

METHODS

We evaluated 9,080 current and former smokers in the COPDGene study who had available volumetric inspiratory and expiratory CT images obtained using a standardized imaging protocol. We defined 10 discrete, nonoverlapping CT imaging subtypes: no CT imaging abnormality, paraseptal emphysema (PSE), bronchial disease, small airway disease, mild emphysema, upper lobe predominant centrilobular emphysema (CLE), lower lobe predominant CLE, diffuse CLE, visual without quantitative emphysema, and quantitative without visual emphysema. Baseline and 5-year longitudinal characteristics and mortality were compared across these CT imaging subtypes.

RESULTS

The overall mortality differed significantly between groups (P < .01) and was highest in the 3 moderate to severe CLE groups. Subjects having quantitative but not visual emphysema and subjects with visual but not quantitative emphysema were unique groups with mild COPD, at risk for progression, and with likely different underlying mechanisms. Subjects with PSE and/or moderate to severe CLE had substantial progression of emphysema over 5 years compared with findings in subjects with no CT imaging abnormality (P < .01).

CONCLUSIONS

The combination of visual and quantitative CT imaging features reflects different underlying pathological processes in the heterogeneous COPD syndrome and provides a useful approach to reclassify types of COPD.

TRIAL REGISTRY

ClinicalTrials.gov; No.: NCT00608764; URL: www.clinicaltrials.gov.

Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study

COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how they should be defined. The COPD Genetic Epidemiology Study (COPDGene), which has generated 10-year longitudinal chest imaging, spirometry, and molecular data, is a rich resource for relating COPD phenotypes to underlying genetic and molecular mechanisms. In this article, we place COPDGene clustering studies in context with other highly cited COPD clustering studies, and summarize the main COPD subtype findings from COPDGene. First, most manifestations of COPD occur along a continuum, which explains why continuous aspects of COPD or disease axes may be more accurate and reproducible than subtypes identified through clustering methods. Second, continuous COPD-related measures can be used to create subgroups through the use of predictive models to define cut-points, and we review COPDGene research on blood eosinophil count thresholds as a specific example. Third, COPD phenotypes identified or prioritized through machine learning methods have led to novel biological discoveries, including novel emphysema genetic risk variants and systemic inflammatory subtypes of COPD. Fourth, trajectory-based COPD subtyping captures differences in the longitudinal evolution of COPD, addressing a major limitation of clustering analyses that are confounded by disease severity. Ongoing longitudinal characterization of subjects in COPDGene will provide useful insights about the relationship between lung imaging parameters, molecular markers, and COPD progression that will enable the identification of subtypes based on underlying disease processes and distinct patterns of disease progression, with the potential to improve the clinical relevance and reproducibility of COPD subtypes.

Influence of Emphysema and Air Trapping Heterogeneity on Pulmonary Function in Patients with COPD

Purpose

To explore the influence of emphysema and air trapping heterogeneity on pulmonary function changes in patients with stable chronic obstructive pulmonary disease (COPD).

Patients and methods

One hundred and seventy-nine patients with stable COPD were enrolled in this prospective study. All patients underwent low-dose inspiratory and expiratory CT scanning and pulmonary-function tests. CT quantitative data for the emphysema index (EI) on full-inspiration and air trapping (AT) on full-expiration were measured for the whole lung, the right and left lungs, and the cranial-caudal lung zones. The heterogeneity index (HI) values for emphysema and air trapping were determined as the ratio of the difference to the sum of the respective indexes. The cranial-caudal HI and left–right lung HI were compared between mild-to-moderate (GOLD stage I and II) and severe (GOLD stage III and IV) disease groups. The associations between HI and pulmonary-function measurements adjusted for age, sex, height, smoking history, EI and AT of the total lung were assessed using multiple linear regression analysis.

Results

The absolute values for cranial-caudal HI (AT_CC_HI) and left–right lung HI (AT_LR_HI) on full-expiration were significantly larger in the mild-to-moderate group, while no significant intergroup differences were observed on full-inspiration. COPD patients with lower-zone and/or left-lung predominance showed significantly lower pulmonary function than those with upper-zone and/or right-lung predominance on full-expiration, whereas no significant differences were observed on full-inspiration. The absolute values of AT_CC_HI and AT_LR_HI significantly correlated with pulmonary-function measurements. Higher AT_CC_HI and lower AT_LR_HI absolute values indicated better pulmonary function, after adjusting for age, sex, height, smoking history, EI and AT of the total lung.

Conclusion

Subjects with more heterogeneous distribution and/or upper-zone predominant and/or right-lung predominant patterns on full-expiration tend to have better pulmonary function. Thus, in comparison with emphysema heterogeneity, AT heterogeneity better reflects the pulmonary function changes in COPD patients.

Regional lung deflation with increased airway volume underlies the functional response to bronchodilators in chronic obstructive pulmonary disease

Bronchodilators, including long-acting muscarinic antagonists (LAMAs), improve airflow limitation and lung hyperinflation in patients with chronic obstructive pulmonary disease (COPD). While bronchodilators increase airway caliber and deflate the lungs, little is known about the effects of the local interaction between airway dilation and lung deflation on functional improvements resulting from bronchodilator therapy. This study aimed to explore whether lung deflation with increased airway volume in the upper and lower lung regions would produce different physiological responses to LAMA therapy. Using the clinical data of 41 patients with COPD who underwent spirometry and inspiratory computed tomography (CT) before and 1 year after LAMA treatment, we measured the 1-year change in the airway tree to lung volume percentage ratio (AWV%) for the right upper, middle, and lower lobes (RUL, RML, and RLL) and the left upper and lower lobes (LUL and LLL), and total airway count (TAC) identifiable on CT in relation to the forced expiratory volume in 1 s (FEV1 ). The results showed that LAMA treatment significantly increased the FEV1 and AWV% of the RUL, RML, RLL, LUL, and LLL. Increased AWV% in the RLL and LLL, but not in the RUL and LUL, was correlated with increased FEV1 . In the multivariate analysis, the increased AWV% in the RLL was associated with the increased FEV1 independent of the change in TAC in the RLL after treatment. This is the first study to show that the physiological improvements after bronchodilator treatment in COPD could be mainly due to the combination of regional deflation and increased airway volume of the lower lobes.

DSP variants may be associated with longitudinal change in quantitative emphysema

Background

Emphysema, characterized by lung destruction, is a key component of Chronic Obstructive Pulmonary Disease (COPD) and is associated with increased morbidity and mortality. Genome-wide association studies (GWAS) have identified multiple genetic factors associated with cross-sectional measures of quantitative emphysema, but the genetic determinants of longitudinal change in quantitative measures of emphysema remain largely unknown. Our study aims to identify genetic variants associated with longitudinal change in quantitative emphysema measured by computed tomography (CT) imaging.

Methods

We included current and ex-smokers from two longitudinal cohorts: COPDGene, a study of Non-Hispanic Whites (NHW) and African Americans (AA), and the Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE). We calculated annual change in two quantitative measures of emphysema based on chest CT imaging: percent low attenuation area (≤ − 950HU) (%LAA-950) and adjusted lung density (ALD). We conducted GWAS, separately in 3030 NHW and 1158 AA from COPDGene and 1397 Whites from ECLIPSE. We further explored effects of 360 previously reported variants and a lung function based polygenic risk score on annual change in quantitative emphysema.

Results

In the genome-wide association analysis, no variants achieved genome-wide significance (P < 5e-08). However, in the candidate region analysis, rs2076295 in the DSP gene, previously associated with COPD, lung function and idiopathic pulmonary fibrosis, was associated with change in %LAA-950 (β (SE) = 0.09 (0.02), P = 3.79e-05) and in ALD (β (SE) = − 0.06 (0.02), P = 2.88e-03). A lung function based polygenic risk score was associated with annual change in %LAA-950 (P = 4.03e-02) and with baseline measures of quantitative emphysema (P < 1e-03) and showed a trend toward association with annual change in ALD (P = 7.31e-02).

Conclusions

DSP variants may be associated with longitudinal change in quantitative emphysema. Additional investigation of the DSP gene are likely to provide further insights into the disease progression in emphysema and COPD.

Trial registration

Clinicaltrials.gov Identifier: NCT00608764, NCT00292552.

Electronic supplementary material

The online version of this article (10.1186/s12931-019-1097-8) contains supplementary material, which is available to authorized users.

Clinical features of three-dimensional computed tomography-based radiologic phenotypes of chronic obstructive pulmonary disease

Purpose

The diagnosis and severity of chronic obstructive pulmonary disease (COPD) are defined by airflow limitation using spirometry. However, COPD has diverse clinical features, and several phenotypes based on non-spirometric data have been investigated. To identify novel phenotypes of COPD using radiologic data obtained by three-dimensional computed tomography (3D-CT).

Patients and methods

The inner luminal area and wall thickness of third- to sixth-generation bronchi and the percentage of the low-attenuation area (less than −950 HU) of the lungs were measured using 3D-CT in patients with COPD. Using the radiologic data, hierarchical clustering was performed. Respiratory reactance and resistance were measured to evaluate functional differences among the clusters.

Results

Four clusters were identified among 167 patients with COPD: Cluster I, mild emphysema with severe airway changes, severe airflow limitation, and high exacerbation risk; Cluster II, mild emphysema with moderate airway changes, mild airflow limitation, and mild dyspnea; Cluster III, severe emphysema with moderate airway changes, severe airflow limitation, and increased dyspnea; and Cluster IV, moderate emphysema with mild airway changes, mild airflow limitation, low exacerbation risk, and mild dyspnea. Cluster I had the highest respiratory resistance among the four clusters. Clusters I and III had higher respiratory reactance than Clusters II and IV.

Conclusions

The 3D-CT-based radiologic phenotypes were associated with the clinical features of COPD. Measurement of respiratory resistance and reactance may help to identify phenotypic differences.

Measuring lung function in airways diseases: current and emerging techniques

Chronic airways diseases, including asthma, COPD and cystic fibrosis, cause significant morbidity and mortality and are associated with high healthcare expenditure, in the UK and worldwide. For patients with these conditions, improvements in clinical outcomes are likely to depend on the application of precision medicine, that is, the matching of the right treatment to the right patient at the right time. In this context, the identification and targeting of 'treatable traits' is an important priority in airways disease, both to ensure the appropriate use of existing treatments and to facilitate the development of new disease-modifying therapy. This requires not only better understanding of airway pathophysiology but also an enhanced ability to make physiological measurements of disease activity and lung function and, if we are to impact on the natural history of these diseases, reliable measures in early disease. In this article, we outline some of the key challenges faced by the respiratory community in the management of airways diseases, including early diagnosis, disease stratification and monitoring of therapeutic response. In this context, we review the advantages and limitations of routine physiological measurements of respiratory function including spirometry, body plethysmography and diffusing capacity and discuss less widely used methods such as forced oscillometry, inert gas washout and the multiple inert gas elimination technique. Finally, we highlight emerging technologies including imaging methods such as quantitative CT and hyperpolarised gas MRI as well as quantification of lung inhomogeneity using precise in-airway gas analysis and mathematical modelling. These emerging techniques have the potential to enhance existing measures in the assessment of airways diseases, may be particularly valuable in early disease, and should facilitate the efforts to deliver precision respiratory medicine.

Reprint of: Voxel-Wise Longitudinal Parametric Response Mapping Analysis of Chest Computed Tomography in Smokers

RATIONALE AND OBJECTIVES

Chronic obstructive pulmonary disease is a heterogeneous disease characterized by small airway abnormality and emphysema. We hypothesized that a voxel-wise computed tomography analytic approach would identify patterns of disease progression in smokers.

MATERIALS AND METHODS

We analyzed 725 smokers in spirometric GOLD stages 0-4 with two chest CTs 5 years apart. Baseline inspiration, follow-up inspiration and follow-up expiration images were spatially registered to baseline expiration so that each voxel had correspondences across all time points and respiratory phases. Voxel-wise Parametric Response Mapping (PRM) was then generated for the baseline and follow-up scans. PRM classifies lung as normal, functional small airway disease (PRM^fSAD), and emphysema (PRM^EMPH).

RESULTS

Subjects with low baseline PRM^fSAD and PRM^EMPH predominantly had an increase in PRM^fSAD on follow-up; those with higher baseline PRM^fSAD and PRM^EMPH mostly had increases in PRM^EMPH. For GOLD 0 participants (n = 419), mean 5-year increases in PRM^fSAD and PRM^EMPH were 0.3% for both; for GOLD 1-4 participants (n = 306), they were 0.6% and 1.6%, respectively. Eighty GOLD 0 subjects (19.1%) had overall radiologic progression (30.0% to PRM^fSAD, 52.5% to PRM^EMPH, and 17.5% to both); 153 GOLD 1-4 subjects (50.0%) experienced progression (17.6% to PRM^fSAD, 48.4% to PRM^EMPH, and 34.0% to both). In a multivariable model, both baseline PRM^fSAD and PRM^EMPH were associated with development of PRM^EMPH on follow-up, although this relationship was diminished at higher levels of baseline PRM^EMPH.

CONCLUSION

A voxel-wise longitudinal PRM analytic approach can identify patterns of disease progression in smokers with and without chronic obstructive pulmonary disease.

Voxel-Wise Longitudinal Parametric Response Mapping Analysis of Chest Computed Tomography in Smokers

RATIONALE AND OBJECTIVES

Chronic obstructive pulmonary disease is a heterogeneous disease characterized by small airway abnormality and emphysema. We hypothesized that a voxel-wise computed tomography analytic approach would identify patterns of disease progression in smokers.

MATERIALS AND METHODS

We analyzed 725 smokers in spirometric GOLD stages 0-4 with two chest CTs 5 years apart. Baseline inspiration, follow-up inspiration and follow-up expiration images were spatially registered to baseline expiration so that each voxel had correspondences across all time points and respiratory phases. Voxel-wise Parametric Response Mapping (PRM) was then generated for the baseline and follow-up scans. PRM classifies lung as normal, functional small airway disease (PRMfSAD), and emphysema (PRMEMPH).

RESULTS

Subjects with low baseline PRMfSAD and PRMEMPH predominantly had an increase in PRMfSAD on follow-up; those with higher baseline PRMfSAD and PRMEMPH mostly had increases in PRMEMPH. For GOLD 0 participants (n = 419), mean 5-year increases in PRMfSAD and PRMEMPH were 0.3% for both; for GOLD 1-4 participants (n = 306), they were 0.6% and 1.6%, respectively. Eighty GOLD 0 subjects (19.1%) had overall radiologic progression (30.0% to PRMfSAD, 52.5% to PRMEMPH, and 17.5% to both); 153 GOLD 1-4 subjects (50.0%) experienced progression (17.6% to PRMfSAD, 48.4% to PRMEMPH, and 34.0% to both). In a multivariable model, both baseline PRMfSAD and PRMEMPH were associated with development of PRMEMPH on follow-up, although this relationship was diminished at higher levels of baseline PRMEMPH.

CONCLUSION

A voxel-wise longitudinal PRM analytic approach can identify patterns of disease progression in smokers with and without chronic obstructive pulmonary disease.

Imaging Advances in Chronic Obstructive Pulmonary Disease. Insights from the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) Study

The Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study, which began in 2007, is an ongoing multicenter observational cohort study of more than 10,000 current and former smokers. The study is aimed at understanding the etiology, progression, and heterogeneity of chronic obstructive pulmonary disease (COPD). In addition to genetic analysis, the participants have been extensively characterized by clinical questionnaires, spirometry, volumetric inspiratory and expiratory computed tomography, and longitudinal follow-up, including follow-up computed tomography at 5 years after enrollment. The purpose of this state-of-the-art review is to summarize the major advances in our understanding of COPD resulting from the imaging findings in the COPDGene study. Imaging features that are associated with adverse clinical outcomes include early interstitial lung abnormalities, visual presence and pattern of emphysema, the ratio of pulmonary artery to ascending aortic diameter, quantitative evaluation of emphysema, airway wall thickness, and expiratory gas trapping. COPD is characterized by the early involvement of the small conducting airways, and the addition of expiratory scans has enabled measurement of small airway disease. Computational advances have enabled indirect measurement of nonemphysematous gas trapping. These metrics have provided insights into the pathogenesis and prognosis of COPD and have aided early identification of disease. Important quantifiable extrapulmonary findings include coronary artery calcification, cardiac morphology, intrathoracic and extrathoracic fat, and osteoporosis. Current active research includes identification of novel quantitative measures for emphysema and airway disease, evaluation of dose reduction techniques, and use of deep learning for phenotyping COPD.

FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images

Pulmonary fissure detection in computed tomography (CT) is a critical component for automatic lobar segmentation. The majority of fissure detection methods use feature descriptors that are hand-crafted, low-level, and have local spatial extent. The design of such feature detectors is typically targeted toward normal fissure anatomy, yielding low sensitivity to weak, and abnormal fissures that are common in clinical data sets. Furthermore, local features commonly suffer from low specificity, as the complex textures in the lung can be indistinguishable from the fissure when the global context is not considered. We propose a supervised discriminative learning framework for simultaneous feature extraction and classification. The proposed framework, called FissureNet, is a coarse-to-fine cascade of two convolutional neural networks. The coarse-to-fine strategy alleviates the challenges associated with training a network to segment a thin structure that represents a small fraction of the image voxels. FissureNet was evaluated on a cohort of 3706 subjects with inspiration and expiration 3DCT scans from the COPDGene clinical trial and a cohort of 20 subjects with 4DCT scans from a lung cancer clinical trial. On both data sets, FissureNet showed superior performance compared with a deep learning approach using the U-Net architecture and a Hessian-based fissure detection method in terms of area under the precision-recall curve (PR-AUC). The overall PR-AUC for FissureNet, U-Net, and Hessian on the COPDGene (lung cancer) data set was 0.980 (0.966), 0.963 (0.937), and 0.158 (0.182), respectively. On a subset of 30 COPDGene scans, FissureNet was compared with a recently proposed advanced fissure detection method called derivative of sticks (DoS) and showed superior performance with a PR-AUC of 0.991 compared with 0.668 for DoS.