Using Pulmonary Imaging to Move Chronic Obstructive Pulmonary Disease beyond FEV1

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.

Phenotyping of COPD with MRI in comparison to same-day CT in a multi-centre trial

OBJECTIVES

A prospective, multi-centre study to evaluate concordance of morphologic lung MRI and CT in chronic obstructive pulmonary disease (COPD) phenotyping for airway disease and emphysema.

METHODS

A total of 601 participants with COPD from 15 sites underwent same-day morpho-functional chest MRI and paired inspiratory-expiratory CT. Two readers systematically scored bronchial wall thickening, bronchiectasis, centrilobular nodules, air trapping and lung parenchyma defects in each lung lobe and determined COPD phenotype. A third reader acted as adjudicator to establish consensus. Inter-modality and inter-reader agreement were assessed using Cohen's kappa (im-κ and ir-κ).

RESULTS

The mean combined MRI score for bronchiectasis/bronchial wall thickening was 4.5/12 (CT scores, 2.2/12 for bronchiectasis and 6/12 for bronchial wall thickening; im-κ, 0.04-0.3). Expiratory right/left bronchial collapse was observed in 51 and 47/583 on MRI (62 and 57/599 on CT; im-κ, 0.49-0.52). Markers of small airways disease on MRI were 0.15/12 for centrilobular nodules (CT, 0.34/12), 0.94/12 for air trapping (CT, 0.9/12) and 7.6/12 for perfusion deficits (CT, 0.37/12 for mosaic attenuation; im-κ, 0.1-0.41). The mean lung defect score on MRI was 1.3/12 (CT emphysema score, 5.8/24; im-κ, 0.18-0.26). Airway-/emphysema/mixed COPD phenotypes were assigned in 370, 218 and 10 of 583 cases on MRI (347, 218 and 34 of 599 cases on CT; im-κ, 0.63). For all examined features, inter-reader agreement on MRI was lower than on CT.

CONCLUSION

Concordance of MRI and CT for phenotyping of COPD in a multi-centre setting was substantial with variable inter-modality and inter-reader concordance for single diagnostic key features.

CLINICAL RELEVANCE STATEMENT

MRI of lung morphology may well serve as a radiation-free imaging modality for COPD in scientific and clinical settings, given that its potential and limitations as shown here are carefully considered.

KEY POINTS

• In a multi-centre setting, MRI and CT showed substantial concordance for phenotyping of COPD (airway-/emphysema-/mixed-type). • Individual features of COPD demonstrated variable inter-modality concordance with features of pulmonary hypertension showing the highest and bronchiectasis showing the lowest concordance. • For all single features of COPD, inter-reader agreement was lower on MRI than on CT.

Ultra-low dose chest CT with silver filter and deep learning reconstruction significantly reduces radiation dose and retains quantitative information in the investigation and monitoring of lymphangioleiomyomatosis (LAM)

PURPOSE

Frequent CT scans to quantify lung involvement in cystic lung disease increases radiation exposure. Beam shaping energy filters can optimize imaging properties at lower radiation dosages. The aim of this study is to investigate whether use of SilverBeam filter and deep learning reconstruction algorithm allows for reduced radiation dose chest CT scanning in patients with lymphangioleiomyomatosis (LAM).

MATERIAL AND METHODS

In a single-center prospective study, 60 consecutive patients with LAM underwent chest CT at standard and ultra-low radiation doses. Standard dose scan was performed with standard copper filter and ultra-low dose scan was performed with SilverBeam filter. Scans were reconstructed using a soft tissue kernel with deep learning reconstruction (AiCE) technique and using a soft tissue kernel with hybrid iterative reconstruction (AIDR3D). Cyst scores were quantified by semi-automated software. Signal-to-noise ratio (SNR) was calculated for each reconstruction. Data were analyzed by linear correlation, paired t-test, and Bland-Altman plots.

RESULTS

Patients averaged 49.4 years and 100% were female with mean BMI 26.6 ± 6.1 kg/m2. Cyst score measured by AiCE reconstruction with SilverBeam filter correlated well with that of AIDR3D reconstruction with standard filter, with a 1.5% difference, and allowed for an 85.5% median radiation dosage reduction (0.33 mSv vs. 2.27 mSv, respectively, p < 0.001). Compared to standard filter with AIDR3D, SNR for SilverBeam AiCE images was slightly lower (3.2 vs. 3.1, respectively, p = 0.005).

CONCLUSION

SilverBeam filter with deep learning reconstruction reduces radiation dosage of chest CT, while maintaining accuracy of cyst quantification as well as image quality in cystic lung disease.

CLINICAL RELEVANCE STATEMENT

Radiation dosage from chest CT can be significantly reduced without sacrificing image quality by using silver filter in combination with a deep learning reconstructive algorithm.

KEY POINTS

• Deep learning reconstruction in chest CT had no significant effect on cyst quantification when compared to conventional hybrid iterative reconstruction. • SilverBeam filter reduced radiation dosage by 85.5% compared to standard dose chest CT. • SilverBeam filter in coordination with deep learning reconstruction maintained image quality and diagnostic accuracy for cyst quantification when compared to standard dose CT with hybrid iterative reconstruction.

A fully automated pipeline for the extraction of pectoralis muscle area from chest computed tomography scans

Background

Computed tomography (CT)-derived pectoralis muscle area (PMA) measurements are prognostic in people with or at-risk of COPD, but fully automated PMA extraction has yet to be developed. Our objective was to develop and validate a PMA extraction pipeline that can automatically: 1) identify the aortic arch slice; and 2) perform pectoralis segmentation at that slice.

Methods

CT images from the Canadian Cohort of Obstructive Lung Disease (CanCOLD) study were used for pipeline development. Aorta atlases were used to automatically identify the slice containing the aortic arch by group-based registration. A deep learning model was trained to segment the PMA. The pipeline was evaluated in comparison to manual segmentation. An external dataset was used to evaluate generalisability. Model performance was assessed using the Dice-Sorensen coefficient (DSC) and PMA error.

Results

In total 90 participants were used for training (age 67.0±9.9 years; forced expiratory volume in 1 s (FEV1) 93±21% predicted; FEV1/forced vital capacity (FVC) 0.69±0.10; 47 men), and 32 for external testing (age 68.6±7.4 years; FEV1 65±17% predicted; FEV1/FVC 0.50±0.09; 16 men). Compared with manual segmentation, the deep learning model achieved a DSC of 0.94±0.02, 0.94±0.01 and 0.90±0.04 on the true aortic arch slice in the train, validation and external test sets, respectively. Automated aortic arch slice detection obtained distance errors of 1.2±1.3 mm and 1.6±1.5 mm on the train and test data, respectively. Fully automated PMA measurements were not different from manual segmentation (p>0.05). PMA measurements were different between people with and without COPD (p=0.01) and correlated with FEV1 % predicted (p<0.05).

Conclusion

A fully automated CT PMA extraction pipeline was developed and validated for use in research and clinical practice.

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.

A Novel CT-Based Radiomics Features Analysis for Identification and Severity Staging of COPD

RATIONALE AND OBJECTIVES

To evaluate the role of radiomics based on Chest Computed Tomography (CT) in the identification and severity staging of chronic obstructive pulmonary disease (COPD).

MATERIALS AND METHODS

This retrospective analysis included 322 participants (249 COPD patients and 73 control subjects). In total, 1395 chest CT-based radiomics features were extracted from each participant's CT images. Three feature selection methods, including variance threshold, Select K Best method, and least absolute shrinkage and selection operator (LASSO), and two classification methods, including support vector machine (SVM) and logistic regression (LR), were used as identification and severity classification of COPD. Performance was compared by AUC, accuracy, sensitivity, specificity, precision, and F1-score.

RESULTS

38 and 10 features were selected to construct radiomics models to detect and stage COPD, respectively. For COPD identification, SVM classifier achieved AUCs of 0.992 and 0.970, while LR classifier achieved AUCs of 0.993 and 0.972 in the training set and test set, respectively. For the severity staging of COPD, the mentioned two machine learning classifiers can better differentiate less severity (GOLD1 + GOLD2) group from greater severity (GOLD3 + GOLD4) group. The AUCs of SVM and LR is 0.907 and 0.903 in the training set, and that of 0.799 and 0.797 in the test set.

CONCLUSION

The present study showed that the novel radiomics approach based on chest CT images that can be used for COPD identification and severity classification, and the constructed radiomics model demonstrated acceptable performance.

Subclinical Pulmonary Congestion and Abnormal Hemodynamics in Heart Failure With Preserved Ejection Fraction

OBJECTIVES

We hypothesized that quantitative computed tomography (QCT) imaging would reveal subclinical increases in lung congestion in patients with heart failure and preserved ejection fraction (HFpEF) and that this would be related to pulmonary vascular hemodynamic abnormalities.

BACKGROUND

Gross evidence of lung congestion on physical examination, laboratory tests, and radiography is typically absent among compensated ambulatory patients with HFpEF. However, pulmonary gas transfer abnormalities are commonly observed and associated with poor outcomes.

METHODS

Patients referred for invasive hemodynamic exercise testing who had undergone chest computed tomography imaging within 1 month were identified (N = 137). A novel artificial intelligence QCT algorithm was used to measure pulmonary fluid content.

RESULTS

Compared with control subjects with noncardiac dyspnea, patients with HFpEF displayed increased mean lung density (-758 Hounsfield units [HU] [-793, -709 HU] Hounsfield units vs -787 HU [-828, -747 HU]; P = 0.002) and a higher ratio of extravascular lung water to total lung volume (EVLWV/TLV) (1.25 [0.80, 1.76] vs 0.66 [0.01, 1.03]; P < 0.0001) by QCT imaging, indicating greater lung congestion. EVLWV/TLV was directly correlated with pulmonary vascular pressures at rest, with stronger correlations observed during exercise. Patients with increasing tertiles of EVLWV/TLV demonstrated higher mean pulmonary artery pressures at rest (34 ± 11 mm Hg vs 39 ± 14 mm Hg vs 45 ± 17 mm Hg; P = 0.0003) and during exercise (55 ± 17 mm Hg vs 59 ± 17 mm Hg vs 69 ± 22 mm Hg; P = 0.0003).

CONCLUSIONS

QCT imaging identifies subclinical lung congestion in HFpEF that is not clinically apparent but is related to abnormalities in pulmonary vascular hemodynamics. These data provide new insight into the long-term effects of altered hemodynamics on pulmonary structure and function in HFpEF.

COPD Diagnosis: Time for Disruption

Articulating a satisfactory definition of a disease is surprisingly difficult. Despite the alarming individual, societal and economic burden of chronic obstructive pulmonary disease (COPD), diagnosis is still largely based on a physiologically dominated disease conception, with spirometrically determined airflow limitation as a cardinal feature of the disease. The diagnostic inaccuracy and insensitivity of this physiological disease definition is reviewed considering scientific developments of imaging of the respiratory system in particular. Disease must be approached as a fluid concept in response to new scientific and medical discoveries, but labelling as well as mislabelling someone as diseased, will have enormous individual, social and financial implications. Nosology of COPD urgently needs to dynamically integrate more sensitive diagnostic procedures to detect the breadth of abnormalities early in the disease process. Integration of broader information for the identification of abnormalities in the respiratory system is a cornerstone for research models of underlying pathomechanisms to create a breakthrough in research.

Identification of Proteomic Signatures in Chronic Obstructive Pulmonary Disease Emphysematous Phenotype

Chronic obstructive pulmonary disease (COPD) is a highly heterogeneous disease. Emphysematous phenotype is the most common and critical phenotype, which is characterized by progressive lung destruction and poor prognosis. However, the underlying mechanism of this structural damage has not been completely elucidated. A total of 12 patients with COPD emphysematous phenotype (COPD-E) and nine patients with COPD non-emphysematous phenotype (COPD-NE) were enrolled to determine differences in differential abundant protein (DAP) expression between both groups. Quantitative tandem mass tag–based proteomics was performed on lung tissue samples of all patients. A total of 29 and 15 lung tissue samples from patients in COPD-E and COPD-NE groups, respectively, were used as the validation cohort to verify the proteomic analysis results using western blotting. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted for DAPs. A total of 4,343 proteins were identified, of which 25 were upregulated and 11 were downregulated in the COPD-E group. GO and KEGG analyses showed that wound repair and retinol metabolism–related pathways play an essential role in the molecular mechanism of COPD emphysematous phenotype. Three proteins, namely, KRT17, DHRS9, and FMO3, were selected for validation. While KRT17 and DHRS9 were highly expressed in the lung tissue samples of the COPD-E group, FMO3 expression was not significantly different between both groups. In conclusion, KRT17 and DHRS9 are highly expressed in the lung tissue of patients with COPD emphysematous phenotype. Therefore, these proteins might involve in wound healing and retinol metabolism in patients with emphysematous phenotype and can be used as phenotype-specific markers.

Ultra-short echo-time magnetic resonance imaging lung segmentation with under-Annotations and domain shift

Ultra-short echo-time (UTE) magnetic resonance imaging (MRI) provides enhanced visualization of pulmonary structural and functional abnormalities and has shown promise in phenotyping lung disease. Here, we describe the development and evaluation of a lung segmentation approach to facilitate UTE MRI methods for patient-based imaging. The proposed approach employs a k-means algorithm in kernel space for pair-wise feature clustering and imposes image domain continuous regularization, coined as continuous kernel k-means (CKKM). The high-order CKKM algorithm was simplified through upper bound relaxation and solved within an iterative continuous max-flow framework. We combined the CKKM with U-net and atlas-based approaches and comprehensively evaluated the performance on 100 images from 25 patients with asthma and bronchial pulmonary dysplasia enrolled at Robarts Research Institute (Western University, London, Canada) and Centre Hospitalier Universitaire (Sainte-Justine, Montreal, Canada). For U-net, we trained the network five times on a mixture of five different images with under-annotations and applied the model to 64 images from the two centres. We also trained a U-net on five images with full and brush annotations from one centre, and tested the model on 32 images from the other centre. For an atlas-based approach, we employed three atlas images to segment 64 target images from the two centres through straightforward atlas registration and label fusion. We applied the CKKM algorithm to the baseline U-net and atlas outputs and refined the initial segmentation through multi-volume image fusion. The integration of CKKM substantially improved baseline results and yielded, with minimal computational cost, segmentation accuracy, and precision that were greater than some state-of-the-art deep learning models and similar to experienced observer manual segmentation. This suggests that deep learning and atlas-based approaches may be utilized to segment UTE MRI datasets using relatively small training datasets with under-annotations.

Radiomics approach for survival prediction in chronic obstructive pulmonary disease

OBJECTIVES

To apply radiomics analysis for overall survival prediction in chronic obstructive pulmonary disease (COPD), and evaluate the performance of the radiomics signature (RS).

METHODS

This study included 344 patients from the Korean Obstructive Lung Disease (KOLD) cohort. External validation was performed on a cohort of 112 patients. In total, 525 chest CT-based radiomics features were semi-automatically extracted. The five most useful features for survival prediction were selected by least absolute shrinkage and selection operation (LASSO) Cox regression analysis and used to generate a RS. The ability of the RS for classifying COPD patients into high or low mortality risk groups was evaluated with the Kaplan-Meier survival analysis and Cox proportional hazards regression analysis.

RESULTS

The five features remaining after the LASSO analysis were %LAA_-950, AWT_Pi10_6^th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA_18mm. The RS demonstrated a C-index of 0.774 in the discovery group and 0.805 in the validation group. Patients with a RS greater than 1.053 were classified into the high-risk group and demonstrated worse overall survival than those in the low-risk group in both the discovery (log-rank test, < 0.001; hazard ratio [HR], 5.265) and validation groups (log-rank test, < 0.001; HR, 5.223). For both groups, RS was significantly associated with overall survival after adjustments for patient age and body mass index.

CONCLUSIONS

A radiomics approach for survival prediction and risk stratification in COPD patients is feasible, and the constructed radiomics model demonstrated acceptable performance. The RS derived from chest CT data of COPD patients was able to effectively identify those at increased risk of mortality.

KEY POINTS

• A total of 525 chest CT-based radiomics features were extracted and the five radiomics features of %LAA_-950, AWT_Pi10_6^th, AWT_Pi10_heterogeneity, %WA_heterogeneity, and VA_18mm were selected to generate a radiomics model. • A radiomics model for predicting survival of COPD patients demonstrated reliable performance with a C-index of 0.774 in the discovery group and 0.805 in the validation group. • Radiomics approach was able to effectively identify COPD patients with an increased risk of mortality, and patients assigned to the high-risk group demonstrated worse overall survival in both the discovery and validation groups.

Improving clinical disease subtyping and future events prediction through a chest CT-based deep learning approach

PURPOSE

To develop and evaluate a deep learning (DL) approach to extract rich information from high-resolution computed tomography (HRCT) of patients with chronic obstructive pulmonary disease (COPD).

METHODS

We develop a DL-based model to learn a compact representation of a subject, which is predictive of COPD physiologic severity and other outcomes. Our DL model learned: (a) to extract informative regional image features from HRCT; (b) to adaptively weight these features and form an aggregate patient representation; and finally, (c) to predict several COPD outcomes. The adaptive weights correspond to the regional lung contribution to the disease. We evaluate the model on 10 300 participants from the COPDGene cohort.

RESULTS

Our model was strongly predictive of spirometric obstruction ( r 2  =  0.67) and grouped 65.4% of subjects correctly and 89.1% within one stage of their GOLD severity stage. Our model achieved an accuracy of 41.7% and 52.8% in stratifying the population-based on centrilobular (5-grade) and paraseptal (3-grade) emphysema severity score, respectively. For predicting future exacerbation, combining subjects' representations from our model with their past exacerbation histories achieved an accuracy of 80.8% (area under the ROC curve of 0.73). For all-cause mortality, in Cox regression analysis, we outperformed the BODE index improving the concordance metric (ours: 0.61 vs BODE: 0.56).

CONCLUSIONS

Our model independently predicted spirometric obstruction, emphysema severity, exacerbation risk, and mortality from CT imaging alone. This method has potential applicability in both research and clinical practice.

A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects

Chronic obstructive pulmonary disease (COPD) is a respiratory disorder involving abnormalities of lung parenchymal morphology with different severities. COPD is assessed by pulmonary-function tests and computed tomography-based approaches. We introduce a new classification method for COPD grouping based on deep learning and a parametric-response mapping (PRM) method. We extracted parenchymal functional variables of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%) with an image registration technique, being provided as input parameters of 3D convolutional neural network (CNN). The integrated 3D-CNN and PRM (3D-cPRM) achieved a classification accuracy of 89.3% and a sensitivity of 88.3% in five-fold cross-validation. The prediction accuracy of the proposed 3D-cPRM exceeded those of the 2D model and traditional 3D CNNs with the same neural network, and was comparable to that of 2D pretrained PRM models. We then applied a gradient-weighted class activation mapping (Grad-CAM) that highlights the key features in the CNN learning process. Most of the class-discriminative regions appeared in the upper and middle lobes of the lung, consistent with the regions of elevated fSAD% and Emph% in COPD subjects. The 3D-cPRM successfully represented the parenchymal abnormalities in COPD and matched the CT-based diagnosis of COPD.

Value of metalloproteinases in predicting COPD in heavy urban smokers

Background

Emphysema in asymptomatic heavy smokers can be detected during CT-scan screening for lung cancer. Metalloproteinases (MMPs) have been found to play a role in the pathogenesis of chronic obstructive pulmonary disease and to possibly serve as biomarkers for emphysema.

Methods

The NYU Lung Cancer Biomarker Center enrolled study subjects over 50 years of age with lung cancer risk factors from January 1, 2010, to December 31, 2015. These subjects received chest multi-detector computed tomography, spirometry, and provided serum for immunoassays for metalloproteinases (MMP) -1, -2, -7, -9, -10 and tissue inhibitor of metalloproteinases (TIMP) -1 and -2.

Results

Three hundred sixteen study subjects were enrolled. Of the 222 patients who met the inclusion criteria, 46% had emphysema. Smokers with emphysema had increased pack-years of smoking compared to smokers without emphysema (51 ± 24 pack-years (mean ± sd) versus 37 ± 20; p < 0.0001). Smokers with emphysema also had lower FEV₁/FVC percent compared to smokers without emphysema (68 ± 11 (mean ± sd) versus 75 ± 8; p < 0.0001). Increased age and pack-years of smoking were associated with increased odds of emphysema. None of the metalloproteinases or tissue inhibitors of metalloproteinases were useful to predict the presence of emphysema in smokers.

Conclusion

Emphysema was detected by CT in almost half of heavy urban smokers. Serum MMP levels provided minimal additional information to improve the detection of mild emphysema among smokers given their clinical characteristics (age, pack-years, and FEV₁/FVC ratio).

Bronchial Rheoplasty for Treatment of Chronic Bronchitis. Twelve-Month Results from a Multicenter Clinical Trial

Rationale: Chronic bronchitis (CB) is characterized by productive cough with excessive mucus production, resulting in quality-of-life impairment and increased exacerbation risk. Bronchial rheoplasty uses an endobronchial catheter to apply nonthermal pulsed electrical fields to the airways. Preclinical studies have demonstrated epithelial ablation followed by regeneration of normalized epithelium.Objectives: To evaluate the feasibility, safety, and initial outcomes of bronchial rheoplasty in patients with CB.Methods: Pooled analysis of two separate studies enrolling 30 patients undergoing bilateral bronchial rheoplasty was conducted. Follow-up through 6 months (primary outcome) and 12 months included assessment of adverse events, airway histology, and changes in symptoms using the Chronic Obstructive Pulmonary Disease (COPD) Assessment Test and St. George's Respiratory Questionnaire (SGRQ).Measurements and Main Results: Bronchial rheoplasty was performed in all 30 patients (63% male; mean [SD] age, 67 [7.4]; mean [SD] postbronchodilator FEV1, 65% [21%]; mean [SD] COPD Assessment Test score 25.6 [7.1]; mean [SD] SGRQ score, 59.6 [15.3]). There were no device-related and four procedure-related serious adverse events through 6 months, and there were none thereafter through 12 months. The most frequent nonserious, device- and/or procedure-related event through 6 months was mild hemoptysis in 47% (14 of 30) patients. Histologically, the mean goblet cell hyperplasia score was reduced by a statistically significant amount (P < 0.001). Significant changes from baseline to 6 months in COPD Assessment Test (mean, -7.9; median, -8.0; P = 0.0002) and SGRQ (mean, -14.6; median, -7.2; P = 0.0002) scores were observed, with similar observations through 12 months.Conclusions: This study provides the first clinical evidence of the feasibility, safety, and initial outcomes of bronchial rheoplasty in symptomatic patients with CB.Clinical trial registered with www.anzctr.org.au (ACTRN 12617000330347) and clinicaltrials.gov (NCT03107494).

Ventilation heterogeneity in smokers: role of unequal lung expansion and peripheral lung structure

Smoking-induced ventilation heterogeneity measured at the mouth via established washout indices [lung clearance index (LCI) and alveolar mixing efficiency (AME)] potentially results from unequal expansion, which can be quantified by computer tomography (CT), and structural changes down to the lung periphery, characterized by CT parametric response mapping indices [percentage of lung affected by functional small airway disease (PRM^fSAD) and emphysema (PRM^Emph)]. By combining CT imaging and nitrogen (N₂) washout tests in smokers, we specifically examined the roles of unequal lung expansion and peripheral structure. We first extracted three-dimensional maps of local lung expansion from registered inspiratory/expiratory CT images in 50 smokers (GOLD 0-IV) to compute for each smoker the theoretical N₂ washout concentration curve solely attributable to unequal local expansion. By a head-on comparison with washout N₂ concentrations measured at the mouth in the same smokers supine, we observed that 1) LCI increased from 4.8 ± 0.2 (SD) to 6.6 ± 0.8 (SD) due to unequal lung expansion alone and further increased to 9.0 ± 1.5 (SD) independent of local expansion and 2) AME decreased (from 100% by definition) to 95 ± 2 (SD)% due to unequal expansion alone and further decreased to 75 ± 7(SD)% independent of local expansion. In a multiple regression between the washout indices and CT-derived PRM^fSAD and PRM^Emph, LCI was related to PRM^fSAD (r = +0.58; P < 0.001), whereas AME was related to both PRM^fSAD (r_partial = -0.44; P = 0.002) and PRM^Emph (r_partial = -0.31; P = 0.033), in line with AME being dominated by alterations in peripheral structure. We conclude that smokers showing an increased LCI without corresponding AME decrease are predominantly affected by unequal lung expansion, whereas an AME decrease with a commensurate LCI increase indicates a smoking-induced alteration of peripheral structure.NEW & NOTEWORTHY A head-on comparison between imaging and multiple breath washout in supine smokers shows that computer tomography-measured unequal local lung expansion accounts for 50% or less of smoking-induced increase in ventilation heterogeneity. The contributions from unequal lung expansion and peripheral structure to the two main washout indices also explain their respective association with parametric response mapping indices.

Validation of Questionnaire-based Case Definitions for Chronic Obstructive Pulmonary Disease

BACKGROUND

Various questionnaire-based definitions of chronic obstructive pulmonary disease (COPD) have been applied using the US representative National Health and Nutrition Examination Survey (NHANES), but few have been validated against objective lung function data. We validated two prior definitions that incorporated self-reported physician diagnosis, respiratory symptoms, and/or smoking. We also validated a new definition that we developed empirically using gradient boosting, an ensemble machine learning method.

METHODS

Data came from 7,996 individuals 40-79 years who participated in NHANES 2007-2012 and underwent spirometry. We considered participants "true" COPD cases if their ratio of postbronchodilator forced expiratory volume in 1 second to forced vital capacity was below 0.7 or the lower limit of normal. We stratified all analyses by smoking history. We developed a gradient boosting model for smokers only; predictors assessed (25 total) included sociodemographics, inhalant exposures, clinical variables, and respiratory symptoms.

RESULTS

The spirometry-based COPD prevalence was 26% for smokers and 8% for never smokers. Among smokers, using questionnaire-based definitions resulted in a COPD prevalence ranging from 11% to 16%, sensitivity ranging from 18% to 35%, and specificity ranging from 88% to 92%. The new definition classified participants based on age, bronchodilator use, body mass index (BMI), smoking pack-years, and occupational organic dust exposure, and resulted in the highest sensitivity (35%) and specificity (92%) among smokers. Among never smokers, the COPD prevalence ranged from 4% to 5%, and we attained good specificity (96%) at the expense of sensitivity (9-10%).

CONCLUSION

Our results can be used to parametrize misclassification assumptions for quantitative bias analysis when pulmonary function data are unavailable.

Pulmonary hypertension in chronic obstructive pulmonary disease

Even mild pulmonary hypertension (PH) is associated with increased mortality and morbidity in patients with chronic obstructive pulmonary disease (COPD). However, the underlying mechanisms remain elusive; therefore, specific and efficient treatment options are not available. Therapeutic approaches tested in the clinical setting, including long-term oxygen administration and systemic vasodilators, gave disappointing results and might be only beneficial for specific subgroups of patients. Preclinical studies identified several therapeutic approaches for the treatment of PH in COPD. Further research should provide deeper insight into the complex pathophysiological mechanisms driving vascular alterations in COPD, especially as such vascular (molecular) alterations have been previously suggested to affect COPD development. This review summarizes the current understanding of the pathophysiology of PH in COPD and gives an overview of the available treatment options and recent advances in preclinical studies. LINKED ARTICLES: This article is part of a themed issue on Risk factors, comorbidities, and comedications in cardioprotection. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v178.1/issuetoc.

Dynamic perfluorinated gas MRI reveals abnormal ventilation despite normal FEV1 in cystic fibrosis

We hypothesized that dynamic perfluorinated gas MRI would sensitively detect mild cystic fibrosis (CF) lung disease. This cross-sectional study enrolled 20 healthy volunteers and 24 stable subjects with CF, including a subgroup of subjects with normal forced expiratory volume in the first second (FEV1; >80% predicted, n = 9). Dynamic fluorine-19-enhanced MRI (19F MRI) were acquired during sequential breath holds while breathing perfluoropropane (PFP) and during gas wash-out. Outcomes included the fraction of lung without significant ventilation (ventilation defect percent, VDP) and time constants that described PFP wash-in and wash-out kinetics. VDP values (mean ± SD) of healthy controls (3.87% ± 2.7%) were statistically different from moderate CF subjects (19.5% ± 15.5%, P = 0.001) but not from mild CF subjects (10.4% ± 9.9%, P = 0.24). In contrast, the fractional lung volume with slow gas wash-out was elevated both in subjects with mild (9.61% ± 4.87%; P = 0.0066) and moderate CF (16.01% ± 5.01%; P = 0.0002) when compared with healthy controls (3.84% ± 2.16%) and distinguished mild from moderate CF (P = 0.006). 19F MRI detected significant ventilation abnormalities in subjects with CF. The ability of gas wash-out kinetics to distinguish between healthy and mild CF lung disease subjects makes 19F MRI a potentially valuable method for the characterization of early lung disease in CF. This study has been registered at ClinicalTrials.gov (NCT03489590).

Quantitative CT detects progression in COPD patients with severe emphysema in a 3-month interval

OBJECTIVES

Chronic obstructive pulmonary disease (COPD) is characterized by variable contributions of emphysema and airway disease on computed tomography (CT), and still little is known on their temporal evolution. We hypothesized that quantitative CT (QCT) is able to detect short-time changes in a cohort of patients with very severe COPD.

METHODS

Two paired in- and expiratory CT each from 70 patients with avg. GOLD stage of 3.6 (mean age = 66 ± 7.5, mean FEV1/FVC = 35.28 ± 7.75) were taken 3 months apart and analyzed by fully automatic software computing emphysema (emphysema index (EI), mean lung density (MLD)), air-trapping (ratio expiration to inspiration of mean lung attenuation (E/I MLA), relative volume change between - 856 HU and - 950 HU (RVC_856-950)), and parametric response mapping (PRM) parameters for each lobe separately and the whole lung. Airway metrics measured were wall thickness (WT) and lumen area (LA) for each airway generation and the whole lung.

RESULTS

The average of the emphysema parameters (EI, MLD) increased significantly by 1.5% (p < 0.001) for the whole lung, whereas air-trapping parameters (E/I MLA, RVC_856-950) were stable. PRM_Emph increased from 34.3 to 35.7% (p < 0.001), whereas PRM_Normal decrased from 23.6% to 22.8% (p = 0.012). WT decreased significantly from 1.17 ± 0.18 to 1.14 ± 0.19 mm (p = 0.036) and LA increased significantly from 25.08 ± 4.49 to 25.84 ± 4.87 mm² (p = 0.041) for the whole lung. The generation-based analysis showed heterogeneous results.

CONCLUSION

QCT detects short-time progression of emphysema in severe COPD. The changes were partly different among lung lobes and airway generations, indicating that QCT is useful to address the heterogeneity of COPD progression.

KEY POINTS

• QCT detects short-time progression of emphysema in severe COPD in a 3-month period. • QCT is able to quantify even slight parenchymal changes, which were not detected by spirometry. • QCT is able to address the heterogeneity of COPD, revealing inconsistent changes individual lung lobes and airway generations.

Comparative analysis of pathophysiological parameters between emphysematous smokers and emphysematous patients with COPD

Emphysematous smokers with normal spirometry form a considerable proportion of the clinical population. However, despite presenting with respiratory symptoms and activity limitation, they cannot be diagnosed with chronic obstructive lung disease (COPD) according to current criteria. Thus, we aimed to determine whether emphysema in smokers has a different pathogenesis from that in patients with COPD. We compared 12 pairs of lung tissue samples from emphysematous patients with normal spirometry and COPD, and determined the degree of emphysema using computed tomography. With a focus on COPD-related pathogenesis, we independently assessed inflammatory response, protease-antiprotease balance, oxidative stress, and apoptosis in both groups. Both groups showed similar pathological changes at a comparable degree of emphysema; the expression of inflammatory factors was comparable, with overexpression of proteases and decreased levels of antiproteases. Moreover, there was no significant difference in the activities of glutathione and superoxide dismutase, and expression of apoptosis-related factors. In conclusion, emphysema in smokers with normal spirometry and in patients with COPD had similar pathogenesis. Forced expiratory volume in 1 second cannot be used as the sole diagnostic criterion in patients with COPD; early intervention is of great importance to such patients.

PET/MRI attenuation estimation in the lung: A review of past, present, and potential techniques

Positron emission tomography/magnetic resonance imaging (PET/MRI) potentially offers several advantages over positron emission tomography/computed tomography (PET/CT), for example, no CT radiation dose and soft tissue images from MR acquired at the same time as the PET. However, obtaining accurate linear attenuation correction (LAC) factors for the lung remains difficult in PET/MRI. LACs depend on electron density and in the lung, these vary significantly both within an individual and from person to person. Current commercial practice is to use a single‐valued population‐based lung LAC, and better estimation is needed to improve quantification. Given the under‐appreciation of lung attenuation estimation as an issue, the inaccuracy of PET quantification due to the use of single‐valued lung LACs, the unique challenges of lung estimation, and the emerging status of PET/MRI scanners in lung disease, a review is timely. This paper highlights past and present methods, categorizing them into segmentation, atlas/mapping, and emission‐based schemes. Potential strategies for future developments are also presented.

What are the respiratory effects of e-cigarettes?

Electronic cigarettes (e-cigarettes) are alternative, non-combustible tobacco products that generate an inhalable aerosol containing nicotine, flavors, propylene glycol, and vegetable glycerin. Vaping is now a multibillion dollar industry that appeals to current smokers, former smokers, and young people who have never smoked. E-cigarettes reached the market without either extensive preclinical toxicology testing or long term safety trials that would be required of conventional therapeutics or medical devices. Their effectiveness as a smoking cessation intervention, their impact at a population level, and whether they are less harmful than combustible tobacco products are highly controversial. Here, we review the evidence on the effects of e-cigarettes on respiratory health. Studies show measurable adverse biologic effects on organ and cellular health in humans, in animals, and in vitro. The effects of e-cigarettes have similarities to and important differences from those of cigarettes. Decades of chronic smoking are needed for development of lung diseases such as lung cancer or chronic obstructive pulmonary disease, so the population effects of e-cigarette use may not be apparent until the middle of this century. We conclude that current knowledge of these effects is insufficient to determine whether the respiratory health effects of e-cigarette are less than those of combustible tobacco products.

Prognostic significance of chronic respiratory symptoms in individuals with normal spirometry

Normal spirometry is often used to preclude airway disease in individuals with unspecific respiratory symptoms. We tested the hypothesis that chronic respiratory symptoms are associated with respiratory hospitalisations and death in individuals with normal spirometry without known airway disease.We included 108 246 randomly chosen individuals aged 20-100 years from a Danish population-based cohort study. Normal spirometry was defined as a pre-bronchodilator forced expiratory volume in 1 s/forced vital capacity ratio ≥0.70. Chronic respiratory symptoms included dyspnoea, chronic mucus hypersecretion, wheezing and cough. Individuals with known airway disease, i.e. chronic obstructive pulmonary disease and/or asthma, were excluded (n=10 291). We assessed risk of hospitalisations due to exacerbations of airway disease and pneumonia, and respiratory and all-cause mortality, from 2003 through 2018.52 999 individuals had normal spirometry without chronic respiratory symptoms and 30 890 individuals had normal spirometry with chronic respiratory symptoms. During follow-up, we observed 1037 hospitalisations with exacerbation of airway disease, 5743 hospitalisations with pneumonia and 8750 deaths, of which 463 were due to respiratory disease. Compared with individuals with normal spirometry without chronic respiratory symptoms, multivariable adjusted hazard ratios for individuals with normal spirometry with chronic respiratory symptoms were 1.62 (95% CI 1.20-2.18) for exacerbation hospitalisations, 1.26 (95% CI 1.17-1.37) for pneumonia hospitalisations, 1.59 (95% CI 1.22-2.06) for respiratory mortality and 1.19 (95% CI 1.13-1.25) for all-cause mortality. There was a positive dose-response relationship between number of symptoms and risk of outcomes. Results were similar after 2 years of follow-up, for never-smokers alone, and for each symptom separately.Chronic respiratory symptoms are associated with respiratory hospitalisations and death in individuals with normal spirometry without known airway disease.

A Comparison between Two Pathophysiologically Different yet Microbiologically Similar Lung Diseases: Cystic Fibrosis and Chronic Obstructive Pulmonary Disease

Cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) are chronic pulmonary diseases that affect ~70,000 and 251 million individuals worldwide, respectively. Although these two diseases have distinctly different pathophysiologies, both cause chronic respiratory insufficiency that erodes quality of life and causes significant morbidity and eventually death. In both CF and COPD, the respiratory microbiome plays a major contributing role in disease progression and morbidity. Pulmonary pathogens can differ dramatically during various stages of each disease and frequently cause acute worsening of lung function due to disease exacerbation. Despite some similarities, outcome and timing/type of exacerbation can also be quite different between CF and COPD. Given these clinical distinctions, both patients and physicians should be aware of emerging therapeutic options currently being offered or in development for the treatment of lung infections in individuals with CF and COPD. Although interventions are available that prolong life and mitigate morbidity, neither disorder is curable. Both acute and chronic pulmonary infections contribute to an inexorable downward course and may trigger exacerbations, culminating in loss of lung function or respiratory failure. Knowledge of the pulmonary pathogens causing these infections, their clinical presentation, consequences, and management are, therefore, critical. In this review, we compare and contrast CF and COPD, including underlying causes, general outcomes, features of the lung microbiome, and potential treatment strategies.

A framework for Fourier-decomposition free-breathing pulmonary 1 H MRI ventilation measurements

PURPOSE

To develop a rapid Fourier decomposition (FD) free-breathing pulmonary ¹ H MRI (FDMRI) image processing and biomarker pipeline for research use.

METHODS

We acquired MRI in 20 asthmatic subjects using a balanced steady-state free precession (bSSFP) sequence optimized for ventilation imaging. 2D ¹ H MRI series were segmented by enforcing the spatial similarity between adjacent images and the right-to-left lung volume-ratio. The segmented lung series were co-registered using a coarse-to-fine deformable registration framework that used dual optimization techniques. All pairwise registrations were implemented in parallel and FD was performed to generate 2D ventilation-weighted maps and ventilation-defect-percent (VDP). Lung segmentation and registration accuracy were evaluated by comparing algorithm and manual lung-masks, deformed manual lung-masks, and fiducials in the moving and fixed images using Dice-similarity-coefficient (DSC), mean-absolute-distance (MAD), and target-registration-error (TRE). The relationship of FD-VDP and ³ He-VDP was evaluated using the Pearson-correlation-coefficient (r) and Bland Altman analysis. Algorithm reproducibility was evaluated using the coefficient-of-variation (CoV) and intra-class-correlation-coefficient (ICC) for segmentation, registration, and FD-VDP components.

RESULTS

For lung segmentation, there was a DSC of 95 ± 1.5% and MAD of 2.3 ± 0.5 mm, and for registration there was a DSC of 97 ± 0.8%, MAD of 1.6 ± 0.4 mm and TRE of 3.6 ± 1.2 mm. Reproducibility for segmentation DSC (CoV/ICC = 0.5%/0.92), registration TRE (CoV/ICC = 0.4%/0.98), and FD-VDP (Cov/ICC = 3.9%/0.97) was high. The pipeline required 10 min/subject. FD-VDP was correlated with ³ He-VDP (r = 0.69, P < 0.001) although there was a bias toward lower FD-VDP (bias = -4.9%).

CONCLUSIONS

We developed and evaluated a pipeline that provides a rapid and precise method for FDMRI ventilation maps.

CT Imaging-Based Low-Attenuation Super Clusters in Three Dimensions and the Progression of Emphysema

BACKGROUND

Distributions of low-attenuation areas in two-dimensional (2-D) CT lung slices are used to quantify parenchymal destruction in patients with COPD. However, these segmental approaches are limited and may not reflect the true three-dimensional (3-D) tissue processes that drive emphysematous changes in the lung. The goal of this study was to instead evaluate distributions of 3-D low-attenuation volumes, which we hypothesized would follow a power law distribution and provide a more complete assessment of the mechanisms underlying disease progression.

METHODS

CT scans and pulmonary function test results were acquired from an observational database for N = 12 patients with COPD and N = 12 control patients. The data set included baseline and two annual follow-up evaluations in patients with COPD. Three-dimensional representations of the lungs were reconstructed from 2-D axial CT slices, with low-attenuation volumes identified as contiguous voxels < -960 Hounsfield units.

RESULTS

Low-attenuation sizes generally followed a power law distribution, with the exception of large, individual outliers termed "super clusters," which deviated from the expected distribution. Super cluster volume was correlated with disease severity (% total low attenuation, ρ = 0.950) and clinical measures of lung function including FEV₁ (ρ = -0.849) and diffusing capacity of the lung for carbon monoxide Dlco (ρ = -0.874). To interpret these results, we developed a personalized computational model of super cluster emergence. Simulations indicated disease progression was more likely to occur near existing emphysematous regions, giving rise to a biomechanical, force-induced mechanism of super cluster growth.

CONCLUSIONS

Low-attenuation super clusters are defining, quantitative features of parenchymal destruction that dominate disease progression, particularly in advanced COPD.

Chronic Obstructive Pulmonary Disease Molecular Subtyping and Pathway Deviation-Based Candidate Gene Identification

OBJECTIVE

The aim of this study was to identify the molecular subtypes of chronic obstructive pulmonary disease (COPD) and to prioritize COPD candidate genes using bioinformatics methods.

MATERIALS AND METHODS

In this bioinformatics study, the gene expression dataset GSE76705 (including 229 COPD samples) and known COPD-related genes (candidate genes) were downloaded from the Gene Expression Omnibus (GEO) and the Online Mendelian Inheritance in Man (OMIM) databases respectively. Based on the expression values of the candidate genes, COPD samples were divided into molecular subtypes through hierarchical clustering analysis. Candidate genes were accordingly allocated into the defined molecular subtypes and functional enrichment analysis was undertaken. Pathway deviation scores were then analyzed, followed by the analysis of clinical indicators (FEV1, FEV1/FVC, age and gender) of COPD patients in each subtype, and prediction models were constructed. Furthermore, the gene expression dataset GSE71220 was used to bioinformatically validate our results.

RESULTS

A total of 213 COPD-related genes were identified, which divided samples into three subtypes based on the gene expression values. After intersection analysis, 160 common genes including transforming growth factor β1 (TGFB1), epidermal growth factor receptor (EGFR) and interleukin 13 (IL13) were obtained. Functional enrichment analysis identified 22 pathways such as 'hsa04060: cytokine-cytokine receptor interaction pathways, 'hsa04110: cell cycle' and 'hsa05222: small cell lung cancer'. Pathways in subtype 2 had higher deviation scores. Furthermore, three receiver operating characteristic (ROC) curves (accuracies >80%) were constructed. The three subtypes in COPD samples were also identified in the validation dataset GSE71220.

CONCLUSION

COPD may be further subdivided into several molecular subtypes, which may be useful in improving COPD therapy based on the molecular subtype of a patient.

Imaging Features of Chronic Bronchitis with Preserved Ratio and Impaired Spirometry (PRISm)

PURPOSE

The purpose of the study was to investigate the quantitative chest tomographic features of chronic bronchitis with preserved ratio and impaired spirometry (PRISm), including airway wall area, emphysema index, and lung capacity.

METHODS

An observational, cross-sectional study of 343 patients at the Ninth Hospital of Xi'an Affiliated Hospital of Xi'an Jiaotong University between October 2014 and September 2017. The patients were divided into three groups: 77 cases of chronic bronchitis with normal lung function (forced expiratory volume in one second/forced vital capacity) (FEV1/FVC > 70%, FEV1%pred > 80%), 80 cases of chronic bronchitis with PRISm (FEV1/FVC > 70%, FEV1%pred < 80%), and 186 cases of the early chronic obstructive pulmonary disease (COPD) (FEV1/FVC < 70%, FEV1%pred > 50%, that is, Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade 1 + 2). We compared and analyzed the differences in imaging between the chronic bronchitis with PRISm and the other two groups.

RESULTS

Compared with the early COPD group, the PRISm group revealed significant differences in airway wall area, emphysema index, and lung capacity (P < 0.05). Compared with the chronic bronchitis with normal lung function group, the PRISm group showed increased WA%_LUL5, decreased lung capacity, and higher mean lung density.

CONCLUSION

In terms of airway wall area and emphysema index, patients with chronic bronchitis with PRISm were essentially no different than those with chronic bronchitis without abnormal spirometry, whereas for symptoms, they are more like GOLD 1 and 2 patients. Our findings show that it is not yet clear whether it constitutes an intermediate stage of chronic bronchitis with normal lung function that progression to early COPD.

Complementary regional heterogeneity information from COPD patients obtained using oxygen-enhanced MRI and chest CT

Background

The heterogeneous distribution of emphysema is a key feature of chronic obstructive pulmonary disease (COPD) patients that typically is evaluated using high-resolution chest computed tomography (HRCT). Oxygen-enhanced pulmonary magnetic resonance imaging (OEMRI) is a new method to obtain information regarding regional ventilation, diffusion, and perfusion in the lung without radiation exposure. We aimed to compare OEMRI with HRCT for the assessment of heterogeneity in COPD patients.

Methods

Forty patients with stable COPD underwent quantitative HRCT, OEMRI, and pulmonary function tests, including arterial blood gas analysis. OEMRI was also performed on nine healthy control subjects. We measured the severity of emphysema (percent low attenuation volume; LAV%) in whole lungs and the standard deviations (SDs) of the LAV% values of 10 isovolumetric partitions (SD-LAV) as an index of cranial-caudal heterogeneity. Similarly, relative enhancement ratios of oxygen (RERs) in whole lungs from OEMRI and SD-RER were analyzed.

Results

COPD patients showed a lower mean RER than control subjects (12.6% vs 22.0%, p<0.01). The regional heterogeneity of the RERs was not always consistent with the LAV distribution. Both the HRCT (LAV% and SD-LAV) and the OEMRI (RER and SD-RER) indices were significantly associated with the diffusion capacity (DL_CO) and partial pressure of oxygen in arterial blood (PaO₂). The PaO₂ was associated only with the heterogeneity index of HRCT (SD-LAV) (R² = 0.39); however, the PaO₂ was associated with both the mean RER and heterogeneity (SD-RER) in the multivariate analysis (R² = 0.38).

Conclusions

OEMRI-derived parameters were directly associated with oxygen uptake in COPD patients. Although the OEMRI-derived parameters were not identical to the HRCT-derived parameters, the cranial-caudal heterogeneity in HRCT or OEMRI was complementary to that in evaluations of oxygen uptake in the lungs. Functional imaging seems to provide new insights into COPD pathophysiology without radiation exposure.

A novel CT-emphysema index/FEV1 approach of phenotyping COPD to predict mortality

Background

COPD-associated mortality was examined using a novel approach of phenotyping COPD based on computed tomography (CT)-emphysema index from quantitative CT (QCT) and post-bronchodilator (BD) forced expiratory volume in 1 second (FEV₁) in a local Malaysian cohort.

Patients and methods

Prospectively collected data of 112 eligible COPD subjects (mean age, 67 years; male, 93%; mean post-BD FEV₁, 45.7%) was available for mortality analysis. Median follow-up time was 1,000 days (range, 60–1,400). QCT and clinicodemographic data were collected at study entry. Based on CT-emphysema index and post-BD FEV₁% predicted, subjects were categorized into “emphysema-dominant,” “airway-dominant,” “mild mixed airway-emphysema,” and “severe mixed airway-emphysema” diseases.

Results

Sixteen patients (14.2%) died of COPD-associated causes. There were 29 (25.9%) “mild mixed,” 23 (20.5%) “airway-dominant,” 15 (13.4%) “emphysema-dominant,” and 45 (40.2%) “severe mixed” cases. “Mild mixed” disease was proportionately more in Global Initiative for Chronic Obstructive Lung Disease (GOLD) Group A, while “severe mixed” disease was proportionately more in GOLD Groups B and D. Kaplan–Meier survival estimates showed increased mortality risk with “severe mixed” disease (log rank test, p=0.03) but not with GOLD groups (p=0.08). Univariate Cox proportionate hazard analysis showed that age, body mass index, long-term oxygen therapy, FEV₁, forced volume capacity, COPD Assessment Test score, modified Medical Research Council score, St Georges’ Respiratory Questionnaire score, CT-emphysema index, and “severe mixed” disease (vs “mild mixed” disease) were associated with mortality. Multivariate Cox analysis showed that age, body mass index, and COPD Assessment Test score remain independently associated with mortality.

Conclusion

“Severe mixed airway-emphysema” disease may predict COPD-associated mortality. Age, body mass index, and COPD Assessment Test score remain as key mortality risk factors in our cohort.

Development of a pulmonary imaging biomarker pipeline for phenotyping of chronic lung disease

We designed and generated pulmonary imaging biomarker pipelines to facilitate high-throughput research and point-of-care use in patients with chronic lung disease. Image processing modules and algorithm pipelines were embedded within a graphical user interface (based on the .NET framework) for pulmonary magnetic resonance imaging (MRI) and x-ray computed-tomography (CT) datasets. The software pipelines were generated using C++ and included: (1) inhaled He3/Xe129 MRI ventilation and apparent diffusion coefficients, (2) CT-MRI coregistration for lobar and segmental ventilation and perfusion measurements, (3) ultrashort echo-time H1 MRI proton density measurements, (4) free-breathing Fourier-decomposition H1 MRI ventilation/perfusion and free-breathing H1 MRI specific ventilation, (5) multivolume CT and MRI parametric response maps, and (6) MRI and CT texture analysis and radiomics. The image analysis framework was implemented on a desktop workstation/tablet to generate biomarkers of regional lung structure and function related to ventilation, perfusion, lung tissue texture, and integrity as well as multiparametric measures of gas trapping and airspace enlargement. All biomarkers were generated within 10 min with measurement reproducibility consistent with clinical and research requirements. The resultant pulmonary imaging biomarker pipeline provides real-time and automated lung imaging measurements for point-of-care and high-throughput research.

Physiological and biological characterization of smokers with and without COPD

Chronic obstructive pulmonary disease (COPD) is a common inflammatory airway disease predominantly associated with cigarette smoking, and its incidence is increasing worldwide. According to the Global Initiative for Obstructive Lung Disease (GOLD) guidelines, spirometry is used to diagnose the disease. However, owing to its complexity, spirometry alone may not account for the multitude of COPD phenotypes or the early, asymptomatic lung damage seen in younger smokers. In addition, suitable biomarkers enabling early diagnosis, guiding treatment and estimating prognosis are still scarce, although large scale ‘omics analyses have added to the spectrum of potential biomarkers that could be used for these purposes.

The aim of the current study was to comprehensively profile patients with mild-to-moderate COPD and compare the profiles to i) a group of currently smoking asymptomatic subjects, ii) a group of healthy former smokers, and iii) a group of healthy subjects that had never smoked. The assessment was conducted at the molecular level using proteomics, transcriptomics, and lipidomics and complemented by a series of measurements of traditional and emerging indicators of lung health (ClinicalTrials.gov identifier: NCT01780298). In this data note, we provide a comprehensive description of the study population’s physiological characteristics including full lung function, lung appearance on chest computed tomography, impulse oscillometry, and exercise tolerance and quality of life (QoL) measures.

SPIROMICS Protocol for Multicenter Quantitative Computed Tomography to Phenotype the Lungs

Multidetector row computed tomography (MDCT) is increasingly taking a central role in identifying subphenotypes within chronic obstructive pulmonary disease (COPD), asthma, and other lung-related disease populations, allowing for the quantification of the amount and distribution of altered parenchyma along with the characterization of airway and vascular anatomy. The embedding of quantitative CT (QCT) into a multicenter trial with a variety of scanner makes and models along with the variety of pressures within a clinical radiology setting has proven challenging, especially in the context of a longitudinal study. SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study), sponsored by the National Institutes of Health, has established a QCT lung assessment system (QCT-LAS), which includes scanner-specific imaging protocols for lung assessment at total lung capacity and residual volume. Also included are monthly scanning of a standardized test object and web-based tools for subject registration, protocol assignment, and data transmission coupled with automated image interrogation to assure protocol adherence. The SPIROMICS QCT-LAS has been adopted and contributed to by a growing number of other multicenter studies in which imaging is embedded. The key components of the SPIROMICS QCT-LAS along with evidence of implementation success are described herein. While imaging technologies continue to evolve, the required components of a QCT-LAS provide the framework for future studies, and the QCT results emanating from SPIROMICS and the growing number of other studies using the SPIROMICS QCT-LAS will provide a shared resource of image-derived pulmonary metrics.

Lung Topology Characteristics in patients with Chronic Obstructive Pulmonary Disease

Quantitative features that can currently be obtained from medical imaging do not provide a complete picture of Chronic Obstructive Pulmonary Disease (COPD). In this paper, we introduce a novel analytical tool based on persistent homology that extracts quantitative features from chest CT scans to describe the geometric structure of the airways inside the lungs. We show that these new radiomic features stratify COPD patients in agreement with the GOLD guidelines for COPD and can distinguish between inspiratory and expiratory scans. These CT measurements are very different to those currently in use and we demonstrate that they convey significant medical information. The results of this study are a proof of concept that topological methods can enhance the standard methodology to create a finer classification of COPD and increase the possibilities of more personalized treatment.

Heterogeneity in pulmonary emphysema: Analysis of CT attenuation using Gaussian mixture model

PURPOSE

To utilize Gaussian mixture model (GMM) for the quantification of chronic obstructive pulmonary disease (COPD) and to evaluate the combined use of multiple types of quantification.

MATERIALS AND METHODS

Eighty-seven patients (67 men, 20 women; age, 67.4 ± 11.0 years) who had undergone computed tomography (CT) and pulmonary function test (PFT) were included. The heterogeneity of CT attenuation in emphysema (HC) was obtained by analyzing a distribution of CT attenuation with GMM. The percentages of low-attenuation volume in the lungs (LAV), wall area of bronchi (WA), and the cross-sectional area of small pulmonary vessels (CSA) were also calculated. The relationships between COPD quantifications and the PFT results were evaluated by Pearson's correlation coefficients and through linear models, with the best models selected using Akaike information criterion (AIC).

RESULTS

The correlation coefficients with FEV1 were as follows: LAV, -0.505; HC, -0.277; CSA, 0.384; WA, -0.196. The correlation coefficients with FEV1/FVC were: LAV, -0.640; HC, -0.136; CSA, 0.288; WA, -0.131. For predicting FEV1, the smallest AIC values were obtained in the model with LAV, HC, CSA, and WA. For predicting FEV1/FVC, the smallest AIC values were obtained in the model with LAV and HC. In both models, the coefficient of HC was statistically significant (P-values = 0.000880 and 0.0441 for FEV1 and FEV1/FVC, respectively).

CONCLUSION

GMM was applied to COPD quantification. The results of this study show that COPD severity was associated with HC. In addition, it is shown that the combined use of multiple types of quantification made the evaluation of COPD severity more reliable.

Pulmonary quantitative CT imaging in focal and diffuse disease: current research and clinical applications

The frenetic development of imaging technology-both hardware and software-provides exceptional potential for investigation of the lung. In the last two decades, CT was exploited for detailed characterization of pulmonary structures and description of respiratory disease. The introduction of volumetric acquisition allowed increasingly sophisticated analysis of CT data by means of computerized algorithm, namely quantitative CT (QCT). Hundreds of thousands of CTs have been analysed for characterization of focal and diffuse disease of the lung. Several QCT metrics were developed and tested against clinical, functional and prognostic descriptors. Computer-aided detection of nodules, textural analysis of focal lesions, densitometric analysis and airway segmentation in obstructive pulmonary disease and textural analysis in interstitial lung disease are the major chapters of this discipline. The validation of QCT metrics for specific clinical and investigational needs prompted the translation of such metrics from research field to patient care. The present review summarizes the state of the art of QCT in both focal and diffuse lung disease, including a dedicated discussion about application of QCT metrics as parameters for clinical care and outcomes in clinical trials.

The Role of Computed Tomography for the Evaluation of Lung Disease in Alpha-1 Antitrypsin Deficiency

Alpha-1 antitrypsin deficiency (AATD) is characterized by low serum levels of or dysfunctional alpha-1 proteinase inhibitor. In the lung parenchyma, this results in a loss of protection against the activity of serine proteases, particularly neutrophil elastase. The resultant imbalance in protease and antiprotease activity leads to an increased risk for the development of early-onset emphysema and COPD. As in traditional smoke-related COPD, the assessment of the severity and disease progression of lung disease in AATD is conventionally based on lung function; however, pulmonary function tests are unable to discriminate between emphysema and airways disease, the two hallmark pathologic features of COPD. CT imaging has been used as a tool to further characterize lung structure and evaluate therapeutic interventions in AATD-related COPD. Moreover, recent advances in quantitative CT have significantly improved our assessment of the lung architecture, which has provided investigators and clinicians with a more detailed evaluation of the extent and severity of emphysema and airways disease in AATD. In addition, serial CT imaging measures are becoming increasingly important, as they provide a tool to monitor emphysema progression. This review describes the principles of CT technology and the role of CT imaging in assessing pulmonary disease progression in AATD, including the effect of therapeutic interventions.

Disease Severity Dependence of the Longitudinal Association Between CT Lung Density and Lung Function in Smokers

BACKGROUND

In smokers, the lung parenchyma is characterized by inflammation and emphysema, processes that can result in local gain and loss of lung tissue. CT measures of lung density might reflect lung tissue changes; however, longitudinal data regarding the effects of CT lung tissue on FEV₁ in smokers with and without COPD are scarce.

METHODS

The 15th percentile of CT lung density was obtained from the scans of 3,390 smokers who completed baseline and 5-year follow-up of the Genetic Epidemiology of COPD (COPDGene) study visits. The longitudinal relationship between total lung capacity-adjusted lung density (TLC-PD15) and FEV₁ was assessed by using multivariable mixed models. Separate models were performed in smokers at risk, smokers with preserved ratio and impaired spirometry (PRISm), and smokers with COPD according to the Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging system.

RESULTS

The direction of the relationship between lung density and lung function was GOLD stage dependent. In smokers with PRISm, a 1-g/L decrease in TLC-PD15 was associated with an increase of 2.8 mL FEV₁ (P = .02). In contrast, among smokers with GOLD III to IV COPD, a 1-g/L decrease in TLC-PD15 was associated with a decrease of 4.1 mL FEV₁ (P = .002).

CONCLUSIONS

A decline in TLC-PD15 was associated with an increase or decrease in FEV₁ depending on disease severity. The associations are GOLD stage specific, and their presence might influence the interpretation of future studies that use CT lung density as an intermediate study end point for a decline in lung function.

TRIAL REGISTRY

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

Lung Mass in Smokers

RATIONALE AND OBJECTIVE

Emphysema is characterized by airspace dilation, inflammation, and irregular deposition of elastin and collagen in the interstitium. Computed tomographic studies have reported that lung mass (LM) may be increased in smokers, a finding attributed to inflammatory and parenchymal remodeling processes observed on histopathology. We sought to examine the epidemiologic and clinical associations of LM in smokers.

MATERIALS AND METHODS

Baseline epidemiologic, clinical, and computed tomography (CT) data (n = 8156) from smokers enrolled into the COPDGene Study were analyzed. LM was calculated from the CT scan. Changes in lung function at 5 years' follow-up were available from 1623 subjects. Regression analysis was performed to assess for associations of LM with forced expiratory volume in 1 second (FEV₁) and FEV₁ decline.

RESULTS

Subjects with Global Initiative for Chronic Obstructive Lung Disease (GOLD) 1 chronic obstructive pulmonary disease had greater LM than either smokers with normal lung function or those with GOLD 2-4 chronic obstructive pulmonary disease (P < 0.001 for both comparisons). LM was predictive of the rate of the decline in FEV₁ (decline per 100 g, -4.7 ± 1.7 mL/y, P = 0.006).

CONCLUSIONS

Our cross-sectional data suggest the presence of a biphasic radiological remodeling process in smokers: the presence of such nonlinearity must be accounted for in longitudinal computed tomographic studies. Baseline LM predicts the decline in lung function.

Anatomical pulmonary magnetic resonance imaging segmentation for regional structure-function measurements of asthma

PURPOSE

Pulmonary magnetic-resonance-imaging (MRI) and x-ray computed-tomography have provided strong evidence of spatially and temporally persistent lung structure-function abnormalities in asthmatics. This has generated a shift in their understanding of lung disease and supports the use of imaging biomarkers as intermediate endpoints of asthma severity and control. In particular, pulmonary (1)H MRI can be used to provide quantitative lung structure-function measurements longitudinally and in response to treatment. However, to translate such biomarkers of asthma, robust methods are required to segment the lung from pulmonary (1)H MRI. Therefore, their objective was to develop a pulmonary (1)H MRI segmentation algorithm to provide regional measurements with the precision and speed required to support clinical studies.

METHODS

The authors developed a method to segment the left and right lung from (1)H MRI acquired in 20 asthmatics including five well-controlled and 15 severe poorly controlled participants who provided written informed consent to a study protocol approved by Health Canada. Same-day spirometry and plethysmography measurements of lung function and volume were acquired as well as (1)H MRI using a whole-body radiofrequency coil and fast spoiled gradient-recalled echo sequence at a fixed lung volume (functional residual capacity + 1 l). We incorporated the left-to-right lung volume proportion prior based on the Potts model and derived a volume-proportion preserved Potts model, which was approximated through convex relaxation and further represented by a dual volume-proportion preserved max-flow model. The max-flow model led to a linear problem with convex and linear equality constraints that implicitly encoded the proportion prior. To implement the algorithm, (1)H MRI was resampled into ∼3 × 3 × 3 mm(3) isotropic voxel space. Two observers placed seeds on each lung and on the background of 20 pulmonary (1)H MR images in a randomized dataset, on five occasions, five consecutive days in a row. Segmentation accuracy was evaluated using the Dice-similarity-coefficient (DSC) of the segmented thoracic cavity with comparison to five-rounds of manual segmentation by an expert observer. The authors also evaluated the root-mean-squared-error (RMSE) of the Euclidean distance between lung surfaces, the absolute, and percent volume error. Reproducibility was measured using the coefficient of variation (CoV) and intraclass correlation coefficient (ICC) for two observers who repeated segmentation measurements five-times.

RESULTS

For five well-controlled asthmatics, forced expiratory volume in 1 s (FEV1)/forced vital capacity (FVC) was 83% ± 7% and FEV1 was 86 ± 9%pred. For 15 severe, poorly controlled asthmatics, FEV1/FV C = 66% ± 17% and FEV1 = 72 ± 27%pred. The DSC for algorithm and manual segmentation was 91% ± 3%, 92% ± 2% and 91% ± 2% for the left, right, and whole lung, respectively. RMSE was 4.0 ± 1.0 mm for each of the left, right, and whole lung. The absolute (percent) volume errors were 0.1 l (∼6%) for each of right and left lung and ∼0.2 l (∼6%) for whole lung. Intra- and inter-CoV (ICC) were <0.5% (>0.91%) for DSC and <4.5% (>0.93%) for RMSE. While segmentation required 10 s including ∼6 s for user interaction, the smallest detectable difference was 0.24 l for algorithm measurements which was similar to manual measurements.

CONCLUSIONS

This lung segmentation approach provided the necessary and sufficient precision and accuracy required for research and clinical studies.

Standardizing CT lung density measure across scanner manufacturers

PURPOSE

Computed Tomography (CT) imaging of the lung, reported in Hounsfield Units (HU), can be parameterized as a quantitative image biomarker for the diagnosis and monitoring of lung density changes due to emphysema, a type of chronic obstructive pulmonary disease (COPD). CT lung density metrics are global measurements based on lung CT number histograms, and are typically a quantity specifying either the percentage of voxels with CT numbers below a threshold, or a single CT number below which a fixed relative lung volume, nth percentile, falls. To reduce variability in the density metrics specified by CT attenuation, the Quantitative Imaging Biomarkers Alliance (QIBA) Lung Density Committee has organized efforts to conduct phantom studies in a variety of scanner models to establish a baseline for assessing the variations in patient studies that can be attributed to scanner calibration and measurement uncertainty.

METHODS

Data were obtained from a phantom study on CT scanners from four manufacturers with several protocols at various tube potential voltage (kVp) and exposure settings. Free from biological variation, these phantom studies provide an assessment of the accuracy and precision of the density metrics across platforms solely due to machine calibration and uncertainty of the reference materials. The phantom used in this study has three foam density references in the lung density region, which, after calibration against a suite of Standard Reference Materials (SRM) foams with certified physical density, establishes a HU-electron density relationship for each machine-protocol. We devised a 5-step calibration procedure combined with a simplified physical model that enabled the standardization of the CT numbers reported across a total of 22 scanner-protocol settings to a single energy (chosen at 80 keV). A standard deviation was calculated for overall CT numbers for each density, as well as by scanner and other variables, as a measure of the variability, before and after the standardization. In addition, a linear mixed-effects model was used to assess the heterogeneity across scanners, and the 95% confidence interval of the mean CT number was evaluated before and after the standardization.

RESULTS

We show that after applying the standardization procedures to the phantom data, the instrumental reproducibility of the CT density measurement of the reference foams improved by more than 65%, as measured by the standard deviation of the overall mean CT number. Using the lung foam that did not participate in the calibration as a test case, a mixed effects model analysis shows that the 95% confidence intervals are [-862.0 HU, -851.3 HU] before standardization, and [-859.0 HU, -853.7 HU] after standardization to 80 keV. This is in general agreement with the expected CT number value at 80 keV of -855.9 HU with 95% CI of [-857.4 HU, -854.5 HU] based on the calibration and the uncertainty in the SRM certified density.

CONCLUSIONS

This study provides a quantitative assessment of the variations expected in CT lung density measures attributed to non-biological sources such as scanner calibration and scanner x-ray spectrum and filtration. By removing scanner-protocol dependence from the measured CT numbers, higher accuracy and reproducibility of quantitative CT measures were attainable. The standardization procedures developed in study may be explored for possible application in CT lung density clinical data.