Parametric response mapping monitors temporal changes on lung CT scans in the subpopulations and intermediate outcome measures in COPD Study (SPIROMICS)

Capturing COPD heterogeneity: anomaly detection and parametric response mapping comparison for phenotyping on chest computed tomography

Background

Chronic obstructive pulmonary disease (COPD) poses a substantial global health burden, demanding advanced diagnostic tools for early detection and accurate phenotyping. In this line, this study seeks to enhance COPD characterization on chest computed tomography (CT) by comparing the spatial and quantitative relationships between traditional parametric response mapping (PRM) and a novel self-supervised anomaly detection approach, and to unveil potential additional insights into the dynamic transitional stages of COPD.

Methods

Non-contrast inspiratory and expiratory CT of 1,310 never-smoker and GOLD 0 individuals and COPD patients (GOLD 1-4) from the COPDGene dataset were retrospectively evaluated. A novel self-supervised anomaly detection approach was applied to quantify lung abnormalities associated with COPD, as regional deviations. These regional anomaly scores were qualitatively and quantitatively compared, per GOLD class, to PRM volumes (emphysema: PRMEmph, functional small-airway disease: PRMfSAD) and to a Principal Component Analysis (PCA) and Clustering, applied on the self-supervised latent space. Its relationships to pulmonary function tests (PFTs) were also evaluated.

Results

Initial t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization of the self-supervised latent space highlighted distinct spatial patterns, revealing clear separations between regions with and without emphysema and air trapping. Four stable clusters were identified among this latent space by the PCA and Cluster Analysis. As the GOLD stage increased, PRMEmph, PRMfSAD, anomaly score, and Cluster 3 volumes exhibited escalating trends, contrasting with a decline in Cluster 2. The patient-wise anomaly scores significantly differed across GOLD stages (p < 0.01), except for never-smokers and GOLD 0 patients. In contrast, PRMEmph, PRMfSAD, and cluster classes showed fewer significant differences. Pearson correlation coefficients revealed moderate anomaly score correlations to PFTs (0.41-0.68), except for the functional residual capacity and smoking duration. The anomaly score was correlated with PRMEmph (r = 0.66, p < 0.01) and PRMfSAD (r = 0.61, p < 0.01). Anomaly scores significantly improved fitting of PRM-adjusted multivariate models for predicting clinical parameters (p < 0.001). Bland-Altman plots revealed that volume agreement between PRM-derived volumes and clusters was not constant across the range of measurements.

Conclusion

Our study highlights the synergistic utility of the anomaly detection approach and traditional PRM in capturing the nuanced heterogeneity of COPD. The observed disparities in spatial patterns, cluster dynamics, and correlations with PFTs underscore the distinct - yet complementary - strengths of these methods. Integrating anomaly detection and PRM offers a promising avenue for understanding of COPD pathophysiology, potentially informing more tailored diagnostic and intervention approaches to improve patient outcomes.

Local heterogeneity of normal lung parenchyma and small airways disease are associated with COPD severity and progression

BACKGROUND

Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline.

METHODS

PRM metrics of normal lung (PRMNorm) and functional SAD (PRMfSAD) were generated from CT scans collected as part of the COPDGene study (n = 8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRMNorm and PRMfSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV1 decline using a machine learning model.

RESULTS

Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRMfSAD and PRMNorm were independently associated with the amount of emphysema. Readouts χfSAD (β of 0.106, p < 0.001) and VfSAD (β of 0.065, p = 0.004) were also independently associated with FEV1% predicted. The machine learning model using PRM topologies as inputs predicted FEV1 decline over five years with an AUC of 0.69.

CONCLUSIONS

We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRMfSAD and PRMNorm may show promise as an early indicator of emphysema onset and COPD progression.

Morphometric analysis of airways in pre-COPD and mild COPD lungs using continuous surface representations of the bronchial lumen

Introduction: Chronic Obstructive Pulmonary Disease (COPD) is a prevalent respiratory disease that presents a high rate of underdiagnosis during onset and early stages. Studies have shown that in mild COPD patients, remodeling of the small airways occurs concurrently with morphological changes in the proximal airways. Despite this evidence, the geometrical study of the airway tree from computed tomography (CT) lung images remains underexplored due to poor representations and limited tools to characterize the airway structure. Methods: We perform a comprehensive morphometric study of the proximal airways based on geometrical measures associated with the different airway generations. To this end, we leverage the geometric flexibility of the Snakes IsoGeometric Analysis method to accurately represent and characterize the airway luminal surface and volume informed by CT images of the respiratory tree. Based on this framework, we study the airway geometry of smoking pre-COPD and mild COPD individuals. Results: Our results show a significant difference between groups in airway volume, length, luminal eccentricity, minimum radius, and surface-area-to-volume ratio in the most distal airways. Discussion: Our findings suggest a higher degree of airway narrowing and collapse in COPD patients when compared to pre-COPD patients. We envision that our work has the potential to deliver a comprehensive tool for assessing morphological changes in airway geometry that take place in the early stages of COPD.

Deep learning parametric response mapping from inspiratory chest CT scans: a new approach for small airway disease screening

OBJECTIVES

Parametric response mapping (PRM) enables the evaluation of small airway disease (SAD) at the voxel level, but requires both inspiratory and expiratory chest CT scans. We hypothesize that deep learning PRM from inspiratory chest CT scans can effectively evaluate SAD in individuals with normal spirometry.

METHODS

We included 537 participants with normal spirometry, a history of smoking or secondhand smoke exposure, and divided them into training, tuning, and test sets. A cascaded generative adversarial network generated expiratory CT from inspiratory CT, followed by a UNet-like network predicting PRM using real inspiratory CT and generated expiratory CT. The performance of the prediction is evaluated using SSIM, RMSE and dice coefficients. Pearson correlation evaluated the correlation between predicted and ground truth PRM. ROC curves evaluated predicted PRMfSAD (the volume percentage of functional small airway disease, fSAD) performance in stratifying SAD.

RESULTS

Our method can generate expiratory CT of good quality (SSIM 0.86, RMSE 80.13 HU). The predicted PRM dice coefficients for normal lung, emphysema, and fSAD regions are 0.85, 0.63, and 0.51, respectively. The volume percentages of emphysema and fSAD showed good correlation between predicted and ground truth PRM (|r| were 0.97 and 0.64, respectively, p < 0.05). Predicted PRMfSAD showed good SAD stratification performance with ground truth PRMfSAD at thresholds of 15%, 20% and 25% (AUCs were 0.84, 0.78, and 0.84, respectively, p < 0.001).

CONCLUSION

Our deep learning method generates high-quality PRM using inspiratory chest CT and effectively stratifies SAD in individuals with normal spirometry.

Quantitative CT of Normal Lung Parenchyma and Small Airways Disease Topologies are Associated With COPD Severity and Progression

Objectives

Small airways disease (SAD) is a major cause of airflow obstruction in COPD patients, and has been identified as a precursor to emphysema. Although the amount of SAD in the lungs can be quantified using our Parametric Response Mapping (PRM) approach, the full breadth of this readout as a measure of emphysema and COPD progression has yet to be explored. We evaluated topological features of PRM-derived normal parenchyma and SAD as surrogates of emphysema and predictors of spirometric decline.

Materials and Methods

PRM metrics of normal lung (PRMNorm) and functional SAD (PRMfSAD) were generated from CT scans collected as part of the COPDGene study (n=8956). Volume density (V) and Euler-Poincaré Characteristic (χ) image maps, measures of the extent and coalescence of pocket formations (i.e., topologies), respectively, were determined for both PRMNorm and PRMfSAD. Association with COPD severity, emphysema, and spirometric measures were assessed via multivariable regression models. Readouts were evaluated as inputs for predicting FEV1 decline using a machine learning model.

Results

Multivariable cross-sectional analysis of COPD subjects showed that V and χ measures for PRMfSAD and PRMNorm were independently associated with the amount of emphysema. Readouts χfSAD (β of 0.106, p<0.001) and VfSAD (β of 0.065, p=0.004) were also independently associated with FEV1% predicted. The machine learning model using PRM topologies as inputs predicted FEV1 decline over five years with an AUC of 0.69.

Conclusions

We demonstrated that V and χ of fSAD and Norm have independent value when associated with lung function and emphysema. In addition, we demonstrated that these readouts are predictive of spirometric decline when used as inputs in a ML model. Our topological PRM approach using PRMfSAD and PRMNorm may show promise as an early indicator of emphysema onset and COPD progression.

An analysis of the regional heterogeneity in tissue elasticity in lung cancer patients with COPD

Purpose

Recent advancements in obtaining image-based biomarkers from CT images have enabled lung function characterization, which could aid in lung interventional planning. However, the regional heterogeneity in these biomarkers has not been well documented, yet it is critical to several procedures for lung cancer and COPD. The purpose of this paper is to analyze the interlobar and intralobar heterogeneity of tissue elasticity and study their relationship with COPD severity.

Methods

We retrospectively analyzed a set of 23 lung cancer patients for this study, 14 of whom had COPD. For each patient, we employed a 5DCT scanning protocol to obtain end-exhalation and end-inhalation images and semi-automatically segmented the lobes. We calculated tissue elasticity using a biomechanical property estimation model. To obtain a measure of lobar elasticity, we calculated the mean of the voxel-wise elasticity values within each lobe. To analyze interlobar heterogeneity, we defined an index that represented the properties of the least elastic lobe as compared to the rest of the lobes, termed the Elasticity Heterogeneity Index (EHI). An index of 0 indicated total homogeneity, and higher indices indicated higher heterogeneity. Additionally, we measured intralobar heterogeneity by calculating the coefficient of variation of elasticity within each lobe.

Results

The mean EHI was 0.223 ± 0.183. The mean coefficient of variation of the elasticity distributions was 51.1% ± 16.6%. For mild COPD patients, the interlobar heterogeneity was low compared to the other categories. For moderate-to-severe COPD patients, the interlobar and intralobar heterogeneities were highest, showing significant differences from the other groups.

Conclusion

We observed a high level of lung tissue heterogeneity to occur between and within the lobes in all COPD severity cases, especially in moderate-to-severe cases. Heterogeneity results demonstrate the value of a regional, function-guided approach like elasticity for procedures such as surgical decision making and treatment planning.

Lung functional imaging

Pulmonary functional imaging modalities such as computed tomography, magnetic resonance imaging and nuclear imaging can quantitatively assess regional lung functional parameters and their distributions. These include ventilation, perfusion, gas exchange at the microvascular level and biomechanical properties, among other variables. This review describes the rationale, strengths and limitations of the various imaging modalities employed for lung functional imaging. It also aims to explain some of the most commonly measured parameters of regional lung function. A brief review of evidence on the role and utility of lung functional imaging in early diagnosis, accurate lung functional characterisation, disease phenotyping and advancing the understanding of disease mechanisms in major respiratory disorders is provided.

Direct estimation of regional lung volume change from paired and single CT images using residual regression neural network

BACKGROUND

Chest computed tomography (CT) enables characterization of pulmonary diseases by producing high-resolution and high-contrast images of the intricate lung structures. Deformable image registration is used to align chest CT scans at different lung volumes, yielding estimates of local tissue expansion and contraction.

PURPOSE

We investigated the utility of deep generative models for directly predicting local tissue volume change from lung CT images, bypassing computationally expensive iterative image registration and providing a method that can be utilized in scenarios where either one or two CT scans are available.

METHODS

A residual regression convolutional neural network, called Reg3DNet+, is proposed for directly regressing high-resolution images of local tissue volume change (i.e., Jacobian) from CT images. Image registration was performed between lung volumes at total lung capacity (TLC) and functional residual capacity (FRC) using a tissue mass- and structure-preserving registration algorithm. The Jacobian image was calculated from the registration-derived displacement field and used as the ground truth for local tissue volume change. Four separate Reg3DNet+ models were trained to predict Jacobian images using a multifactorial study design to compare the effects of network input (i.e., single image vs. paired images) and output space (i.e., FRC vs. TLC). The models were trained and evaluated on image datasets from the COPDGene study. Models were evaluated against the registration-derived Jacobian images using local, regional, and global evaluation metrics.

RESULTS

Statistical analysis revealed that both factors - network input and output space - were significant determinants for change in evaluation metrics. Paired-input models performed better than single-input models, and model performance was better in the output space of FRC rather than TLC. Mean structural similarity index for paired-input models was 0.959 and 0.956 for FRC and TLC output spaces, respectively, and for single-input models was 0.951 and 0.937. Global evaluation metrics demonstrated correlation between registration-derived Jacobian mean and predicted Jacobian mean: coefficient of determination (r2 ) for paired-input models was 0.974 and 0.938 for FRC and TLC output spaces, respectively, and for single-input models was 0.598 and 0.346. After correcting for effort, registration-derived lobar volume change was strongly correlated with the predicted lobar volume change: for paired-input models r2 was 0.899 for both FRC and TLC output spaces, and for single-input models r2 was 0.803 and 0.862, respectively.

CONCLUSIONS

Convolutional neural networks can be used to directly predict local tissue mechanics, eliminating the need for computationally expensive image registration. Networks that use paired CT images acquired at TLC and FRC allow for more accurate prediction of local tissue expansion compared to networks that use a single image. Networks that only require a single input image still show promising results, particularly after correcting for effort, and allow for local tissue expansion estimation in cases where multiple CT scans are not available. For single-input networks, the FRC image is more predictive of local tissue volume change compared to the TLC image.

A Single-Cell Atlas of Small Airway Disease in Chronic Obstructive Pulmonary Disease: A Cross-Sectional Study

Rationale: Emerging data demonstrate that the smallest conducting airways, terminal bronchioles, are the early site of tissue destruction in chronic obstructive pulmonary disease (COPD) and are reduced by as much as 41% by the time someone is diagnosed with mild (Global Initiative for Chronic Obstructive Lung Disease [GOLD] stage 1) COPD. Objectives: To develop a single-cell atlas that describes the structural, cellular, and extracellular matrix alterations underlying terminal bronchiole loss in COPD. Methods: This cross-sectional study of 262 lung samples derived from 34 ex-smokers with normal lung function (n = 10) or GOLD stage 1 (n = 10), stage 2 (n = 8), or stage 4 (n = 6) COPD was performed to assess the morphology, extracellular matrix, single-cell atlas, and genes associated with terminal bronchiole reduction using stereology, micro-computed tomography, nonlinear optical microscopy, imaging mass spectrometry, and transcriptomics. Measurements and Main Results: The lumen area of terminal bronchioles progressively narrows with COPD severity as a result of the loss of elastin fibers within alveolar attachments, which was observed before microscopic emphysematous tissue destruction in GOLD stage 1 and 2 COPD. The single-cell atlas of terminal bronchioles in COPD demonstrated M1-like macrophages and neutrophils located within alveolar attachments and associated with the pathobiology of elastin fiber loss, whereas adaptive immune cells (naive, CD4, and CD8 T cells, and B cells) are associated with terminal bronchiole wall remodeling. Terminal bronchiole pathology was associated with the upregulation of genes involved in innate and adaptive immune responses, the interferon response, and the degranulation of neutrophils. Conclusions: This comprehensive single-cell atlas highlights terminal bronchiole alveolar attachments as the initial site of tissue destruction in centrilobular emphysema and an attractive target for disease modification.

Impact of Marijuana Smoking on COPD Progression in a Cohort of Middle-Aged and Older Persons

Background

Limited data are available regarding marijuana smoking's impact on the development or progression of chronic obstructive pulmonary disease (COPD) in middle-aged or older adults with a variable history of tobacco cigarette smoking.

Methods

We divided ever-tobacco smoking participants in the SubPopulations and InteRmediate Outcomes In COPD Study (SPIROMICS) into 3 groups based on self-reported marijuana use: current, former, or never marijuana smokers (CMSs, FMSs or NMSs, respectively). Longitudinal data were analyzed in participants with ≥2 visits over a period of ≥52 weeks.

Measurements

We compared CMSs, FMSs, and NMSs, and those with varying amounts of lifetime marijuana use. Mixed effects linear regression models were used to analyze changes in spirometry, symptoms, health status, and radiographic metrics; zero-inflated negative binomial models were used for exacerbation rates. All models were adjusted for age, sex, race, baseline tobacco smoking amount, and forced expiratory volume in 1 second (FEV1) %predicted.

Results

Most participants were followed for ≥4 years. Annual rates of change in FEV1, incident COPD, respiratory symptoms, health status, radiographic extent of emphysema or air trapping, and total or severe exacerbations were not different between CMSs or FMSs versus NMSs or between those with any lifetime amount of marijuana use versus NMSs.

Conclusions

Among SPIROMICS participants with or without COPD, neither former nor current marijuana smoking of any lifetime amount was associated with evidence of COPD progression or its development. Because of our study's limitations, these findings underscore the need for further studies to better understand longer-term effects of marijuana smoking in COPD.

COPD Phenotyping

COPD is a heterogeneous condition, the onset and trajectory of which is influenced not only by tobacco exposure but also an individual's genetics and the exposures they accumulate over their life course. In such a complex chronic disease, phenotyping individuals based on similar clinical or molecular characteristics can aid in guiding appropriate therapeutic management. Treatable traits, characteristics for which evidence exists for a specific favorable treatment response, are increasingly incorporated into COPD clinical guidelines. But the COPD phenotyping literature is evolving. Innovations in lung imaging and physiologic metrics, as well as omics technologies and biomarker science, are contributing to a better understanding of COPD heterogeneity. This review summarizes the evolution of COPD phenotyping, the current use of phenotyping to direct clinical care, and how innovations in clinical and molecular approaches to unraveling disease heterogeneity are refining our understanding of COPD phenotypes.

Inspiratory and Expiratory Computed Tomography Imaging Clusters Reflect Functional Characteristics in Chronic Obstructive Pulmonary Disease

Purpose

Disease probability measure (DPM) is a useful voxel-wise imaging assessment of gas-trapping and emphysematous lesions in patients with chronic obstructive pulmonary disease (COPD). To elucidate the progression of COPD, we performed a cluster analysis using the following DPM parameters: normal (DPM_Normal), gas-trapping (DPM_GasTrap), and emphysematous lesions (DPM_Emph). Our findings revealed the characteristics of each cluster and the 3-year disease progression using imaging parameters.

Patients and Methods

Inspiratory and expiratory chest computed tomography (CT) images of 131 patients with COPD were examined, of which 84 were followed up for 3 years. The percentage of low attenuation volume (LAV%) and the square root of the wall area of a hypothetical airway with an internal perimeter of 10 mm (√Aaw at Pi10) were quantitatively measured using inspiratory chest CT. A hierarchical cluster analysis was performed using the DPM parameters at baseline. Five clusters were named according to the dominant DPM parameters: normal (NL), normal-GasTrap (NL-GT), GasTrap (GT), GasTrap-Emphysema (GT-EM), and Emphysema (EM).

Results

Women were predominantly diagnosed with GT. Forced expiratory volume in 1 s gradually decreased in the following order: NL, NL-GT, GT, GT-EM, and EM. DPM_Emph correlated well with LAV%. Four clusters other than NL showed significantly higher values of √Aaw at Pi10 than NL; however, no significant differences were observed among them. In all clusters, DPM_Emph increased after 3 years. DPM_Normal only increased in the GT cluster.

Conclusion

Clusters using DPM parameters may reflect the characteristics of COPD and help understand the pathophysiology of the disease.

Artificial Intelligence in Lung Imaging

Recently, interest and advances in artificial intelligence (AI) including deep learning for medical images have surged. As imaging plays a major role in the assessment of pulmonary diseases, various AI algorithms have been developed for chest imaging. Some of these have been approved by governments and are now commercially available in the marketplace. In the field of chest radiology, there are various tasks and purposes that are suitable for AI: initial evaluation/triage of certain diseases, detection and diagnosis, quantitative assessment of disease severity and monitoring, and prediction for decision support. While AI is a powerful technology that can be applied to medical imaging and is expected to improve our current clinical practice, some obstacles must be addressed for the successful implementation of AI in workflows. Understanding and becoming familiar with the current status and potential clinical applications of AI in chest imaging, as well as remaining challenges, would be essential for radiologists and clinicians in the era of AI. This review introduces the potential clinical applications of AI in chest imaging and also discusses the challenges for the implementation of AI in daily clinical practice and future directions in chest imaging.

Reversible Airflow Obstruction Predicts Future Chronic Obstructive Pulmonary Disease Development in the SPIROMICS Cohort: An Observational Cohort Study

Rationale: Chronic obstructive pulmonary disease (COPD) is defined by fixed spirometric ratio, FEV1/FVC < 0.70 after inhaled bronchodilators. However, the implications of variable obstruction (VO), in which the prebronchodilator FEV1/FVC ratio is less than 0.70 but increases to 0.70 or more after inhaled bronchodilators, have not been determined. Objectives: We explored differences in physiology, exacerbations, and health status in participants with VO compared with reference participants without obstruction. Methods: Data from the SPIROMICS (Subpopulations and Intermediate Outcome Measures in COPD Study) cohort were obtained. Participants with VO were compared with reference participants without obstruction. Measurements and Main Results: We assessed differences in baseline radiographic emphysema and small airway disease at study entry, baseline, and change in lung function by spirometry, functional capacity by 6-minute walk, health status using standard questionnaires, exacerbation rates, and progression to COPD between the two groups. All models were adjusted for participant characteristics, asthma history, and tobacco exposure. We assessed 175 participants with VO and 603 reference participants without obstruction. Participants with VO had 6.2 times the hazard of future development of COPD controlling for other factors (95% confidence interval, 4.6-8.3; P < 0.001). Compared with reference participants, the VO group had significantly lower baseline pre- and post-bronchodilator (BD) FEV1, and greater decline over time in post-BD FEV1, and pre- and post-BD FVC. There were no significant differences in exacerbations between groups. Conclusions: Significant risk for future COPD development exists for those with pre- but not post-BD airflow obstruction. These findings support consideration of expanding spirometric criteria defining COPD to include pre-BD obstruction. Clinical trial registered with www.clinicaltrials.gov (NCT01969344).

Single material beam hardening correction via an analytical energy response model for diagnostic CT

BACKGROUND

Various clinical studies show the potential for a wider quantitative role of diagnostic X-ray computed tomography (CT) beyond size measurements. Currently, the clinical use of attenuation values is however limited due to their lack of robustness. This issue can be observed even on the same scanner across patient size and positioning. There are different causes for the lack of robustness in the attenuation values; one possible source of error is beam hardening of the X-ray source spectrum. The conventional and well-established approach to address this issue is a calibration-based single material beam hardening correction (BHC) using a water cylinder.

PURPOSE

We investigate an alternative approach for single material BHC with the aim of producing a more robust result for the attenuation values. The underlying hypothesis of this investigation is that calibration based BHC automatically corrects for scattered radiation in a manner that is sub-optimal in terms of bias as soon as the scanned object strongly deviates from the water cylinder used for calibration.

METHODS

The approach we propose performs BHC via an analytical energy response model that is embedded into a correction pipeline that efficiently estimates and subtracts scattered radiation in a patient-specific manner prior to BHC. The estimation of scattered radiation is based on minimizing, in average, the squared difference between our corrected data and the vendor-calibrated data. The used energy response model is considering the spectral effects of the detector response and of the pre-filtration of the source spectrum including a beam-shaping bowtie filter. The performance of the correction pipeline is first characterized with computer simulated data. Afterwards, it is tested using real 3-D CT data sets of two different phantoms, with various kV settings and phantom positions, assuming a circular data acquisition. The results are compared in the image domain to those from the scanner.

RESULTS

For experiments with a water cylinder, the proposed correction pipeline leads to similar results as the vendor. For reconstructions of a QRM liver phantom with extension ring, the proposed correction pipeline achieved a more uniform and stable outcome in the attenuation values of homogeneous materials within the phantom. For example, the root mean squared deviation between centered and off-centered phantom positioning was reduced from 6.6 HU to 1.8 HU in one profile.

CONCLUSIONS

We have introduced a patient-specific approach for single material BHC in diagnostic CT via the use of an analytical energy response model. This approach shows promising improvements in terms of robustness of attenuation values for large patient sizes. Our results contribute towards improving CT images so as to make CT attenuation values more reliable for use in clinical practice. This article is protected by copyright. All rights reserved.

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.

Head-to-head Comparison of Qualitative Radiologist Assessment With Automated Quantitative Computed Tomography Analysis for Bronchiolitis Obliterans Syndrome After Hematopoietic Cell Transplantation

PURPOSE

Computed tomography (CT) findings of bronchiolitis obliterans syndrome (BOS) can be nonspecific and variable. This study aims to measure the incremental value of automated quantitative lung CT analysis to clinical CT interpretation. A head-to-head comparison of quantitative CT lung density analysis by parametric response mapping (PRM) with qualitative radiologist performance in BOS diagnosis was performed.

MATERIALS AND METHODS

Inspiratory and end-expiratory CTs of 65 patients referred to a post-bone marrow transplant lung graft-versus-host-disease clinic were reviewed by 3 thoracic radiologists for the presence of mosaic attenuation, centrilobular opacities, airways dilation, and bronchial wall thickening. Radiologists' majority consensus diagnosis of BOS was compared with automated PRM air trapping quantification and to the gold-standard diagnosis of BOS as per National Institutes of Health (NIH) consensus criteria.

RESULTS

Using a previously established threshold of 28% air trapping on PRM, the diagnostic performance for BOS was as follows: sensitivity 56% and specificity 94% (area under the receiver operator curve [AUC]=0.75). Radiologist review of inspiratory CT images alone resulted in a sensitivity of 80% and a specificity of 69% (AUC=0.74). When radiologists assessed both inspiratory and end-expiratory CT images in combination, the sensitivity was 92% and the specificity was 59% (AUC=0.75). The highest performance was observed when the quantitative PRM report was reviewed alongside inspiratory and end-expiratory CT images, with a sensitivity of 92% and a specificity of 73% (AUC=0.83).

CONCLUSIONS

In the CT diagnosis of BOS, qualitative expert radiologist interpretation was noninferior to quantitative PRM. The highest level of diagnostic performance was achieved by the combination of quantitative PRM measurements with qualitative image feature assessments.

Treatment Trials in Young Patients with Chronic Obstructive Pulmonary Disease and Pre-Chronic Obstructive Pulmonary Disease Patients: Time to Move Forward

Chronic obstructive pulmonary disease (COPD) is the end result of a series of dynamic and cumulative gene-environment interactions over a lifetime. The evolving understanding of COPD biology provides novel opportunities for prevention, early diagnosis, and intervention. To advance these concepts, we propose therapeutic trials in two major groups of subjects: "young" individuals with COPD and those with pre-COPD. Given that lungs grow to about 20 years of age and begin to age at approximately 50 years, we consider "young" patients with COPD those patients in the age range of 20-50 years. Pre-COPD relates to individuals of any age who have respiratory symptoms with or without structural and/or functional abnormalities, in the absence of airflow limitation, and who may develop persistent airflow limitation over time. We exclude from the current discussion infants and adolescents because of their unique physiological context and COPD in older adults given their representation in prior randomized controlled trials (RCTs). We highlight the need of RCTs focused on COPD in young patients or pre-COPD to reduce disease progression, providing innovative approaches to identifying and engaging potential study subjects. We detail approaches to RCT design, including potential outcomes such as lung function, patient-reported outcomes, exacerbations, lung imaging, mortality, and composite endpoints. We critically review study design components such as statistical powering and analysis, duration of study treatment, and formats to trial structure, including platform, basket, and umbrella trials. We provide a call to action for treatment RCTs in 1) young adults with COPD and 2) those with pre-COPD at any age.

Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective

Artificial intelligence (AI) applications for chest radiography and chest CT are among the most developed applications in radiology. More than 40 certified AI products are available for chest radiography or chest CT. These AI products cover a wide range of abnormalities, including pneumonia, pneumothorax and lung cancer. Most applications are aimed at detecting disease, complemented by products that characterize or quantify tissue. At present, none of the thoracic AI products is specifically designed for the pediatric population. However, some products developed to detect tuberculosis in adults are also applicable to children. Software is under development to detect early changes of cystic fibrosis on chest CT, which could be an interesting application for pediatric radiology. In this review, we give an overview of current AI products in thoracic radiology and cover recent literature about AI in chest radiography, with a focus on pediatric radiology. We also discuss possible pediatric applications.

CNN-based Deformable Registration Facilitates Fast and Accurate Air Trapping Measurements at Inspiratory and Expiratory CT

PURPOSE

To develop a convolutional neural network (CNN)-based deformable lung registration algorithm to reduce computation time and assess its potential for lobar air trapping quantification.

MATERIALS AND METHODS

In this retrospective study, a CNN algorithm was developed to perform deformable registration of lung CT (LungReg) using data on 9118 patients from the COPDGene Study (data collected between 2007 and 2012). Loss function constraints included cross-correlation, displacement field regularization, lobar segmentation overlap, and the Jacobian determinant. LungReg was compared with a standard diffeomorphic registration (SyN) for lobar Dice overlap, percentage voxels with nonpositive Jacobian determinants, and inference runtime using paired t tests. Landmark colocalization error (LCE) across 10 patients was compared using a random effects model. Agreement between LungReg and SyN air trapping measurements was assessed using intraclass correlation coefficient. The ability of LungReg versus SyN emphysema and air trapping measurements to predict Global Initiative for Chronic Obstructive Lung Disease (GOLD) stages was compared using area under the receiver operating characteristic curves.

RESULTS

Average performance of LungReg versus SyN showed lobar Dice overlap score of 0.91-0.97 versus 0.89-0.95, respectively (P < .001); percentage voxels with nonpositive Jacobian determinant of 0.04 versus 0.10, respectively (P < .001); inference run time of 0.99 second (graphics processing unit) and 2.27 seconds (central processing unit) versus 418.46 seconds (central processing unit) (P < .001); and LCE of 7.21 mm versus 6.93 mm (P < .001). LungReg and SyN whole-lung and lobar air trapping measurements achieved excellent agreement (intraclass correlation coefficients > 0.98). LungReg versus SyN area under the receiver operating characteristic curves for predicting GOLD stage were not statistically different (range, 0.88-0.95 vs 0.88-0.95, respectively; P = .31-.95).

CONCLUSION

CNN-based deformable lung registration is accurate and fully automated, with runtime feasible for clinical lobar air trapping quantification, and has potential to improve diagnosis of small airway diseases.Keywords: Air Trapping, Convolutional Neural Network, Deformable Registration, Small Airway Disease, CT, Lung, Semisupervised Learning, Unsupervised Learning Supplemental material is available for this article. © RSNA, 2021 An earlier incorrect version of this article appeared online. This article was corrected on December 22, 2021.

Panlobular emphysema is associated with COPD disease severity: A study of emphysema subtype by computed tomography

BACKGROUND

Computed tomography has the potential to inform COPD prognosis. We sought to determine associations of emphysema phenotype with clinical parameters including lung function, inflammatory markers, and quality of life.

METHODS

Participants of this single-center observational cohort (n = 83) were 40-80 years old, had ≥10 pack-year smoking, and a diagnosis of COPD confirmed by spirometry. All participants had available historic chest CT scans which were systematically reviewed by a single expert radiologist and scored for emphysema subtype, extent, and distribution. Associations between radiographic findings and clinical parameters were determined.

RESULTS

Median age of participants was 72 years, median smoking 40 pack-years, and median FEV₁ 59% predicted. 84% of the participants had radiographic emphysema. Of those, 26% had panlobular emphysema (PLE), 68% centrilobular emphysema (CLE), and 6% paraseptal emphysema (PSE). As compared to the participants with no radiographic emphysema, the presence of PLE-dominant emphysema was associated with a lower BMI (P = 0.012) and greater extent of emphysema (P = 0.014). After adjusting for age, sex, and pack-years smoking history, PLE was associated with greater airflow obstruction by FEV₁% (48% vs 71%, P = 0.005), greater symptom burden by CAT score (18 vs 9, P = 0.015), worse quality of life by SGRQ score (43 vs 22, P = 0.025), and more systemic inflammation by erythrocyte sedimentation rate (P = 0.001). CLE- or PSE-dominant emphysema were not similarly associated with clinical features or symptom burden.

CONCLUSIONS

The presence of PLE-dominant emphysema was associated with greater extent of emphysema, greater airflow obstruction, increased respiratory symptoms, worse quality of life, and systemic inflammation. Further investigation is indicated to explore the pathogenesis of the PLE phenotype and the prognostic and treatment implications of PLE.

Significance of FEV3/FEV6 in recognition of early airway disease in smokers at risk of development of COPD: Analysis of the SPIROMICS cohort

BACKGROUND

Small airways are known to be affected early in the course of chronic obstructive pulmonary disease (COPD); however, traditional spirometric indices may not accurately identify small airways disease.

RESEARCH QUESTION

Can FEV₃/FEV₆ identify early airflow abnormalities and predict future clinically important respiratory-related outcomes, including development of COPD?

STUDY DESIGN AND METHODS

We included eight hundred thirty-two current and former smokers with post-bronchodilator FEV₁/FVC ≥0.7 from the SPIROMICS cohort. Participants were classified as having a reduced pre-bronchodilator FEV₃/FEV₆ based on lower limit of normal (LLN) values. Repeatability analysis was performed for FEV₃ and FEV₆. Regression modeling was used to evaluate the relationship between baseline FEV₃/FEV₆ and outcome measures including functional small airways disease on thoracic imaging and respiratory exacerbations. Interval censored analysis was used to assess progression to COPD.

RESULTS

FEV₃/FEV₆ <LLN at baseline, defined as reduced compared to FEV₃/FEV₆ ≥LLN, was associated with lower FEV₁, poorer health status (St. George's Respiratory Questionnaire score), more emphysema, and more functional small airways disease on quantitative imaging. FEV₃ and FEV₆ showed excellent agreement between repeat measurements. A reduced FEV₃/FEV₆ was associated with increased odds of a severe respiratory exacerbation within the first year of follow-up and decreased time to first exacerbation. A low FEV₃/FEV₆ was also associated with development of COPD by spirometry (post-bronchodilator FEV₁/FVC <0.7) during study follow-up.

INTERPRETATION

FEV₃/FEV₆ is a routinely available and repeatable spirometric index which can be useful in the evaluation of early airflow obstruction in current and former smokers without COPD. A reduced FEV₃/FEV₆ can identify those at risk for future development of COPD and respiratory exacerbations.

Feasibility of function-guided lung treatment planning with parametric response mapping

Purpose

Recent advancements in functional lung imaging have been developed to improve clinicians’ knowledge of patient pulmonary condition prior to treatment. Ultimately, it may be possible to employ these functional imaging modalities to tailor radiation treatment plans to optimize patient outcome and mitigate pulmonary complications. Parametric response mapping (PRM) is a computed tomography (CT)–based functional lung imaging method that utilizes a voxel‐wise image analysis technique to classify lung abnormality phenotypes, and has previously been shown to be effective at assessing lung complication risk in diagnostic applications. The purpose of this work was to demonstrate the implementation of PRM guidance in radiotherapy treatment planning.

Methods and materials

A retrospective study was performed with 18 lung cancer patients to test the incorporation of PRM into a radiotherapy planning workflow. Paired inspiration/expiration pretreatment CT scans were acquired and PRM analysis was utilized to classify each voxel as normal, parenchymal disease, small airway disease, and emphysema. Density maps were generated for each PRM classification to contour high density regions of pulmonary abnormalities. Conventional volumetric‐modulated arc therapy and PRM‐guided treatment plans were designed for each patient.

Results

PRM guidance was successfully implemented into the treatment planning process. The inclusion of PRM priorities resulted in statistically significant (p < 0.05) improvements to the V20Gy within the PRM avoidance contours. On average, reductions of 5.4% in the V20Gy(%) were found. The PRM‐guided treatment plans did not significantly increase the dose to the organs at risk or result in insufficient planning target volume coverage, but did increase plan complexity.

Conclusions

PRM guidance was successfully implemented into a treatment planning workflow and shown to be effective for dose redistribution within the lung. This work has provided a framework for the potential clinical implementation of PRM‐guided treatment planning.

Quantification of pulmonary perfusion abnormalities using DCE-MRI in COPD: comparison with quantitative CT and pulmonary function

Objectives

Pulmonary perfusion abnormalities are prevalent in patients with chronic obstructive pulmonary disease (COPD), are potentially reversible, and may be associated with emphysema development. Therefore, we aimed to evaluate the clinical meaningfulness of perfusion defects in percent (QDP) using DCE-MRI.

Methods

We investigated a subset of baseline DCE-MRIs, paired inspiratory/expiratory CTs, and pulmonary function testing (PFT) of 83 subjects (age = 65.7 ± 9.0 years, patients-at-risk, and all GOLD groups) from one center of the “COSYCONET” COPD cohort. QDP was computed from DCE-MRI using an in-house developed quantification pipeline, including four different approaches: Otsu’s method, k-means clustering, texture analysis, and 80^th percentile threshold. QDP was compared with visual MRI perfusion scoring, CT parametric response mapping (PRM) indices of emphysema (PRM_Emph) and functional small airway disease (PRM_fSAD), and FEV1/FVC from PFT.

Results

All QDP approaches showed high correlations with the MRI perfusion score (r = 0.67 to 0.72, p < 0.001), with the highest association based on Otsu’s method (r = 0.72, p < 0.001). QDP correlated significantly with all PRM indices (p < 0.001), with the strongest correlations with PRM_Emph (r = 0.70 to 0.75, p < 0.001). QDP was distinctly higher than PRM_Emph (mean difference = 35.85 to 40.40) and PRM_fSAD (mean difference = 15.12 to 19.68), but in close agreement when combining both PRM indices (mean difference = 1.47 to 6.03) for all QDP approaches. QDP correlated moderately with FEV1/FVC (r = − 0.54 to − 0.41, p < 0.001).

Conclusion

QDP is associated with established markers of disease severity and the extent corresponds to the CT-derived combined extent of PRM_Emph and PRM_fSAD. We propose to use QDP based on Otsu’s method for future clinical studies in COPD.

Key Points

• QDP quantified from DCE-MRI is associated with visual MRI perfusion score, CT PRM indices, and PFT.

• The extent of QDP from DCE-MRI corresponds to the combined extent of PRM_Emph and PRM_fSAD from CT.

• Assessing pulmonary perfusion abnormalities using DCE-MRI with QDP improved the correlations with CT PRM indices and PFT compared to the quantification of pulmonary blood flow and volume.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00330-021-08229-6.

New Method for Combined Quantitative Assessment of Air-Trapping and Emphysema on Chest Computed Tomography in Chronic Obstructive Pulmonary Disease: Comparison with Parametric Response Mapping

Objective

Emphysema and small-airway disease are the two major components of chronic obstructive pulmonary disease (COPD). We propose a novel method of quantitative computed tomography (CT) emphysema air-trapping composite (EAtC) mapping to assess each COPD component. We analyzed the potential use of this method for assessing lung function in patients with COPD.

Materials and Methods

A total of 584 patients with COPD underwent inspiration and expiration CTs. Using pairwise analysis of inspiration and expiration CTs with non-rigid registration, EAtC mapping classified lung parenchyma into three areas: Normal, functional air trapping (fAT), and emphysema (Emph). We defined fAT as the area with a density change of less than 60 Hounsfield units (HU) between inspiration and expiration CTs among areas with a density less than −856 HU on inspiration CT. The volume fraction of each area was compared with clinical parameters and pulmonary function tests (PFTs). The results were compared with those of parametric response mapping (PRM) analysis.

Results

The relative volumes of the EAtC classes differed according to the Global Initiative for Chronic Obstructive Lung Disease stages (p < 0.001). Each class showed moderate correlations with forced expiratory volume in 1 second (FEV₁) and FEV₁/forced vital capacity (FVC) (r = −0.659–0.674, p < 0.001). Both fAT and Emph were significant predictors of FEV₁ and FEV₁/FVC (R² = 0.352 and 0.488, respectively; p < 0.001). fAT was a significant predictor of mean forced expiratory flow between 25% and 75% and residual volume/total vital capacity (R² = 0.264 and 0.233, respectively; p < 0.001), while Emph and age were significant predictors of carbon monoxide diffusing capacity (R² = 0.303; p < 0.001). fAT showed better correlations with PFTs than with small-airway disease on PRM.

Conclusion

The proposed quantitative CT EAtC mapping provides comprehensive lung functional information on each disease component of COPD, which may serve as an imaging biomarker of lung function.

Seven Pillars of Small Airways Disease in Asthma and COPD: Supporting Opportunities for Novel Therapies

Identification of pathologic changes in early and mild obstructive lung disease has shown the importance of the small airways and their contribution to symptoms. Indeed, significant small airways dysfunction has been found prior to any overt airway obstruction being detectable by conventional spirometry techniques. However, most therapies for the treatment of obstructive lung disease target the physiological changes and associated symptoms that result from chronic lung disease, rather than directly targeting the specific underlying causes of airflow disruption or the drivers of disease progression. In addition, although spirometry is the current standard for diagnosis and monitoring of response to therapy, the most widely used measure, FEV1 , does not align with the pathologic changes in early or mild disease and may not align with symptoms or exacerbation frequency in the individual patient. Newer functional and imaging techniques allow more effective assessment of small airways dysfunction; however, significant gaps in our understanding remain. Improving our knowledge of the role of small airways dysfunction in early disease in the airways, along with the identification of novel end points to measure subclinical changes in this region (ie, those not captured as symptoms or identified through standard FEV1), may lead to the development of novel therapies that directly combat early airways disease processes with a view to slowing disease progression and reversing damage. This expert opinion paper discusses small airways disease in the context of asthma and COPD and highlights gaps in current knowledge that impede earlier identification of obstructive lung disease and the development and standardization of novel small airways-specific end points for use in clinical trials.

Fully integrating pathophysiological insights in COPD: an updated working disease model to broaden therapeutic vision

Our starting point is that relatively new findings into the pathogenesis and pathophysiology of airway disease in smokers that lead to chronic obstructive pulmonary disease (COPD) need to be reassessed as a whole and integrated into "mainstream" thinking along with traditional concepts which have stood the test of time. Such a refining of the accepted disease paradigm is urgently needed as thinking on therapeutic targets is currently under active reconsideration. We feel that generalised airway wall "inflammation" is unduly over-emphasised, and highlight the patchy and variable nature of the pathology (with the core being airway remodelling). In addition, we present evidence for airway wall disease in smokers/COPD as including a hypocellular, hypovascular, destructive, fibrotic pathology, with a likely spectrum of epithelial-mesenchymal transition states as significant drivers of this remodelling. Furthermore, we present data from a number of research modalities and integrate this with the aetiology of lung cancer, the role of chronic airway luminal colonisation/infection by a specific group of "respiratory" bacteria in smokers (which results in luminal inflammation) and the central role for oxidative stress on the epithelium. We suggest translation of these insights into more focus on asymptomatic smokers and early COPD, with the potential for fresh preventive and therapeutic approaches.

Progression of Emphysema and Small Airways Disease in Cigarette Smokers

BACKGROUND

Little is known about factors associated with emphysema progression in cigarette smokers. We evaluated factors associated with change in emphysema and forced expiratory volume in 1 second (FEV1) in participants with and without chronic obstructive pulmonary disease (COPD).

METHODS

This retrospective study included individuals participating in the COPD Genetic Epidemiology study who completed the 5-year follow-up, including inspiratory and expiratory computed tomography (CT) and spirometry. All paired CT scans were analyzed using micro-mapping, which classifies individual voxels as emphysema or functional small airway disease (fSAD). Presence and progression of emphysema and FEV1 were determined based on comparison to nonsmoker values. Logistic regression analyses were used to identify clinical parameters associated with disease progression.

RESULTS

A total of 3088 participants were included with a mean ± SD age of 60.7±8.9 years, including 72 nonsmokers. In all Global initiative for chronic Obstructive Lung Disease (GOLD) stages, the presence of emphysema at baseline was associated with emphysema progression (odds ratio [OR]: GOLD 0: 4.32; preserved ratio-impaired spirometry [PRISm]; 5.73; GOLD 1: 5.16; GOLD 2: 5.69; GOLD 3/4: 5.55; all p ≤0.01). If there was no emphysema at baseline, the amount of fSAD at baseline was associated with emphysema progression (OR for 1% increase: GOLD 0: 1.06; PRISm: 1.20; GOLD 1: 1.7; GOLD 3/4: 1.08; all p ≤ 0.03).In 1735 participants without spirometric COPD, progression in emphysema occurred in 105 (6.1%) participants and only 21 (1.2%) had progression in both emphysema and FEV1.

CONCLUSIONS

The presence of emphysema is an important predictor of emphysema progression. In patients without emphysema, fSAD is associated with the development of emphysema. In participants without spirometric COPD, emphysema progression occurred independently of FEV1 decline.

Pulmonary Functional Imaging: Part 2-State-of-the-Art Clinical Applications and Opportunities for Improved Patient Care

Pulmonary functional imaging may be defined as the regional quantification of lung function by using primarily CT, MRI, and nuclear medicine techniques. The distribution of pulmonary physiologic parameters, including ventilation, perfusion, gas exchange, and biomechanics, can be noninvasively mapped and measured throughout the lungs. This information is not accessible by using conventional pulmonary function tests, which measure total lung function without viewing the regional distribution. The latter is important because of the heterogeneous distribution of virtually all lung disorders. Moreover, techniques such as hyperpolarized xenon 129 and helium 3 MRI can probe lung physiologic structure and microstructure at the level of the alveolar-air and alveolar-red blood cell interface, which is well beyond the spatial resolution of other clinical methods. The opportunities, challenges, and current stage of clinical deployment of pulmonary functional imaging are reviewed, including applications to chronic obstructive pulmonary disease, asthma, interstitial lung disease, pulmonary embolism, and pulmonary hypertension. Among the challenges to the deployment of pulmonary functional imaging in routine clinical practice are the need for further validation, establishment of normal values, standardization of imaging acquisition and analysis, and evidence of patient outcomes benefit. When these challenges are addressed, it is anticipated that pulmonary functional imaging will have an expanding role in the evaluation and management of patients with lung disease.

Automated CT Staging of Chronic Obstructive Pulmonary Disease Severity for Predicting Disease Progression and Mortality with a Deep Learning Convolutional Neural Network

Purpose

To develop a deep learning-based algorithm to stage the severity of chronic obstructive pulmonary disease (COPD) through quantification of emphysema and air trapping on CT images and to assess the ability of the proposed stages to prognosticate 5-year progression and mortality.

Materials and Methods

In this retrospective study, an algorithm using co-registration and lung segmentation was developed in-house to automate quantification of emphysema and air trapping from inspiratory and expiratory CT images. The algorithm was then tested in a separate group of 8951 patients from the COPD Genetic Epidemiology study (date range, 2007-2017). With measurements of emphysema and air trapping, bivariable thresholds were determined to define CT stages of severity (mild, moderate, severe, and very severe) and were evaluated for their ability to prognosticate disease progression and mortality using logistic regression and Cox regression.

Results

On the basis of CT stages, the odds of disease progression were greatest among patients with very severe disease (odds ratio [OR], 2.67; 95% CI: 2.02, 3.53; P < .001) and were elevated in patients with moderate disease (OR, 1.50; 95% CI: 1.22, 1.84; P = .001). The hazard ratio of mortality for very severe disease at CT was 2.23 times the normal ratio (95% CI: 1.93, 2.58; P < .001). When combined with Global Initiative for Chronic Obstructive Lung Disease (GOLD) staging, patients with GOLD stage 2 disease had the greatest odds of disease progression when the CT stage was severe (OR, 4.48; 95% CI: 3.18, 6.31; P < .001) or very severe (OR, 4.72; 95% CI: 3.13, 7.13; P < .001).

Conclusion

Automated CT algorithms can facilitate staging of COPD severity, have diagnostic performance comparable with that of spirometric GOLD staging, and provide further prognostic value when used in conjunction with GOLD staging.Supplemental material is available for this article.© RSNA, 2021See also commentary by Kalra and Ebrahimian in this issue.

A quantitative analysis of biomechanical lung model consistency using 5DCT datasets

PURPOSE

Lung biomechanical models are important for understanding and characterizing lung anatomy and physiology. A key parameter of biomechanical modeling is the underlying tissue elasticity distribution. While human lung elasticity estimations do not have ground truths, model consistency checks can and should be employed to gauge the stability of the estimation techniques. This work proposes such a consistency check using a set of 10 subjects.

METHODS

We hypothesize that lung dynamics will be stable over a 2-3 min time period and that this stability can be employed to check biomechanical estimation stability. For this purpose, two sets of 12 fast helical free breathing computed tomography scans (FHFBCT) were acquired back-to-back for each of the subjects. A published breathing motion model [five-dimensional CT (5DCT)] was generated from each set. Both of the models were used to generate two biomechanical modeling input sets: (a) The lung geometry at the end-exhalation, and (b) the voxel displacement map that mapped the end-exhalation lung geometry with the end-inhalation lung geometry. Finite element biomechanical lung models were instantiated using the end-exhalation lung geometries. The models included voxel-specific lung tissue elasticity values and were optimized using a gradient search approach until the biomechanical model-generated displacement maps matched those of the 5DCT voxel displacement maps. Finally, the two elasticity distributions associated with each of the patient 5DCTs were quantitatively compared. Because the end-exhalation geometries differed slightly between the two scan datasets, the elasticity distributions were deformably mapped to one of the exhalation datasets.

RESULTS

For the 10 patients, on average, 90% of parenchymal voxels had <2 kPa Young's modulus difference between the two estimations, with a mean voxel difference of only 0.6 kPa. Similarly, 97% of the parenchymal voxels had <2 mm displacement difference between the two models with a mean difference of 0.48 mm. Furthermore, overlapping elasticity histograms for voxels between -600 and -900 HU (parenchymal tissues) showed that the histograms were consistent between the two estimations.

CONCLUSION

In this paper, we demonstrated that biomechanical lung models can be consistently estimated when using motion-model based imaging datasets, even though the models were created from scans acquired at different breaths.

Machine Learning Algorithms to Differentiate Among Pulmonary Complications After Hematopoietic Cell Transplant

BACKGROUND

Pulmonary complications, including infections, are highly prevalent in patients after hematopoietic cell transplantation with chronic graft-vs-host disease. These comorbid diseases can make the diagnosis of early lung graft-vs-host disease (bronchiolitis obliterans syndrome) challenging. A quantitative method to differentiate among these pulmonary diseases can address diagnostic challenges and facilitate earlier and more targeted therapy.

STUDY DESIGN AND METHODS

We conducted a single-center study of 66 patients with CT chest scans analyzed with a quantitative imaging tool known as parametric response mapping. Parametric response mapping results were correlated with pulmonary function tests and clinical characteristics. Five parametric response mapping metrics were applied to K-means clustering and support vector machine models to distinguish among posttransplantation lung complications solely from quantitative output.

RESULTS

Compared with parametric response mapping, spirometry showed a moderate correlation with radiographic air trapping, and total lung capacity and residual volume showed a strong correlation with radiographic lung volumes. K-means clustering analysis distinguished four unique clusters. Clusters 2 and 3 represented obstructive physiology (encompassing 81% of patients with bronchiolitis obliterans syndrome) in increasing severity (percentage air trapping 15.6% and 43.0%, respectively). Cluster 1 was dominated by normal lung, and cluster 4 was characterized by patients with parenchymal opacities. A support vector machine algorithm differentiated bronchiolitis obliterans syndrome with a specificity of 88%, sensitivity of 83%, accuracy of 86%, and an area under the receiver operating characteristic curve of 0.85.

INTERPRETATION

Our machine learning models offer a quantitative approach for the identification of bronchiolitis obliterans syndrome vs other lung diseases, including late pulmonary complications after hematopoietic cell transplantation.

Multicenter evaluation of parametric response mapping as an indicator of bronchiolitis obliterans syndrome after hematopoietic stem cell transplantation

Parametric response mapping (PRM) is a novel computed tomography (CT) technology that has shown potential for assessment of bronchiolitis obliterans syndrome (BOS) after hematopoietic stem cell transplantation (HCT). The primary aim of this study was to evaluate whether variations in image acquisition under real-world conditions affect the PRM measurements of clinically diagnosed BOS. CT scans were obtained retrospectively from 72 HCT recipients with BOS and graft-versus-host disease from Fred Hutchinson Cancer Research Center, Karolinska Institute, and the University of Michigan. Whole lung volumetric scans were performed at inspiration and expiration using site-specific acquisition and reconstruction protocols. PRM and pulmonary function measurements were assessed. Patients with moderately severe BOS at diagnosis (median forced expiratory volume at 1 second [FEV1] 53.5% predicted) had similar characteristics between sites. Variations in site-specific CT acquisition protocols had a negligible effect on the PRM-derived small airways disease (SAD), that is, BOS measurements. PRM-derived SAD was found to correlate with FEV1% predicted and FEV1/ forced vital capacity (R = -0.236, P = .046; and R = -0.689, P < .0001, respectively), which suggests that elevated levels in the PRM measurements are primarily affected by BOS airflow obstruction and not CT scan acquisition parameters. Based on these results, PRM may be applied broadly for post-HCT diagnosis and monitoring of BOS.

Recent advances in chronic obstructive pulmonary disease pathogenesis: from disease mechanisms to precision medicine

Chronic obstructive pulmonary disease (COPD) is a devastating lung disease with a high personal and societal burden. Exposure to toxic particles and gases, including cigarette smoke, is the main risk factor for COPD. Together with smoking cessation, current treatment strategies of COPD aim to improve symptoms and prevent exacerbations, but there is no disease-modifying treatment. The biggest drawback of today's COPD treatment regimen is the 'one size fits all' pharmacological intervention, mainly based on disease severity and symptoms and not the individual's disease pathology. To halt the worrying increase in the burden of COPD, disease management needs to be advanced with a focus on personalized treatment. The main pathological feature of COPD includes a chronic and abnormal inflammatory response within the lungs, which results in airway and alveolar changes in the lung as reflected by (small) airways disease and emphysema. Here we discuss recent developments related to the abnormal inflammatory response, ECM and age-related changes, structural changes in the small airways and the role of sex-related differences, which are all relevant to explain the individual differences in the disease pathology of COPD and improve disease endotyping. Furthermore, we will discuss the most recent developments of new treatment strategies using biologicals to target specific pathological features or disease endotypes of COPD. © 2020 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of Pathological Society of Great Britain and Ireland.

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

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

The Effects of Rare SERPINA1 Variants on Lung Function and Emphysema in SPIROMICS

Rationale: The role of PI (protease inhibitor) type Z heterozygotes and additional rare variant genotypes in the gene encoding alpha-1 antitrypsin, SERPINA1 (serpin peptidase inhibitor, clade A, member 1), in determining chronic obstructive pulmonary disease risk and severity is controversial.Objectives: To comprehensively evaluate the effects of rare SERPINA1 variants on lung function and emphysema phenotypes in subjects with significant tobacco smoke exposure using deep gene resequencing and alpha-1 antitrypsin concentrations.Methods: DNA samples from 1,693 non-Hispanic white individuals, 385 African Americans, and 90 Hispanics with ≥20 pack-years smoking were resequenced for the identification of rare variants (allele frequency < 0.05) in 16.9 kB of SERPINA1.Measurements and Main Results: White PI Z heterozygotes confirmed by sequencing (MZ; n = 74) had lower post-bronchodilator FEV1 (P = 0.007), FEV1/FVC (P = 0.003), and greater computed tomography-based emphysema (P = 0.02) compared with 1,411 white individuals without PI Z, S, or additional rare variants denoted as VR. PI Z-containing compound heterozygotes (ZS/ZVR; n = 7) had lower FEV1/FVC (P = 0.02) and forced expiratory flow, midexpiratory phase (P = 0.009). Nineteen white heterozygotes for five non-S/Z coding variants associated with lower alpha-1 antitrypsin had greater computed tomography-based emphysema compared with those without rare variants. In African Americans, a 5' untranslated region insertion (rs568223361) was associated with lower alpha-1 antitrypsin and functional small airway disease (P = 0.007).Conclusions: In this integrative deep sequencing study of SERPINA1 with alpha-1 antitrypsin concentrations in a heavy smoker and chronic obstructive pulmonary disease cohort, we confirmed the effects of PI Z heterozygote and compound heterozygote genotypes. We demonstrate the cumulative effects of multiple SERPINA1 variants on alpha-1 antitrypsin deficiency, lung function, and emphysema, thus significantly increasing the frequency of SERPINA1 variation associated with respiratory disease in at-risk smokers.

Pulmonary Imaging Phenotypes of Chronic Obstructive Pulmonary Disease Using Multiparametric Response Maps

Background Pulmonary imaging of chronic obstructive pulmonary disease (COPD) has focused on CT or MRI measurements, but these have not been evaluated in combination. Purpose To generate multiparametric response map (mPRM) measurements in ex-smokers with or without COPD by using volume-matched CT and hyperpolarized helium 3 (³He) MRI. Materials and Methods In this prospective study (https://clinicaltrials.gov, NCT02279329), participants underwent MRI and CT and completed pulmonary function tests, questionnaires, and the 6-minute walk test between December 2010 and January 2019. Disease status was determined by using Global initiative for chronic Obstructive Lung Disease (GOLD) criteria. The mPRM voxel values were generated by using co-registered MRI and CT labels. Kruskal-Wallis and Bonferroni tests were used to determine differences across disease severity, and correlations were determined by using Spearman coefficients. Results A total of 175 ex-smokers (mean age, 69 years ± 9 [standard deviation], 108 men) with or without COPD were evaluated. Ex-smokers without COPD had a larger fraction of normal mPRM voxels (60% vs 37%, 20%, and 7% for GOLD I, II, and III/IV disease, respectively; all P ≤ .001) and a smaller fraction of abnormal voxels, including small airways disease (normal CT, not ventilated: 5% vs 6% [not significant], 11%, and 19% [P ≤ .001 for both] for GOLD I, II, and III/IV disease, respectively) and mild emphysema (normal CT, abnormal apparent diffusion coefficient [ADC]: 33% vs 54%, 56%, and 54% for GOLD I, II, and III/IV disease respectively; all P ≤ .001). Normal mPRM measurements were positively correlated with forced expiratory volume in 1 second (FEV₁) (r = 0.65, P < .001), the FEV₁-to-forced vital capacity ratio (r = 0.81, P < .001), and diffusing capacity (r = 0.75, P < .001) and were negatively correlated with worse quality of life (r = -0.48, P < .001). Abnormal mPRM measurements of small airways disease (normal CT, not ventilated) and mild emphysema (normal CT, abnormal ADC) were negatively correlated with FEV₁ (r = -0.65 and -0.42, respectively; P < .001) and diffusing capacity (r = -0.53 and -0.60, respectively; P < .001) and were positively correlated with worse quality of life (r = 0.45 and r = 0.33, respectively; P < .001), both of which were present in ex-smokers without COPD. Conclusion Multiparametric response maps revealed two abnormal structure-function results related to emphysema and small airways disease, both of which were unexpectedly present in ex-smokers with normal spirometry and CT findings. © RSNA, 2020 Online supplemental material is available for this article.

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.

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.

Novel Respiratory Impedance-Based Phenotypes Reflect Different Pathophysiologies in Chronic Obstructive Pulmonary Disease Patients

Purpose

The forced oscillation technique (FOT) is a non-invasive method to measure respiratory impedance, the respiratory resistance (Rrs) and reactance (Xrs). The disease probability measure (DPM) is a useful computed tomography (CT) imaging variable for the assessment of gas trapping and emphysema in patients with chronic obstructive pulmonary disease (COPD) using pairs of inspiratory and expiratory CT images. We aimed to develop FOT-based phenotypes and determine whether the phenotypes and their imaging characteristics could facilitate the understanding of COPD pathophysiology.

Patients and methods

FOT and spirometry were examined in 164 COPD patients and 22 non-COPD smokers. COPD patients were divided into four FOT-based phenotypes (NL, normal group; RD, resistance-dominant group; XD, reactance-dominant group; and MIX, mixed group) based on the 3rd quartile values of R5 (Rrs at 5Hz) and X5 (Xrs at 5Hz) in the non-COPD group. The emphysematous lesions and the airway lesions were quantitatively assessed in CT images by low attenuation volume and the square root of the wall area of a hypothetical airway with an internal perimeter of 10 mm (√Aaw at Pi10), respectively. DPM imaging analysis was also performed in 131 COPD patients. We investigated the differences in COPD parameters between the FOT-based phenotypes.

Results

√Aaw at Pi10 were significantly higher in the RD, XD, and MIX groups than in the NL group. The XD group showed lower pulmonary function and higher dyspnea scores than the RD group. No significant changes in DPM values were observed between the RD and the NL groups. The gas-trapping area was significantly higher in the XD group than in the NL group. The MIX group showed the highest dyspnea score, most emphysematous lesions, and the lowest forced expiratory volume in 1 s % predicted value.

Conclusion

The FOT-based phenotyping may be useful to assess pathophysiological changes of COPD with CT assessments.

Clinical Significance of Bronchodilator Responsiveness Evaluated by Forced Vital Capacity in COPD: SPIROMICS Cohort Analysis

Objective

Bronchodilator responsiveness (BDR) is prevalent in COPD, but its clinical implications remain unclear. We explored the significance of BDR, defined by post-bronchodilator change in FEV₁ (BDR_FEV1) as a measure reflecting the change in flow and in FVC (BDR_FVC) reflecting the change in volume.

Methods

We analyzed 2974 participants from a multicenter observational study designed to identify varying COPD phenotypes (SPIROMICS). We evaluated the association of BDR with baseline clinical characteristics, rate of prospective exacerbations and mortality using negative binomial regression and Cox proportional hazards models.

Results

A majority of COPD participants exhibited BDR (52.7%). BDR_FEV1 occurred more often in earlier stages of COPD, while BDR_FVC occurred more frequently in more advanced disease. When defined by increases in either FEV₁ or FVC, BDR was associated with a self-reported history of asthma, but not with blood eosinophil counts. BDR_FVC was more prevalent in subjects with greater emphysema and small airway disease on CT. In a univariate analysis, BDR_FVC was associated with increased exacerbations and mortality, although no significance was found in a model adjusted for post-bronchodilator FEV₁.

Conclusion

With advanced airflow obstruction in COPD, BDR_FVC is more prevalent in comparison to BDR_FEV1 and correlates with the extent of emphysema and degree of small airway disease. Since these associations appear to be related to the impairment of FEV₁, BDR_FVC itself does not define a distinct phenotype nor can it be more predictive of outcomes, but it can offer additional insights into the pathophysiologic mechanism in advanced COPD.

Clinical trials registration

ClinicalTrials.gov: NCT01969344T4.

The matrikine acetyl-proline-glycine-proline and clinical features of COPD: findings from SPIROMICS

BACKGROUND

Pulmonary and systemic inflammation are central features of chronic obstructive pulmonary disease (COPD). Previous studies have demonstrated relationships between biologically active extracellular matrix components, or matrikines, and COPD pathogenesis. We studied the relationships between the matrikine acetyl-proline-glycine-proline (AcPGP) in sputum and plasma and clinical features of COPD.

METHODS

Sputum and plasma samples were obtained from COPD participants in the SPIROMICS cohort at enrollment. AcPGP was isolated using solid phase extraction and measured by mass spectrometry. Demographics, spirometry, quality of life questionnaires, and quantitative computed tomography (CT) imaging with parametric response mapping (PRM) were obtained at baseline. Severe COPD exacerbations were recorded at 1-year of prospective follow-up. We used linear and logistic regression models to measure associations between AcPGP and features of COPD, and Kaplan-Meier analyses to measure time-to-first severe exacerbation.

RESULTS

The 182 COPD participants in the analysis were 66 ± 8 years old, 62% male, 84% White race, and 39% were current smokers. AcPGP concentrations were 0.61 ± 1.89 ng/mL (mean ± SD) in sputum and 0.60 ± 1.13 ng/mL in plasma. In adjusted linear regression models, sputum AcPGP was associated with FEV₁/FVC, spirometric GOLD stage, PRM-small airways disease, and PRM-emphysema. Sputum AcPGP also correlated with severe AECOPD, and elevated sputum AcPGP was associated with shorter time-to-first severe COPD exacerbation. In contrast, plasma AcPGP was not associated with symptoms, pulmonary function, or severe exacerbation risk.

CONCLUSIONS

In COPD, sputum but not plasma AcPGP concentrations are associated with the severity of airflow limitation, small airways disease, emphysema, and risk for severe AECOPD at 1-year of follow-up.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT01969344 (SPIROMICS).

Functional CT imaging for identification of the spatial determinants of small-airways disease in adults with asthma

BACKGROUND

Asthma is a disease characterized by ventilation heterogeneity (VH). A number of studies have demonstrated that VH markers derived by using impulse oscillometry (IOS) or multiple-breath washout (MBW) are associated with key asthmatic patient-related outcome measures and airways hyperresponsiveness. However, the topographical mechanisms of VH in the lung remain poorly understood.

OBJECTIVES

We hypothesized that specific regionalization of topographical small-airway disease would best account for IOS- and MBW-measured indices in patients.

METHODS

We evaluated the results of paired expiratory/inspiratory computed tomography in a cohort of asthmatic (n = 41) and healthy (n = 11) volunteers to understand the determinants of clinical VH indices commonly reported by using IOS and MBW. Parametric response mapping (PRM) was used to calculate the functional small-airways disease marker PRMfSAD and Hounsfield unit (HU)-based density changes from total lung capacity to functional residual capacity (ΔHU); gradients of ΔHU in gravitationally perpendicular (parallel) inferior-superior (anterior-posterior) axes were quantified.

RESULTS

The ΔHU gradient in the inferior-superior axis provided the highest level of discrimination of both acinar VH (measured by using phase 3 slope analysis of multiple-breath washout data) and resistance at 5 Hz minus resistance at 20 Hz measured by using impulse oscillometry (R5-R20) values. Patients with a high inferior-superior ΔHU gradient demonstrated evidence of reduced specific ventilation in the lower lobes of the lungs and high levels of PRMfSAD. A computational small-airway tree model confirmed that constriction of gravitationally dependent, lower-zone, small-airway branches would promote the largest increases in R5-R20 values. Ventilation gradients correlated with asthma control and quality of life but not with exacerbation frequency.

CONCLUSIONS

Lower lobe-predominant small-airways disease is a major driver of clinically measured VH in adults with asthma.

Dynamics of exhaled breath temperature after smoking a cigarette and its association with lung function changes predictive of COPD risk in smokers: a cross-sectional study

Exhaled breath temperature (EBT) is a biomarker of inflammation and vascularity of the airways already shown to predict incident COPD. This cross-sectional study was aimed to assess the potential of EBT in identifying "healthy" smokers susceptible to cigarette smoke toxicity of the airways and to the risk of developing COPD by analysing the dynamics of EBT after smoking a cigarette and its associations with their demographics (age, smoking burden) and lung function. The study included 55 current smokers of both sexes, 29-62 years of age, with median smoking exposure of 15 (10-71.8) pack-years. EBT was measured at baseline and 5, 15, 30, 45, and 60 min after smoking a single cigarette. Lung function was measured with spirometry followed by a bronchodilator test. To compare changes in EBT between repeated measurements we used the analysis of variance and the area under the curve (EBTAUC) as a dependent variable. Multivariate regression analysis was used to look for associations with patient characteristics and lung function in particular. The average (±SD) baseline EBT was 33.42±1.50 °C. The highest significant increase to 33.84 (1.25) °C was recorded 5 min after the cigarette was smoked (p=0.003), and it took one hour for it to return to the baseline. EBTAUC showed significant repeatability (ICC=0.85, p<0.001) and was significantly associated with age, body mass index, number of cigarettes smoked a day, baseline EBT, and baseline FEF75 (R2=0.39, p<0.001 for the model). Our results suggest that EBT after smoking a single cigarette could be used as early risk predictor of changes associated with chronic cigarette smoke exposure.