Classifying Future Healthcare Utilization in COPD Using Quantitative CT Lung Imaging and Two-Step Feature Selection via Sparse Subspace Learning with the CanCOLD Study.

RATIONALE: Although numerous candidate features exist for predicting risk of higher risk of healthcare utilization in patients with chronic obstructive pulmonary disease (COPD), the process for selecting the most discriminative features remains unclear. OBJECTIVE: The objective of this study was to develop a robust feature selection method to identify the most discriminative candidate features for predicting healthcare utilization in COPD, and compare the model performance with other common feature selection methods. MATERIALS AND METHODS: In this retrospective study, demographic, lung function measurements and CT images were collected from 454 COPD participants from the Canadian Cohort Obstructive Lung Disease study from 2010-2017. A follow-up visit was completed approximately 1.5 years later and participants reported healthcare utilization. CT analysis was performed for feature extraction. A two-step hybrid feature selection method was proposed that utilized: (1) sparse subspace learning with nonnegative matrix factorization, and, (2) genetic algorithm. Seven commonly used feature selection methods were also implemented that reported the top 10 or 20 features for comparison. Performance was evaluated using accuracy. RESULTS: Of the 454 COPD participants evaluated, 161 (35%) utilized healthcare services at follow-up. The accuracy for predicting subsequent healthcare utilization for the seven commonly used feature selection methods ranged from 72%-76% with the top 10 features, and 77%-80% with the top 20 features. Relative to these methods, hybrid feature selection obtained significantly higher accuracy for predicting subsequent healthcare utilization at 82% ± 3% (p < 0.05). Selected features with the proposed method included: DLCO, FEV1, RV, FVC, TAC, LAA950, Pi-10, LAA856, LAC total hole count, outer area RB1, wall area RB1, wall area and Jacobian. CONCLUSION: The hybrid feature selection method identified the most discriminative features for classifying individuals with and without future healthcare utilization, and increased the accuracy compared to other state-of-the-art approaches.

Airway tree caliber heterogeneity and airflow obstruction among older adults.

INTRODUCTION: Smaller mean airway tree caliber is associated with airflow obstruction and chronic obstructive pulmonary disease (COPD). We investigated whether airway tree caliber heterogeneity was associated with airflow obstruction and COPD. METHODS: Two community-based cohorts (MESA Lung, CanCOLD) and a longitudinal case-control study of COPD (SPIROMICS) performed spirometry and computed tomography measurements of airway lumen diameters at standard anatomic locations and total lung volume. Percent-predicted airway lumen diameters were calculated using sex-specific reference equations accounting for age, height and lung volume. The association of airway tree caliber heterogeneity, quantified as the standard deviation (SD) of percent-predicted airway lumen diameters, with baseline forced expired volume in 1-second (FEV1), FEV1/forced vital capacity (FEV1/FVC) and COPD, as well as longitudinal spirometry, were assessed using regression models adjusted for age, sex, height, race-ethnicity, and mean airway tree caliber. RESULTS: Among 2,505 MESA Lung participants (mean±SD age: 69±9 years; 53% female, mean airway tree caliber: 99±10% predicted, airway tree caliber heterogeneity: 14±5%; median follow-up: 6.1 years), participants in the highest quartile of airway tree caliber heterogeneity exhibited lower FEV1 (adjusted mean difference: -125 ml, 95%CI:-171,-79), lower FEV1/FVC (adjusted mean difference: -0.01, 95%CI:-0.02,-0.01), and higher odds of COPD (adjusted OR 1.42, 95%CI:1.01-2.02) when compared with the lowest quartile, whereas longitudinal changes in FEV1 and FEV1/FVC did not differ significantly. Observations in CanCOLD and SPIROMICS were consistent. CONCLUSION: Among older adults, airway tree caliber heterogeneity was associated with airflow obstruction and COPD at baseline but was not associated with longitudinal changes in spirometry.

Mucosal immune alterations at the early onset of tissue destruction in chronic obstructive pulmonary disease.

Rationale: COPD is characterized by chronic airway inflammation, small airways changes, with disappearance and obstruction, and also distal/alveolar destruction (emphysema). The chronology by which these three features evolve with altered mucosal immunity remains elusive. This study assessed the mucosal immune defense in human control and end-stage COPD lungs, by detailed microCT and RNA transcriptomic analysis of diversely affected zones. Methods: In 11 control (non-used donors) and 11 COPD (end-stage) explant frozen lungs, 4 cylinders/cores were processed per lung for microCT and tissue transcriptomics. MicroCT was used to quantify tissue percentage and alveolar surface density to classify the COPD cores in mild, moderate and severe alveolar destruction groups, as well as to quantify terminal bronchioles in each group. Transcriptomics of each core assessed fold changes in innate and adaptive cells and pathway enrichment score between control and COPD cores. Immunostainings of immune cells were performed for validation. Results: In mildly affected zones, decreased defensins and increased mucus production were observed, along CD8+ T cell accumulation and activation of the IgA pathway. In more severely affected zones, CD68+ myeloid antigen-presenting cells, CD4+ T cells and B cells, as well as MHCII and IgA pathway genes were upregulated. In contrast, terminal bronchioles were decreased in all COPD cores. Conclusion: Spatial investigation of end-stage COPD lungs show that mucosal defense dysregulation with decreased defensins and increased mucus and IgA responses, start concomitantly with CD8+ T-cell accumulation in mild emphysema zones, where terminal bronchioles are already decreased. In contrast, adaptive Th and B cell activation is observed in areas with more advanced tissue destruction. This study suggests that in COPD innate immune alterations occur early in the tissue destruction process, which affects both the alveoli and the terminal bronchioles, before the onset of an adaptive immune response.

CT Imaging With Machine Learning for Predicting Progression to COPD in Individuals at Risk.

BACKGROUND: Identifying individuals at risk of progressing to COPD may allow for initiation of treatment to potentially slow the progression of the disease or the selection of subgroups for discovery of novel interventions. RESEARCH QUESTION: Does the addition of CT imaging features, texture-based radiomic features, and established quantitative CT scan to conventional risk factors improve the performance for predicting progression to COPD in individuals who smoke with machine learning? STUDY DESIGN AND METHODS: Participants at risk (individuals who currently or formerly smoked, without COPD) from the Canadian Cohort Obstructive Lung Disease (CanCOLD) population-based study underwent CT imaging at baseline and spirometry at baseline and follow-up. Various combinations of CT scan features, texture-based CT scan radiomics (n = 95), and established quantitative CT scan (n = 8), as well as demographic (n = 5) and spirometry (n = 3) measurements, with machine learning algorithms were evaluated to predict progression to COPD. Performance metrics included the area under the receiver operating characteristic curve (AUC) to evaluate the models. DeLong test was used to compare the performance of the models. RESULTS: Among the 294 at-risk participants who were evaluated (mean age, 65.6 ± 9.2 years; 42% female; mean pack-years, 17.9 ± 18.7), 52 participants (23.7%) in the training data set and 17 participants (23.0%) in the testing data set progressed to spirometric COPD at follow-up (2.5 ± 0.9 years from baseline). Compared with machine learning models with demographics alone (AUC, 0.649), the addition of CT imaging features to demographics (AUC, 0.730; P < .05) or CT imaging features and spirometry to demographics (AUC, 0.877; P < .05) significantly improved the performance for predicting progression to COPD. INTERPRETATION: Heterogeneous structural changes occur in the lungs of individuals at risk that can be quantified using CT imaging features, and evaluation of these features together with conventional risk factors improves performance for predicting progression to COPD.

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.

Factors Associated with Attrition in a Longitudinal Cohort of Older Adults in the Community.

Introduction: Retaining participants in longitudinal studies increases their power. We undertook this study in a population-based longitudinal cohort of adults with COPD to determine the factors associated with increased cohort attrition. Methods: In the longitudinal population-based Canadian Cohort of Obstructive Lung Disease (CanCOLD) study, 1561 adults > 40 years old were randomly recruited from 9 urban sites. Participants completed in-person visits at 18-month intervals and also were followed up every 3 months over the phone or by email. The cohort retention for the study and the reasons for attrition were analyzed. Hazard ratios and robust standard errors were calculated using Cox regression methods to explore the associations between participants who remained in the study and those who did not. Results: The median follow-up (years) of the study is 9.0 years. The overall mean retention was 77%. Reasons for attrition (23%) were: dropout by participant (39%), loss of contact (27%), investigator-initiated withdrawal (15%), deaths (9%), serious disease (9%), and relocation (2%). Factors independently associated with attrition were lower educational attainment, higher pack-year tobacco consumption, diagnosed cardiovascular disease, and a higher Hospital Anxiety and Depression Scale score: adjusted hazard ratios (95% confidence interval) were 1.43(1.11, 1.85); 1.01(1.00, 1.01); 1.44(1.13, 1.83); 1.06(1.02, 1.10) respectively. Conclusions: Identification and awareness of risk factors for attrition could direct targeted retention strategies in longitudinal studies. Moreover, the identification of patient characteristics associated with study dropout could address any potential bias introduced by differential dropouts.

Quantitative Imaging Metrics for the Assessment of Pulmonary Pathophysiology: An Official American Thoracic Society and Fleischner Society Joint Workshop Report.

Multiple thoracic imaging modalities have been developed to link structure to function in the diagnosis and monitoring of lung disease. Volumetric computed tomography (CT) renders three-dimensional maps of lung structures and may be combined with positron emission tomography (PET) to obtain dynamic physiological data. Magnetic resonance imaging (MRI) using ultrashort-echo time (UTE) sequences has improved signal detection from lung parenchyma; contrast agents are used to deduce airway function, ventilation-perfusion-diffusion, and mechanics. Proton MRI can measure regional ventilation-perfusion ratio. Quantitative imaging (QI)-derived endpoints have been developed to identify structure-function phenotypes, including air-blood-tissue volume partition, bronchovascular remodeling, emphysema, fibrosis, and textural patterns indicating architectural alteration. Coregistered landmarks on paired images obtained at different lung volumes are used to infer airway caliber, air trapping, gas and blood transport, compliance, and deformation. This document summarizes fundamental "good practice" stereological principles in QI study design and analysis; evaluates technical capabilities and limitations of common imaging modalities; and assesses major QI endpoints regarding underlying assumptions and limitations, ability to detect and stratify heterogeneous, overlapping pathophysiology, and monitor disease progression and therapeutic response, correlated with and complementary to, functional indices. The goal is to promote unbiased quantification and interpretation of in vivo imaging data, compare metrics obtained using different QI modalities to ensure accurate and reproducible metric derivation, and avoid misrepresentation of inferred physiological processes. The role of imaging-based computational modeling in advancing these goals is emphasized. Fundamental principles outlined herein are critical for all forms of QI irrespective of acquisition modality or disease entity.

Quantitative CT Lung Imaging and Machine Learning Improves Prediction of Emergency Room Visits and Hospitalizations in COPD.

RATIONALE: Predicting increased risk of future healthcare utilization in chronic obstructive pulmonary disease (COPD) patients is an important goal for improving patient management. OBJECTIVE: Our objective was to determine the importance of computed tomography (CT) lung imaging measurements relative to other demographic and clinical measurements for predicting future health services use with machine learning in COPD. MATERIALS AND METHODS: In this retrospective study, lung function measurements and chest CT images were acquired from Canadian Cohort of Obstructive Lung Disease study participants from 2010 to 2017 (https://clinicaltrials.gov, NCT00920348). Up to two follow-up visits (1.5- and 3-year follow-up) were performed and participants were asked for details related to healthcare utilization. Healthcare utilization was defined as any COPD hospitalization or emergency room visit due to respiratory problems in the 12 months prior to the follow-up visits. CT analysis was performed (VIDA Diagnostics Inc.); a total of 108 CT quantitative emphysema, airway and vascular measurements were investigated. A hybrid feature selection method with support vector machine classifier was used to predict healthcare utilization. Performance was determined using accuracy, F1-measure and area under the receiver operating characteristic curve (AUC) and Matthews's correlation coefficient (MC). RESULTS: Of the 527 COPD participants evaluated, 179 (35%) used healthcare services at follow-up. There were no significant differences between the participants with or without healthcare utilization at follow-up for age (p = 0.50), sex (p = 0.44), BMI (p = 0.05) or pack-years (p = 0.76). The accuracy for predicting subsequent healthcare utilization was 80% ± 3% (F1-measure = 74%, AUC = 0.80, MC = 0.6) when all measurements were considered, 76% ± 6% (F1-measure = 72%, AUC = 0.77, MC = 0.55) for CT measurements alone and 65% ± 5% (F1-measure = 60%, AUC = 0.67, MC = 0.34) for demographic and lung function measurements alone. CONCLUSION: The combination of CT lung imaging and conventional measurements leads to greater prediction accuracy of subsequent health services use than conventional measurements alone, and may provide needed prognostic information for patients suffering from COPD.

Comparison of Feature Selection Methods and Machine Learning Classifiers for Predicting Chronic Obstructive Pulmonary Disease Using Texture-Based CT Lung Radiomic Features.

RATIONALE: Texture-based radiomics analysis of lung computed tomography (CT) images has been shown to predict chronic obstructive pulmonary disease (COPD) status using machine learning models. However, various approaches are used and it is unclear which provides the best performance. OBJECTIVES: To compare the most commonly used feature selection and classification methods and determine the optimal models for classifying COPD status in a mild, population-based COPD cohort. MATERIALS AND METHODS: CT images from the multi-center Canadian Cohort Obstructive Lung Disease (CanCOLD) study were pre-processed by resampling the image to a 1mm isotropic voxel volume, segmenting the lung and removing the airways (VIDA Diagnostics Inc.), and applying a threshold of -1000HU-to-0HU. A total of 95 texture features were then extracted from each CT image. Combinations of 17 feature selection methods and 9 classifiers were tested and evaluated. In addition, the role of data cleaning (outlier removal and highly correlated feature removal) was evaluated. The area under the curve (AUC) from the receiver operating characteristic curve was used to evaluate model performance. RESULTS: A total of 1204 participants were evaluated (n = 602 no COPD, n = 602 COPD). There were no significant differences between the groups for female sex (no COPD = 46.3%; COPD = 38.5%; p = 0.77), or body mass index (no COPD = 27.7 kg/m2; COPD = 27.4 kg/m2; p = 0.21). The highest AUC value for predicting COPD status (AUC = 0.78 [0.73, 0.84]) was obtained following data cleaning and feature selection using Elastic Net with the Linear-SVM classifier. CONCLUSION: In a population-based cohort, the optimal combination for radiomics-based prediction of COPD status was Elastic Net as the feature selection method and Linear-SVM as the classifier.

Hedgehog Signaling as a Therapeutic Target for Airway Remodeling and Inflammation in Allergic Asthma.

Genome-wide association studies (GWAS) have shown that variants of patched homolog 1 (PTCH1) are associated with lung function abnormalities in the general population. It has also been shown that sonic hedgehog (SHH), an important ligand for PTCH1, is upregulated in the airway epithelium of patients with asthma and is suggested to be involved in airway remodeling. The contribution of hedgehog signaling to airway remodeling and inflammation in asthma is poorly described. To determine the biological role of hedgehog signaling-associated genes in asthma, gene silencing, over-expression, and pharmacologic inhibition studies were conducted after stimulating human airway epithelial cells or not with transforming growth factor ß1 (TGFß1), an important fibrotic mediator in asthmatic airway remodeling that also interacts with SHH pathway. TGFß1 increased hedgehog-signaling-related gene expression including SHH, GLI1 and GLI2. Knockdown of PTCH1 or SMO with siRNA, or use of hedgehog signaling inhibitors, consistently attenuated COL1A1 expression induced by TGFß1 stimulation. In contrast, Ptch1 over-expression augmented TGFß1-induced an increase in COL1A1 and MMP2 gene expression. We also showed an increase in hedgehog-signaling-related gene expression in primary airway epithelial cells from controls and asthmatics at different stages of cellular differentiation. GANT61, an inhibitor of GLI1/2, attenuated TGFß1-induced increase in COL1A1 protein expression in primary airway epithelial cells differentiated in air-liquid interface. Finally, to model airway tissue remodeling in vivo, C57BL/6 wildtype (WT) and Ptch1+/- mice were intranasally challenged with house dust mite (HDM) or phosphate-buffered saline (PBS) control. Ptch1+/- mice showed reduced sub-epithelial collagen expression and serum inflammatory proteins compared to WT mice in response to HDM challenge. In conclusion, TGFß1-induced airway remodeling is partially mediated through the hedgehog signaling pathway via the PTCH1-SMO-GLI axis. The Hedgehog signaling pathway is a promising new potential therapeutic target to alleviate airway tissue remodeling in patients with allergic airways disease.

Lung Microenvironments and Disease Progression in Fibrotic Hypersensitivity Pneumonitis.

Rationale: Fibrotic hypersensitivity pneumonitis (fHP) is an interstitial lung disease caused by sensitization to an inhaled allergen. Objectives: To identify the molecular determinants associated with progression of fibrosis. Methods: Nine fHP explant lungs and six unused donor lungs (as controls) were systematically sampled (4 samples/lung). According to microcomputed tomography measures, fHP cores were clustered into mild, moderate, and severe fibrosis groups. Gene expression profiles were assessed using weighted gene co-expression network analysis, xCell, gene ontology, and structure enrichment analysis. Gene expression of the prevailing molecular traits was also compared with idiopathic pulmonary fibrosis (IPF). The explant lung findings were evaluated in separate clinical fHP cohorts using tissue, BAL samples, and computed tomography scans. Measurements and Main Results: We found six molecular traits that associated with differential lung involvement. In fHP, extracellular matrix and antigen presentation/sensitization transcriptomic signatures characterized lung zones with only mild structural and histological changes, whereas signatures involved in honeycombing and B cells dominated the transcriptome in the most severely affected lung zones. With increasing disease severity, endothelial function was progressively lost, and progressive disruption in normal cellular homeostatic processes emerged. All six were also found in IPF, with largely similar associations with disease microenvironments. The molecular traits correlated with in vivo disease behavior in a separate clinical fHP cohort. Conclusions: We identified six molecular traits that characterize the morphological progression of fHP and associate with in vivo clinical behavior. Comparing IPF with fHP, the transcriptome landscape was determined considerably by local disease extent rather than by diagnosis alone.

Mian: interactive web-based microbiome data table visualization and machine learning platform.

SUMMARY: Mian is a web application to interactively visualize, run statistical tools and train machine learning models on operational taxonomic unit (OTU) or amplicon sequence variant (ASV) datasets to identify key taxonomic groups, diversity trends or taxonomic composition shifts in the context of provided categorical or numerical sample metadata. Tools, including Fisher's exact test, Boruta feature selection, alpha and beta diversity, and random forest and deep neural network classifiers, facilitate open-ended data exploration and hypothesis generation on microbial datasets. AVAILABILITY: Mian is freely available at: miandata.org. Mian is an open-source platform licensed under the MIT license with source code available at github.com/tbj128/mian. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The molecular and cellular mechanisms associated with the destruction of terminal bronchioles in COPD.

RATIONALE: Peripheral airway obstruction is a key feature of chronic obstructive pulmonary disease (COPD), but the mechanisms of airway loss are unknown. This study aims to identify the molecular and cellular mechanisms associated with peripheral airway obstruction in COPD. METHODS: Ten explanted lung specimens donated by patients with very severe COPD treated by lung transplantation and five unused donor control lungs were sampled using systematic uniform random sampling (SURS), resulting in 240 samples. These samples were further examined by micro-computed tomography (CT), quantitative histology and gene expression profiling. RESULTS: Micro-CT analysis showed that the loss of terminal bronchioles in COPD occurs in regions of microscopic emphysematous destruction with an average airspace size of ≥500 and <1000 µm, which we have termed a "hot spot". Based on microarray gene expression profiling, the hot spot was associated with an 11-gene signature, with upregulation of pro-inflammatory genes and downregulation of inhibitory immune checkpoint genes, indicating immune response activation. Results from both quantitative histology and the bioinformatics computational tool CIBERSORT, which predicts the percentage of immune cells in tissues from transcriptomic data, showed that the hot spot regions were associated with increased infiltration of CD4 and CD8 T-cell and B-cell lymphocytes. INTERPRETATION: The reduction in terminal bronchioles observed in lungs from patients with COPD occurs in a hot spot of microscopic emphysema, where there is upregulation of IFNG signalling, co-stimulatory immune checkpoint genes and genes related to the inflammasome pathway, and increased infiltration of immune cells. These could be potential targets for therapeutic interventions in COPD.

Impact of image pre-processing methods on computed tomography radiomics features in chronic obstructive pulmonary disease.

Computed tomography (CT) imaging texture-based radiomics analysis can be used to assess chronic obstructive pulmonary disease (COPD). However, different image pre-processing methods are commonly used, and how these different methods impact radiomics features and lung disease assessment, is unknown. The purpose of this study was to develop an image pre-processing pipeline to investigate how various pre-processing combinations impact radiomics features and their use for COPD assessment. Spirometry and CT images were obtained from the multi-centered Canadian Cohort of Obstructive Lung Disease study. Participants were divided based on assessment site and were further dichotomized as No COPD or COPD within their participant groups. An image pre-processing pipeline was developed, calculating 32 grey level co-occurrence matrix radiomics features. The pipeline included lung segmentation, airway segmentation or no segmentation, image resampling or no resampling, and either no pre-processing, binning, edgmentation, or thresholding pre-processing techniques. A three-way analysis of variance was used for method comparison. A nested 10-fold cross validation using logistic regression and multiple linear regression models were constructed to classify COPD and assess correlation with lung function, respectively. Logistic regression performance was evaluated using the area under the receiver operating characteristic curve (AUC). A total of 1210 participants (Sites 1-8: No COPD:n = 447, COPD:n = 413; and Site 9: No COPD:n = 155, COPD:n = 195) were evaluated. Between the two participant groups, at least 16/32 features were different between airway segmentation/no segmentation (P ≤ 0.04), at least 29/32 features were different between no resampling/resampling (P ≤ 0.04), and 32/32 features were different between the pre-processing techniques (P < 0.0001). Features generated using the resampling/edgmentation and resampling/thresholding pre-processing combinations, regardless of airway segmentation, performed the best in COPD classification (AUC ≥ 0.718), and explained the most variance with lung function (R2 ≥ 0.353). Therefore, the image pre-processing methods completed prior to CT radiomics feature extraction significantly impacted extracted features and their ability to assess COPD.

Computed tomography total airway count predicts progression to COPD in at-risk smokers.

There is limited understanding of how to identify people at high risk of developing COPD. Our objective was to investigate the association between computed tomography (CT) total airway count (TAC) and incident COPD over 3 years among ever-smokers from the population-based Canadian Cohort Obstructive Lung Disease (CanCOLD) study. CT and spirometry were acquired in ever-smokers at baseline; spirometry was repeated at 3-year follow-up. CT TAC was generated by summing all airway segments in the segmented airway tree (VIDA Diagnostics, Inc.). CT airway wall area, wall thickness for a theoretical airway with 10 mm perimeter (Pi10), and low attenuation areas below -856 HU (LAA856) were also measured. Logistic and mixed effects regression models were constructed to determine the association for CT measurements with development of COPD and forced expiratory volume in 1 s/forced vital capacity (FEV1/FVC) decline, respectively. Among 316 at-risk participants evaluated at baseline (65±9 years, 40% female, 18±19 pack-years), incident COPD was detected in 56 participants (18%) over a median 3.1±0.6 years of follow-up. Among CT measurements, only TAC was associated with incident COPD (p=0.03), where a 1-sd decrement in TAC increased the odds ratio for incident COPD by a factor of two. In a multivariable linear regression model, reduced TAC was significantly associated with greater longitudinal FEV1/FVC decline (p=0.03), but no other measurements were significant. CT TAC predicts incident COPD in at-risk smokers, indicating that smokers exhibit early structural changes associated with COPD prior to abnormal spirometry.

Spatial Dependence of CT Emphysema in Chronic Obstructive Pulmonary Disease Quantified by Using Join-Count Statistics.

Background Existing CT emphysema measurements quantify the extent or clustering of emphysema voxels in chronic obstructive pulmonary disease (COPD); however, these measurements do not quantify how those voxels are clustered. Purpose To develop a CT measurement to quantify the "compactness" of emphysema voxels, called the normalized join count (NJC), and to determine whether the NJC measurement differentiates COPD disease severity and correlates with lung function and visual emphysema scores. Materials and Methods In this secondary analysis of a prospective study, lung function and CT images were obtained from the Canadian Cohort Obstructive Lung Disease study visit 1 from 2009 to 2013. Participants were categorized as never-smokers, at risk, mild COPD, or moderate-severe COPD. Diffusion capacity for carbon monoxide/alveolar volume was measured. CT emphysema was scored visually by radiologists. CT measurements included the percentage low-attenuation area with attenuation less than -950 HU (%LAA-950insp), low-attenuation cluster (LAC), and lowest 15th percentile point of the CT lung density histogram. NJC was developed to measure compactness of CT emphysema voxels. An analysis of variance determined differences between groups. Multivariable ridge regression determined association between CT measurements with lung function and radiologist scores. Results A total of 1294 participants (750 men; mean age, 67 years ± 10) were analyzed (277 never-smokers, 306 at risk, 427 mild COPD, and 284 moderate-severe COPD). NJC, %LAA-950insp, and LAC measurements were higher in moderate-severe COPD than in never-smokers and at-risk participants (P < .05 for all comparisons), but only NJC was different between mild and ;moderate-severe COPD (mean, 1.98% ± 3.61 vs 1.44% ± 2.14; P < .05). In multivariable regression analysis, among all CT measurements NJC had the greatest relative contribution to diffusion capacity for carbon monoxide/alveolar volume (P = .002) and visual emphysema score (P < .001). Conclusion The relationship of normalized join count with severity of chronic obstructive pulmonary disease may indicate that the assessment of this disease is dependent on the number of low attenuating voxels or the size of clusters and the spatial arrangement of such voxels. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Grenier in this issue.

Small Airway Reduction and Fibrosis Is an Early Pathologic Feature of Idiopathic Pulmonary Fibrosis.

Rationale: To improve disease outcomes in idiopathic pulmonary fibrosis (IPF), it is essential to understand its early pathophysiology so that it can be targeted therapeutically. Objectives: Perform three-dimensional assessment of the IPF lung microstructure using stereology and multiresolution computed tomography (CT) imaging. Methods: Explanted lungs from patients with IPF (n = 8) and donor control subjects (n = 8) were inflated with air and frozen. CT scans were used to assess large airways. Unbiased, systematic uniform random samples (n = 8/lung) were scanned with microCT for stereological assessment of small airways (count number, and measure airway wall and lumen area) and parenchymal fibrosis (volume fraction of tissue, alveolar surface area, and septal wall thickness). Measurements and Main Results: The total number of airways on clinical CT was greater in IPF lungs than control lungs (P < 0.01), owing to an increase in the wall (P < 0.05) and lumen area (P < 0.05) resulting in more visible airways with a lumen larger than 2 mm. In IPF tissue samples without microscopic fibrosis, assessed by the volume fraction of tissue using microCT, there was a reduction in the number of the terminal (P < 0.01) and transitional (P < 0.001) bronchioles, and an increase in terminal bronchiole wall area (P < 0.001) compared with control lungs. In IPF tissue samples with microscopic parenchymal fibrosis, terminal bronchioles had increased airway wall thickness (P < 0.05) and dilated airway lumens (P < 0.001) leading to honeycomb cyst formations. Conclusions: This study has important implications for the current thinking on how the lung tissue is remodeled in IPF and highlights small airways as a potential target to modify IPF outcomes.

FAM13A as potential therapeutic target in modulating TGF-ß-induced airway tissue remodeling in COPD.

Genome-wide association studies have shown that a gene variant in the Family with sequence similarity 13, member A (FAM13A) is strongly associated with reduced lung function and the appearance of respiratory symptoms in patients with chronic obstructive pulmonary disease (COPD). A key player in smoking-induced tissue injury and airway remodeling is the transforming growth factor-ß1 (TGF-ß1). To determine the role of FAM13A in TGF-ß1 signaling, FAM13A-/- airway epithelial cells were generated using CRISPR-Cas9, whereas overexpression of FAM13A was achieved using lipid nanoparticles. Wild-type (WT) and FAM13A-/- cells were treated with TGF-ß1, followed by gene and/or protein expression analyses. FAM13A-/- cells augmented TGF-ß1-induced increase in collagen type 1 (COL1A1), matrix metalloproteinase 2 (MMP2), expression compared with WT cells. This effect was mediated by an increase in ß-catenin (CTNNB1) expression in FAM13A-/- cells compared with WT cells after TGF-ß1 treatment. FAM13A overexpression was partially protective from TGF-ß1-induced COL1A1 expression. Finally, we showed that airway epithelial-specific FAM13A protein expression is significantly increased in patients with severe COPD compared with control nonsmokers, and negatively correlated with lung function. In contrast, ß-catenin (CTNNB1), which has previously been linked to be regulated by FAM13A, is decreased in the airway epithelium of smokers with COPD compared with non-COPD subjects. Together, our data showed that FAM13A may be protective from TGF-ß1-induced fibrotic response in the airway epithelium via sequestering CTNNB1 from its regulation on downstream targets. Therapeutic increase in FAM13A expression in the airway epithelium of smokers at risk for COPD, and those with mild COPD, may reduce the extent of airway tissue remodeling.

The transition from normal lung anatomy to minimal and established fibrosis in idiopathic pulmonary fibrosis (IPF).

BACKGROUND: The transition from normal lung anatomy to minimal and established fibrosis is an important feature of the pathology of idiopathic pulmonary fibrosis (IPF). The purpose of this report is to examine the molecular and cellular mechanisms associated with this transition. METHODS: Pre-operative thoracic Multidetector Computed Tomography (MDCT) scans of patients with severe IPF (n = 9) were used to identify regions of minimal(n = 27) and established fibrosis(n = 27). MDCT, Micro-CT, quantitative histology, and next-generation sequencing were used to compare 24 samples from donor controls (n = 4) to minimal and established fibrosis samples. FINDINGS: The present results extended earlier reports about the transition from normal lung anatomy to minimal and established fibrosis by showing that there are activations of TGFBI, T cell co-stimulatory genes, and the down-regulation of inhibitory immune-checkpoint genes compared to controls. The expression patterns of these genes indicated activation of a field immune response, which is further supported by the increased infiltration of inflammatory immune cells dominated by lymphocytes that are capable of forming lymphoid follicles. Moreover, fibrosis pathways, mucin secretion, surfactant, TLRs, and cytokine storm-related genes also participate in the transitions from normal lung anatomy to minimal and established fibrosis. INTERPRETATION: The transition from normal lung anatomy to minimal and established fibrosis is associated with genes that are involved in the tissue repair processes, the activation of immune responses as well as the increased infiltration of CD4, CD8, B cell lymphocytes, and macrophages. These molecular and cellular events correlate with the development of structural abnormality of IPF and probably contribute to its pathogenesis.