129Xe MRI Ventilation Textures and Longitudinal Quality-of-Life Improvements in Long-COVID.

RATIONALE AND OBJECTIVES: It remains difficult to predict longitudinal outcomes in long-COVID, even with chest CT and functional MRI. 129Xe MRI reflects airway dysfunction, measured using ventilation defect percent (VDP) and in long-COVID patients, MRI VDP was abnormal, suggestive of airways disease. While MRI VDP and quality-of-life improved 15-month post-COVID infection, both remained abnormal. To better understand the relationship of airways disease and quality-of-life improvements in patients with long-COVID, we extracted 129Xe ventilation MRI textures and generated machine-learning models in an effort to predict improved quality-of-life, 15-month post-infection. MATERIALS AND METHODS: Long-COVID patients provided written-informed consent to 3-month and 15-month post-infection visits. Pyradiomics was used to extract 129Xe ventilation MRI texture features, which were ranked using a Random-Forest classifier. Top-ranking features were used in classification models to dichotomize patients based on St. George's Respiratory Questionnaire (SGRQ) score improvement greater than the minimal-clinically-important-difference (MCID). Classification performance was evaluated using the area under the receiver-operator-characteristic-curve (AUC), sensitivity, and specificity. RESULTS: 120 texture features were extracted from 129Xe ventilation MRI in 44 long-COVID participants (54 ± 14 years), including 30 (52 ± 12 years) with ΔSGRQ≥MCID and 14 (58 ± 18 years) with ΔSGRQ

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.

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. 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 anatomical locations (trachea-to-subsegments) 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. Among 2,505 MESA Lung participants (means ± SD age: 69 ± 9 yr; 53% female, mean airway tree caliber: 99 ± 10% predicted, airway tree caliber heterogeneity: 14 ± 5%; median follow-up: 6.1 yr), 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 odds ratio: 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. 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.NEW & NOTEWORTHY In this study, by leveraging two community-based samples and a case-control study of heavy smokers, we show that among older adults, airway tree caliber heterogeneity quantified by CT is associated with airflow obstruction and COPD independent of age, sex, height, race-ethnicity, and dysanapsis. These observations suggest that airway tree caliber heterogeneity is a structural trait associated with low baseline lung function and normal decline trajectory that is relevant to COPD.

Beyond Spirometry: Linking Wasted Ventilation to Exertional Dyspnea in the Initial Stages of COPD.

Exertional dyspnea, a key complaint of patients with chronic obstructive pulmonary disease (COPD), ultimately reflects an increased inspiratory neural drive to breathe. In non-hypoxemic patients with largely preserved lung mechanics - as those in the initial stages of the disease - the heightened inspiratory neural drive is strongly associated with an exaggerated ventilatory response to metabolic demand. Several lines of evidence indicate that the so-called excess ventilation (high ventilation-CO2 output relationship) primarily reflects poor gas exchange efficiency, namely increased physiological dead space. Pulmonary function tests estimating the extension of the wasted ventilation and selected cardiopulmonary exercise testing variables can, therefore, shed unique light on the genesis of patients' out-of-proportion dyspnea. After a succinct overview of the basis of gas exchange efficiency in health and inefficiency in COPD, we discuss how wasted ventilation translates into exertional dyspnea in individual patients. We then outline what is currently known about the structural basis of wasted ventilation in "minor/trivial" COPD vis-à-vis the contribution of emphysema versus a potential impairment in lung perfusion across non-emphysematous lung. After summarizing some unanswered questions on the field, we propose that functional imaging be amalgamated with pulmonary function tests beyond spirometry to improve our understanding of this deeply neglected cause of exertional dyspnea. Advances in the field will depend on our ability to develop robust platforms for deeply phenotyping (structurally and functionally), the dyspneic patients showing unordinary high wasted ventilation despite relatively preserved FEV1.

Physiological Characterization of Preserved Ratio Impaired Spirometry in the CanCOLD Study: Implications for Exertional Dyspnea and Exercise Intolerance.

RATIONALE: It is increasingly recognized that adults with preserved ratio impaired spirometry (PRISm) are prone to increased morbidity. However, the underlying pathophysiological mechanisms are unknown. OBJECTIVES: Evaluate the mechanisms of increased dyspnea and reduced exercise capacity in PRISm. METHODS: We completed a cross-sectional analysis of the CanCOLD population-based study. We compared physiological responses in 59 participants meeting PRISm spirometric criteria (post-bronchodilator FEV1<80% predicted and FEV1/FVC≥0.7), 264 controls, and 170 ever-smokers with chronic obstructive pulmonary disease (COPD), at rest and during cardiopulmonary exercise testing (CPET). MEASUREMENTS AND MAIN RESULTS: PRISm had lower total lung, vital and inspiratory capacities than controls (all p<0.05), and minimal small airway, pulmonary gas-exchange, and radiographic parenchymal lung abnormalities. Compared with control, PRISm had higher dyspnea/oxygen uptake [V̇O2] ratio at peak exercise (4.0±2.2vs2.9±1.9, Borg units/L/min, p<0.001) and lower V̇O2peak (74±22vs96±25% predicted, p<0.001). At standardized submaximal work rates, PRISm had greater tidal volume/inspiratory capacity (VT%IC, p<0.001), reflecting inspiratory mechanical constraint. In contrast to PRISm, COPD had characteristic small airways dysfunction, dynamic hyperinflation, and pulmonary gas-exchange abnormalities. Despite these physiological differences between the 3 groups, the relationship between increasing dyspnea and VT%IC during CPET was similar. Resting IC significantly correlated with V̇O2peak (r=0.65, p<0.001) in the entire sample, even after adjusting for airflow limitation, gas-trapping and diffusing capacity. CONCLUSION: In PRISm, lower exercise capacity and higher exertional dyspnea than healthy controls were mainly explained by lower resting lung volumes and earlier onset of dynamic inspiratory mechanical constraints at relatively low work rates.

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

Background: Computed tomography (CT)-derived pectoralis muscle area (PMA) measurements are prognostic in people with or at-risk of COPD, but fully automated PMA extraction has yet to be developed. Our objective was to develop and validate a PMA extraction pipeline that can automatically: 1) identify the aortic arch slice; and 2) perform pectoralis segmentation at that slice. Methods: CT images from the Canadian Cohort of Obstructive Lung Disease (CanCOLD) study were used for pipeline development. Aorta atlases were used to automatically identify the slice containing the aortic arch by group-based registration. A deep learning model was trained to segment the PMA. The pipeline was evaluated in comparison to manual segmentation. An external dataset was used to evaluate generalisability. Model performance was assessed using the Dice-Sorensen coefficient (DSC) and PMA error. Results: In total 90 participants were used for training (age 67.0±9.9 years; forced expiratory volume in 1 s (FEV1) 93±21% predicted; FEV1/forced vital capacity (FVC) 0.69±0.10; 47 men), and 32 for external testing (age 68.6±7.4 years; FEV1 65±17% predicted; FEV1/FVC 0.50±0.09; 16 men). Compared with manual segmentation, the deep learning model achieved a DSC of 0.94±0.02, 0.94±0.01 and 0.90±0.04 on the true aortic arch slice in the train, validation and external test sets, respectively. Automated aortic arch slice detection obtained distance errors of 1.2±1.3 mm and 1.6±1.5 mm on the train and test data, respectively. Fully automated PMA measurements were not different from manual segmentation (p>0.05). PMA measurements were different between people with and without COPD (p=0.01) and correlated with FEV1 % predicted (p<0.05). Conclusion: A fully automated CT PMA extraction pipeline was developed and validated for use in research and clinical practice.

Mucus plugs in the airways of asthmatic subjects and smoking status.

BACKGROUND: Mucus plugs have been described in the airways of asthmatic subjects, particularly those with associated with type 2 inflammation and sputum eosinophilia. In the current study we addressed the question of whether smoking, neutrophilic inflammation and airway dimensions affected the prevalence of mucus plugs. METHODS: In a cohort of moderate to severe asthmatics (n = 50), including a group of ex-smokers and current smokers, the prevalence of mucus plugs was quantified using a semi-quantitative score based on thoracic computerized tomography. The relationships between mucus score, sputum inflammatory profile and airway architecture were tested according to patient's smoking status. RESULTS: Among the asthmatics (37% former or active smokers), 74% had at least one mucus plug. The median score was 3 and was unrelated to smoking status. A significant but weak correlation was found between mucus score, FEV1 and FEV1/FVC. Mucus score was significantly correlated with sputum eosinophils. Among former and active smokers, mucus score was correlated with sputum neutrophils. Mucus score was positively associated with FeNO in non-smoking subjects. The lumen dimensions of the main and lobar bronchi were significantly inversely correlated with mucus score. CONCLUSION: Airway mucus plugs could define an asthma phenotype with altered airway architecture and can occur in asthmatic subjects with either neutrophilic or eosinophilic sputum according to their smoking status.

Dysanapsis is not associated with exertional dyspnoea in healthy male and female never-smokers aged 40 years and older.

In healthy adults, airway-to-lung (i.e., dysanapsis) ratio is lower and dyspnoea during exercise at a given minute ventilation (V̇E) is higher in females than in males. We investigated the relationship between dysanapsis and sex on exertional dyspnoea in healthy adults. We hypothesized that females would have a smaller airway-to-lung ratio than males and that exertional dyspnoea would be associated with airway-to-lung ratio in males and females. We analyzed data from n = 100 healthy never-smokers aged ≥40 years enrolled in the Canadian Cohort Obstructive Lung Disease (CanCOLD) study who underwent pulmonary function testing, a chest computed tomography scan, and cardiopulmonary exercise testing. The luminal area of the trachea, right main bronchus, left main bronchus, right upper lobe, bronchus intermedius, left upper lobe, and left lower lobe were 22%-37% smaller (all p < 0.001) and the airway-to-lung ratio (i.e., average large conducting airway diameter relative to total lung capacity) was lower in females than in males (0.609 ± 0.070 vs. 0.674 ± 0.082; p < 0.001). During exercise, there was a significant effect of V̇E, sex, and their interaction on dyspnoea (all p < 0.05), indicating that dyspnoea increased as a function of V̇E to a greater extent in females than in males. However, after adjusting for age and total lung capacity, there were no significant associations between airway-to-lung ratio and measures of exertional dyspnoea, regardless of sex (all r < 0.34; all p > 0.05). Our findings suggest that sex differences in airway size do not contribute to sex differences in exertional dyspnoea.

Semiautomated Segmentation and Analysis of Airway Lumen in Pediatric Patients Using Ultra Short Echo Time MRI.

RATIONALE AND OBJECTIVES: Ultra short echo time (UTE) magnetic resonance imaging (MRI) pulse sequences have shown promise for airway assessment, but the feasibility and repeatability in the pediatric lung are unknown. The purpose of this work was to develop a semiautomated UTE MRI airway segmentation pipeline from the trachea-to-tertiary airways in pediatric participants and assess repeatability and lumen diameter correlations to lung function. MATERIALS AND METHODS: A total of 29 participants (n = 7 healthy, n = 11 cystic fibrosis, n = 6 asthma, and n = 5 ex-preterm), aged 7-18 years, were imaged using a 3D stack-of-spirals UTE examination at 3 T. Two independent observers performed airway segmentations using a pipeline developed in-house; observer 1 repeated segmentations 1 month later. Segmentations were extracted using region-growing with leak detection, then manually edited if required. The airway trees were skeletonized, pruned, and labeled. Airway lumen diameter measurements were extracted using ray casting. Intra- and interobserver variability was assessed using the Sørensen-Dice coefficient (DSC) and intra-class correlation coefficient (ICC). Correlations between lumen diameter and pulmonary function were assessed using Spearman's correlation coefficient. RESULTS: For airway segmentations and lumen diameter, intra- and interobserver DSCs were 0.88 and 0.80, while ICCs were 0.95 and 0.89, respectively. The variability increased from the trachea-to-tertiary airways for intra- (DSC: 0.91-0.64; ICC: 0.91-0.49) and interobserver (DSC: 0.84-0.51; ICC: 0.89-0.21) measurements. Lumen diameter was significantly correlated with forced expiratory volume in 1 second and forced vital capacity (P < .05). CONCLUSION: UTE MRI airway segmentation from the trachea-to-tertiary airways in pediatric participants across a range of diseases is feasible. The UTE MRI-derived lumen measurements were repeatable and correlated with lung function.

Machine Learning and CT Texture Features in Ex-smokers with no CT Evidence of Emphysema and Mildly Abnormal Diffusing Capacity.

RATIONALE AND OBJECTIVES: Ex-smokers without spirometry or CT evidence of chronic obstructive pulmonary disease (COPD) but with mildly abnormal diffusing capacity of the lungs for carbon monoxide (DLCO) are at higher risk of developing COPD. It remains difficult to make clinical management decisions for such ex-smokers without other objective assessments consistent with COPD. Hence, our objective was to develop a machine-learning and CT texture-analysis pipeline to dichotomize ex-smokers with normal and abnormal DLCO (DLCO≥75%pred and DLCO<75%pred). MATERIALS AND METHODS: In this retrospective study, 71 ex-smokers (50-85yrs) without COPD underwent spirometry, plethysmography, thoracic CT, and 3He MRI to generate ventilation defect percent (VDP) and apparent diffusion coefficients (ADC). PyRadiomics was utilized to extract 496 CT texture-features; Boruta and principal component analysis were used for feature selection and various models were investigated for classification. Machine-learning classifiers were evaluated using area under the receiver operator characteristic curve (AUC), sensitivity, specificity, and F1-measure. RESULTS: Of 71 ex-smokers without COPD, 29 with mildly abnormal DLCO had significantly different MRI ADC (p < .001), residual-volume to total-lung-capacity ratio (p = .003), St. George's Respiratory Questionnaire (p = .029), and six-minute-walk distance (6MWD) (p < .001), but similar relative area of the lung < -950 Hounsfield-units (RA950) (p = .9) compared to 42 ex-smokers with normal DLCO. Logistic-regression machine-learning mixed-model trained on selected texture-features achieved the best classification accuracy of 87%. All clinical and imaging measurements were outperformed by high-high-pass filter high-gray-level-run-emphasis texture-feature (AUC=0.81), which correlated with DLCO (ρ = -0.29, p = .02), MRI ADC (ρ = 0.23, p = .048), and 6MWD (ρ = -0.25, p = .02). CONCLUSION: In ex-smokers with no CT evidence of emphysema, machine-learning models exclusively trained on CT texture-features accurately classified ex-smokers with abnormal diffusing capacity, outperforming conventional quantitative CT measurements.

Complexity in clinical diagnoses of acute exacerbation of chronic obstructive pulmonary disease.

BACKGROUND: Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) is a clinical syndrome with various causes. It is not uncommon that COPD patients presenting with dyspnea have multiple causes for their symptoms including AECOPD, pneumonia, or congestive heart failure occurring concurrently. METHODS: To identify clinical, radiographic, and laboratory characteristics that might help distinguish AECOPD from another dominant disease in patients with a history of COPD, we conducted a retrospective cohort study of hospitalized patients with admitting diagnosis of AECOPD who were screened for a prospective randomized controlled trial from Sep 2016 to Mar 2018. Clinical characteristics, course in hospital, and final diagnosis at discharge were reviewed and adjudicated by two authors. The final diagnosis of each patient was determined based on the synthesis of all presenting signs and symptoms, imaging, and laboratory results. We adhered to AECOPD diagnosis definitions based on the GOLD guidelines. Univariate and multivariate analyses were performed to identify any associated features of AECOPD with and without other acute processes contributing to dyspnea. RESULTS: Three hundred fifteen hospitalized patients with admitting diagnosis of AECOPD were included. Mean age was 72.5 (SD 10.6) years. Two thirds (65.4%) had spirometry defined COPD. The most common presenting symptom was dyspnea (96.5%), followed by cough (67.9%), and increased sputum (57.5%). One hundred and eighty (57.1%) had a final diagnosis of AECOPD alone whereas 87 (27.6%) had AECOPD with other conditions and 48 (15.2%) did not have AECOPD after adjudication. Increased sputum purulence (OR 3.35, 95%CI 1.68-6.69) and elevated venous pCO2 (OR 1.04, 95%CI 1.01 - 1.07) were associated with a diagnosis of AECOPD but these were not associated with AECOPD alone without concomitant conditions. Radiographic evidence of pleural effusion (OR 0.26, 95%CI 0.12 - 0.58) was negatively associated with AECOPD with or without other conditions while radiographic evidence of pulmonary edema (OR 0.31; 95%CI 0.11 - 0.91) and lobar pneumonia (OR 0.13, 95%CI 0.07 - 0.25) suggested against the diagnosis of AECOPD alone. CONCLUSION: The study highlighted the complexity and difficulty of AECOPD diagnosis. A more specific clinical tool to diagnose AECOPD is needed.

Reduced Total Airway Count and Airway Wall Tapering after Three-Years in Ex-Smokers.

Computed tomography (CT) total-airway-count (TAC) and airway wall-thickness differ across chronic obstructive pulmonary disease (COPD) severities, but longitudinal insights are lacking. The aim of this study was to evaluate longitudinal CT airway measurements over three-years in ex-smokers. In this prospective convenience sample study, ex-smokers with (n = 50; 13 female; age = 70 ± 9 years; pack-years = 43 ± 26) and without (n = 40; 17 female; age = 69 ± 10 years; pack-years = 31 ± 17) COPD completed CT, 3He magnetic resonance imaging (MRI), and pulmonary function tests at baseline and three-year follow-up. CT TAC, airway wall-area (WA), lumen-area (LA), and wall-area percent (WA%) were generated. Emphysema was quantified as the relative-area-of-the-lung with attenuation < -950 Hounsfield-units (RA950). MRI ventilation-defect-percent (VDP) was also quantified. Differences over time were evaluated using paired-samples t tests. Multivariable prediction models using the backwards approach were generated. After three-years, forced-expiratory-volume in 1-second (FEV1) was not different in ex-smokers with (p = 0.4) and without (p = 0.5) COPD, whereas RA950 was (p < 0.001, p = 0.02, respectively). In ex-smokers without COPD, there was no change in TAC (p = 0.2); however, LA (p = 0.009) and WA% (p = 0.01) were significantly different. In ex-smokers with COPD, TAC (p < 0.001), WA (p = 0.04), LA (p < 0.001), and WA% (p < 0.001) were significantly different. In all ex-smokers, TAC was related to VDP (baseline: ρ = -0.30, p = 0.005; follow-up: ρ = -0.33, p = 0.002). In significant multivariable models, baseline airway wall-thickness was predictive of TAC worsening. After three-years, in the absence of FEV1 worsening, TAC diminished only in ex-smokers with COPD and airway walls were thinner in all ex-smokers. These longitudinal findings suggest that the evaluation of CT airway remodeling may be a useful clinical tool for predicting disease progression and managing COPD.Clinical trial registration: www.clinicaltrials.gov NCT02279329.

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.

CT airway remodelling and chronic cough.

RATIONALE: Structural airway changes related to chronic cough (CC) are described in the literature, but so far reported data are rare and non-conclusive. Furthermore, they derive mainly from cohorts with small sample sizes. Advanced CT imaging not only allows airway abnormalities to be quantified, but also to count the number of visible airways. The current study evaluates these airway abnormalities in CC and assesses the contribution of CC in addition to CT findings on the progression of airflow limitation, defined as a decline in forced expiratory volume in 1 s (FEV1) over time. METHODS: A total of 1183 males and females aged ≥40 years with thoracic CT scans and valid spirometry from Canadian Obstructive Lung Disease, a Canadian multicentre, population-based study has been included in this analysis. Participants were stratified into 286 never-smokers, 297 ever-smokers with normal lung function and 600 with chronic obstructive pulmonary disease (COPD) of different severity grades. Imaging parameters analyses included total airway count (TAC), airway wall thickness, emphysema as well as parameters for functional small airway disease quantification. RESULTS: Irrespective of COPD presence, CC was not related to specific airway and lung structure features. Independent of TAC and emphysema score, CC was highly associated with FEV1 decline over time in the entire study population, particularly in ever-smokers (p<0.0001). CONCLUSION: The absence of specific structural CT features independently from COPD presence indicate that other underlying mechanisms are contributing to the symptomatology of CC. On top of derived CT parameters, CC seems to be independently associated with FEV1 decline. TRIAL REGISTRATION NUMBER: NCT00920348.

Quantitative computed tomography and visual emphysema scores: association with lung function decline.

Background: Computed tomography (CT) visual emphysema score is a better predictor of mortality than single quantitative CT emphysema measurements in COPD, but there are numerous CT measurements that reflect COPD-related disease features. The purpose of this study was to determine if linear combinations of quantitative CT measurements by principal component analysis (PCA) have a greater association with forced expiratory volume in 1 s (FEV1) lower limit of normal (LLN) annualised change (ΔFEV1) than visual emphysema score in COPD. Methods: In this retrospective, longitudinal study, demographic, spirometry and CT images were acquired. CT visual emphysema score and quantitative analysis were performed; low attenuation area <950 HU (LAA950) and 12 other quantitative CT measurements were investigated. PCA was used for CT feature extraction. Multiple linear regression models for baseline FEV1 LLN and 6-year ΔFEV1 were used to determine associations with visual emphysema score and CT measurements. A total of 725 participants were analysed (n=299 never-smokers, n=242 at-risk and n=184 COPD). Results: Quantitative CT measures (LAA950 and PCA components) were independently statistically significant (p<0.05) in predicting baseline FEV1 LLN, whereas visual emphysema score was not statistically significant in any baseline model. When predicting 6-year ΔFEV1, only visual emphysema score was significant (p<0.05) in models with LAA950 and PCA combination of emphysema measurements. In the model with PCA using all CT measurements predicting 6-year ΔFEV1, visual emphysema score (p=0.021) along with one PCA component (p=0.004) were statistically significant. Conclusions: PCA with a combination of CT measurements reflecting several different COPD-related disease features independently predicted baseline lung function and increased the relative importance of quantitative CT compared with visual emphysema score for predicting lung function decline.

Quantitative CT Scan Imaging of the Airways for Diagnosis and Management of Lung Disease.

CT scan imaging provides high-resolution images of the lungs in patients with chronic respiratory diseases. Extensive research over the last several decades has focused on developing novel quantitative CT scan airway measurements that reflect abnormal airway structure. Despite many observational studies demonstrating that associations between CT scan airway measurements and clinically important outcomes such as morbidity, mortality, and lung function decline, few quantitative CT scan measurements are applied in clinical practice. This article provides an overview of the relevant methodologic considerations for implementing quantitative CT scan airway analyses and provides a review of the scientific literature involving quantitative CT scan airway measurements used in clinical or randomized trials and observational studies of humans. We also discuss emerging evidence for the clinical usefulness of quantitative CT scan imaging of the airways and discuss what is required to bridge the gap between research and clinical application. CT scan airway measurements continue to improve our understanding of disease pathophysiologic features, diagnosis, and outcomes. However, a literature review revealed a need for studies evaluating clinical benefit when quantitative CT scan imaging is applied in the clinical setting. Technical standards for quantitative CT scan imaging of the airways and high-quality evidence of clinical benefit from management guided by quantitative CT scan imaging of the airways are required.