Standardized airway wall thickness Pi10 from routine CT scans of COPD patients as imaging biomarker for disease severity, lung function decline, and mortality

Prediction of disease severity in COPD: a deep learning approach for anomaly-based quantitative assessment of chest CT

OBJECTIVES

To quantify regional manifestations related to COPD as anomalies from a modeled distribution of normal-appearing lung on chest CT using a deep learning (DL) approach, and to assess its potential to predict disease severity.

MATERIALS AND METHODS

Paired inspiratory/expiratory CT and clinical data from COPDGene and COSYCONET cohort studies were included. COPDGene data served as training/validation/test data sets (N = 3144/786/1310) and COSYCONET as external test set (N = 446). To differentiate low-risk (healthy/minimal disease, [GOLD 0]) from COPD patients (GOLD 1-4), the self-supervised DL model learned semantic information from 50 × 50 × 50 voxel samples from segmented intact lungs. An anomaly detection approach was trained to quantify lung abnormalities related to COPD, as regional deviations. Four supervised DL models were run for comparison. The clinical and radiological predictive power of the proposed anomaly score was assessed using linear mixed effects models (LMM).

RESULTS

The proposed approach achieved an area under the curve of 84.3 ± 0.3 (p < 0.001) for COPDGene and 76.3 ± 0.6 (p < 0.001) for COSYCONET, outperforming supervised models even when including only inspiratory CT. Anomaly scores significantly improved fitting of LMM for predicting lung function, health status, and quantitative CT features (emphysema/air trapping; p < 0.001). Higher anomaly scores were significantly associated with exacerbations for both cohorts (p < 0.001) and greater dyspnea scores for COPDGene (p < 0.001).

CONCLUSION

Quantifying heterogeneous COPD manifestations as anomaly offers advantages over supervised methods and was found to be predictive for lung function impairment and morphology deterioration.

CLINICAL RELEVANCE STATEMENT

Using deep learning, lung manifestations of COPD can be identified as deviations from normal-appearing chest CT and attributed an anomaly score which is consistent with decreased pulmonary function, emphysema, and air trapping.

KEY POINTS

• A self-supervised DL anomaly detection method discriminated low-risk individuals and COPD subjects, outperforming classic DL methods on two datasets (COPDGene AUC = 84.3%, COSYCONET AUC = 76.3%). • Our contrastive task exhibits robust performance even without the inclusion of expiratory images, while voxel-based methods demonstrate significant performance enhancement when incorporating expiratory images, in the COPDGene dataset. • Anomaly scores improved the fitting of linear mixed effects models in predicting clinical parameters and imaging alterations (p < 0.001) and were directly associated with clinical outcomes (p < 0.001).

Clinical utilization of airway inflammatory biomarkers in the prediction and monitoring of clinical outcomes in patients with chronic obstructive pulmonary disease

INTRODUCTION

Chronic obstructive pulmonary disease (COPD) accounts for 545 million people living with chronic respiratory disorders and is the third leading cause of morbidity and mortality around the world. COPD is a progressive disease, characterized by episodes of acute worsening of symptoms such as cough, dyspnea, and sputum production.

AREAS COVERED

Airway inflammation is a prominent feature of COPD. Chronic airway inflammation results in airway structural remodeling and emphysema. Persistent airway inflammation is a treatable trait of COPD and plays a significant role in disease development and progression. In this review, the authors summarize the current and emerging biomarkers that reveal the heterogeneity of airway inflammation subtypes, clinical outcomes, and therapeutic response in COPD.

EXPERT OPINION

Airway inflammation can be broadly categorized as eosinophilic (type 2 inflammation) and non-eosinophilic (non-type 2 inflammation) in COPD. Currently, blood eosinophil counts are incorporated in clinical practice guidelines to identify COPD patients who are at a higher risk of exacerbations and lung function decline, and who are likely to respond to inhaled corticosteroids. As new therapeutics are being developed for the chronic management of COPD, it is essential to identify biomarkers that will predict treatment response.

Small airways in asthma: From inflammation and pathophysiology to treatment response

Small airways are characterized as those with an inner diameter less than 2 mm and constitute a major site of pathology and inflammation in asthma disease. It is estimated that small airways dysfunction may occur before the emergence of noticeable symptoms, spirometric abnormalities and imaging findings, thus characterizing them as "the quiet or silent zone" of the lungs. Despite their importance, measuring and quantifying small airways dysfunction presents a considerable challenge due to their inaccessibility in usual functional measurements, primarily due to their size and peripheral localization. Several pulmonary function tests have been proposed for the assessment of the small airways, including impulse oscillometry, nitrogen washout, body plethysmography, as well as imaging methods. Nevertheless, none of these methods has been established as the definitive "gold standard," thus, a combination of them should be used for an effective assessment of the small airways. Widely used asthma treatments seem to also affect several parameters of the small airways. Emerging biologic treatments show promising results in reducing small airways inflammation and remodelling, providing evidence for potential alterations in the disease's progression and outcomes. These novel therapies have implications not only in the clinical aspects of asthma but also in its inflammatory and functional aspects.

Midregional proatrial naturetic peptide (MRproANP) and copeptin (COPAVP) as predictors of all-cause mortality in recently diagnosed mild to moderate COPD-results from COSYCONET

BACKGROUND

MRproANP and COPAVP are prognostic markers for mortality in chronic obstructive pulmonary disease (COPD). Furthermore, these biomarkers predict mortality due to cardiovascular diseases, which are important prognostically determining comorbidities in patients with COPD. However, less is known about these biomarkers in recently diagnosed mild to moderate COPD. Therefore, we analyzed these biomarkers as potential predictors of mortality in recently diagnosed mild to moderate COPD.

METHODS

The blood biomarkers considered were copeptin (COPAVP), midregional adrenomedullin (MRproADM), midregional proatrial naturetic peptide (MRproANP), and fibrinogen. Analyses were performed in patients with stable "recently diagnosed mild to moderate COPD" defined by GOLD grades 0-2 and diagnosis of COPD ≤ 5 years prior to inclusion into the COSYCONET cohort (COPD and Systemic Consequences-Comorbidities Network), using Cox regression analysis with stepwise adjustment for multiple COPD characteristics, comorbidities, troponin and NT-proBNP.

RESULTS

655 patients with recently diagnosed mild to moderate COPD were included. In the initial regression model, 43 of 655 patients died during the 6-year follow-up, in the final model 27 of 487. Regression analyses with adjustment for confounders identified COPAVP and MRproANP as statistically robust biomarkers (p < 0.05 each) of all-cause mortality, while MRproADM and fibrinogen were not. The fourth quartile of MRproANP (97 pmol/L) was associated with a hazard ratio of 4.5 (95%CI: 1.6; 12.8), and the fourth quartile of COPAVP (9.2 pmol/L) with 3.0 (1.1; 8.0). The results for MRproANP were confirmed in the total cohort of grade 0-4 (n = 1470 finally).

CONCLUSION

In patients with recently diagnosed mild to moderate COPD, elevated values of COPVP and in particular MRproANP were robust, independent biomarkers for all-cause mortality risk after adjustment for multiple other factors. This suggests that these markers might be considered in the risk assessment of early COPD.

Quantitative assessment of airway wall thickness in COPD patients with interstitial lung abnormalities

Background

Whether the airway is involved in the pathogenesis of interstitial lung abnormalities (ILA) is not well understood. Also the impact of ILA on lung function in COPD patients remains controversial. We aimed to assess the quantitative CT measurements of airway wall thickness (AWT) and lung function according to ILA status in COPD patients.

Methods

157 COPD patients discharged from our hospital from August 1, 2019 through August 31, 2022 who underwent chest CT imagings and pulmonary function tests were retrospectively enrolled. Linear regression analysis and multiple models were used to analyze associations between quantitative assessment of airway wall changes and the presence of ILA.

Results

In 157 COPD patients, 23 patients (14.6%) had equivocal ILA, 42 patients (26.8%) had definite ILA. The definite ILA group had the highest measurements of Pi10 (square root of theoretical airway wall area with a lumen perimeter of 10 mm), segmental AWT and segmental WA% (percentage of wall area), whereas the no ILA group had the lowest measurements of Pi10, segmental AWT and segmental WA%. In the adjusted analyses (adjusted by age, sex, body mass index, smoking intensity, COPD GOLD stage, lung function, slice thickness and scanner type), compared to COPD patients without ILA, the measurements of Pi10, segmental AWT and segmental WA% were higher in definite ILA group with differences of 0.225 mm (p = 0.012), 0.152 mm (p < 0.001), 4.8% (p < 0.001) respectively. COPD patients with definite ILA tended to have higher FEV1% predicted, FVC% predicted and lower MMEF75/25% predicted, but there were no statistically differences among the three groups.

Conclusion

Our study demonstrates the higher AWT measures in COPD patients with ILA compared to the patients without ILA. These findings suggest that the airway may be involved in the pathogenesis of ILA.

The utility of quantitative computed tomography in cohort studies of chronic obstructive pulmonary disease: a narrative review

Background and Objective

Chronic obstructive pulmonary disease (COPD) is a significant contributor to global morbidity and mortality. Quantitative computed tomography (QCT), a non-invasive imaging modality, offers the potential to assess lung structure and function in COPD patients. Amidst the coronavirus disease 2019 (COVID-19) pandemic, chest computed tomography (CT) scans have emerged as a viable alternative for assessing pulmonary function (e.g., spirometry), minimizing the risk of aerosolized virus transmission. However, the clinical application of QCT measurements is not yet widespread enough, necessitating broader validation to determine its usefulness in COPD management.

Methods

We conducted a search in the PubMed database in English from January 1, 2013 to April 20, 2023, using keywords and controlled vocabulary related to QCT, COPD, and cohort studies.

Key Content and Findings

Existing studies have demonstrated the potential of QCT in providing valuable information on lung volume, airway geometry, airway wall thickness, emphysema, and lung tissue density in COPD patients. Moreover, QCT values have shown robust correlations with pulmonary function tests, and can predict exacerbation risk and mortality in patients with COPD. QCT can even discern COPD subtypes based on phenotypic characteristics such as emphysema predominance, supporting targeted management and interventions.

Conclusions

QCT has shown promise in cohort studies related to COPD, since it can provide critical insights into the pathogenesis and progression of the disease. Further research is necessary to determine the clinical significance of QCT measurements for COPD management.

Qualitative and quantitative evaluation of computed tomography changes in adults with cystic fibrosis treated with elexacaftor-tezacaftor-ivacaftor: a retrospective observational study

Introduction: The availability of highly effective triple cystic fibrosis transmembrane conductance regulator (CFTR) modulator combination therapy with elexacaftor-tezacaftor-ivacaftor (ETI) has improved pulmonary outcomes and quality of life of people with cystic fibrosis (pwCF). The aim of this study was to assess computed tomography (CT) changes under ETI visually with the Brody score and quantitatively with dedicated software, and to correlate CT measures with parameters of clinical response. Methods: Twenty two adult pwCF with two consecutive CT scans before and after ETI treatment initiation were retrospectively included. CT was assessed visually employing the Brody score and quantitatively by YACTA, a well-evaluated scientific software computing airway dimensions and lung parenchyma with wall percentage (WP), wall thickness (WT), lumen area (LA), bronchiectasis index (BI), lung volume and mean lung density (MLD) as parameters. Changes in CT metrics were evaluated and the visual and quantitative parameters were correlated with each other and with clinical changes in sweat chloride concentration, spirometry [percent predicted of forced expiratory volume in one second (ppFEV1)] and body mass index (BMI). Results: The mean (SD) Brody score improved with ETI [55 (12) vs. 38 (15); p < 0.001], incl. sub-scores for mucus plugging, peribronchial thickening, and parenchymal changes (all p < 0.001), but not for bronchiectasis (p = 0.281). Quantitatve WP (p < 0.001) and WT (p = 0.004) were reduced, conversely LA increased (p = 0.003), and BI improved (p = 0.012). Lung volume increased (p < 0.001), and MLD decreased (p < 0.001) through a reduction of ground glass opacity areas (p < 0.001). Changes of the Brody score correlated with those of quantitative parameters, exemplarily WT with the sub-score for mucus plugging (r = 0.730, p < 0.001) and peribronchial thickening (r = 0.552, p = 0.008). Changes of CT parameters correlated with those of clinical response parameters, in particular ppFEV1 with the Brody score (r = -0.606, p = 0.003) and with WT (r = -0.538, p = 0.010). Discussion: Morphological treatment response to ETI can be assessed using the Brody score as well as quantitative CT parameters. Changes in CT correlated with clinical improvements. The quantitative analysis with YACTA proved to be an objective, reproducible and simple method for monitoring lung disease, particularly with regard to future interventional clinical trials.

Quantitative Analysis for Lung Disease on Thin-Section CT

Thin-section computed tomography (CT) is widely employed not only for assessing morphology but also for evaluating respiratory function. Three-dimensional images obtained from thin-section CT provide precise measurements of lung, airway, and vessel volumes. These volumetric indices are correlated with traditional pulmonary function tests (PFT). CT also generates lung histograms. The volume ratio of areas with low and high attenuation correlates with PFT results. These quantitative image analyses have been utilized to investigate the early stages and disease progression of diffuse lung diseases, leading to the development of novel concepts such as pre-chronic obstructive pulmonary disease (pre-COPD) and interstitial lung abnormalities. Quantitative analysis proved particularly valuable during the COVID-19 pandemic when clinical evaluations were limited. In this review, we introduce CT analysis methods and explore their clinical applications in the context of various lung diseases. We also highlight technological advances, including images with matrices of 1024 × 1024 and slice thicknesses of 0.25 mm, which enhance the accuracy of these analyses.