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

Short- and long-term outcomes of pulmonary emphysema patients on mechanical ventilation admitted to the intensive care unit for acute respiratory failure: A retrospective observational study

INTRODUCTION

Acute respiratory failure is a leading cause of admission to the intensive care unit (ICU), with mortality rates remaining stagnant despite advances in resuscitation techniques. Comorbidities, notably chronic obstructive pulmonary disease, significantly impact ICU patient outcomes. Pulmonary emphysema, commonly associated with chronic obstructive pulmonary disease, poses a significant risk, yet its influence on ICU mortality remains understudied.

OBJECTIVES

The aim of this study was to assess the short- and long-term outcomes of ICU patients with pulmonary emphysema requiring mechanical ventilation for acute respiratory failure, evaluating the impact of emphysema severity.

METHODS

A single-centre retrospective cohort study was conducted from 2015 to 2021. Patients with pulmonary emphysema requiring invasive ventilation were included. Emphysema severity was assessed using chest computed tomography scans. Data on mortality, length of stay, and ventilator-free days were collected. Statistical analyses were performed to identify factors associated with outcomes.

RESULTS

Of the 89 included patients, 31.5% died during their ICU stay, with a 39.3% mortality within 12 months postdischarge. Emphysema severity did not significantly correlate with mortality or ventilator-free days. Chronic heart failure emerged as a significant predictor of ICU and in-hospital mortality.

CONCLUSIONS

Emphysema severity does not appear to independently affect mortality in intubated ICU patients with acute respiratory failure. However, mortality rates remain high, warranting further investigation into contributing factors. Our findings underline the complexity of managing critically ill patients with pulmonary emphysema and emphasise the need for comprehensive patient assessment and personalised treatment approaches.

Automated CT Image Processing for the Diagnosis, Prediction, and Differentiation of Phenotypes in Chronic Lung Allograft Dysfunction After Lung Transplantation

BACKGROUND

Chronic lung allograft dysfunction (CLAD) after lung transplantation is a common complication with a poor prognosis. We assessed the utility of quantitative computed tomography (CT) for the diagnosis, prediction, and discrimination of CLAD phenotypes.

METHODS

We retrospectively analyzed routine inspiratory and expiratory CT scans from 78 patients at different time points after lung transplantation. Mean lung density (MLD), parametric response mapping (PRM), percentage of air trapping, and airway wall morphology parameters were calculated using the image processing software YACTA. Diagnostic and predictive utility was determined by receiver operating characteristic analysis and Pearson correlation.

RESULTS

Markers of air trapping showed promise for the diagnosis and prediction of bronchiolitis obliterans syndrome (BOS); for example, expiratory MLD showed areas under the curve (AUCs) of 0.905 for diagnosis and 0.729 for 1-year prediction. For diagnosis of CLAD with mixed phenotype, peripheral measurements (e.g., PRM of peripheral functional small airway disease: AUC 0.893) were most suitable. Markers of airway thickening (e.g., expiratory wall thickness at an inner perimeter of 10 mm: AUC 0.767) gave good diagnostic values for the undefined phenotype. CT biomarkers differed significantly among CLAD phenotypes.

CONCLUSIONS

Different CT biomarkers are suitable for the diagnosis of CLAD phenotypes, prediction of BOS, and differentiation of CLAD phenotypes.

GOLD grade-specific characterization of COPD in the COSYCONET multi-center trial: comparison of semiquantitative MRI and quantitative CT

OBJECTIVES

We hypothesized that semiquantitative visual scoring of lung MRI is suitable for GOLD-grade specific characterization of parenchymal and airway disease in COPD and that MRI scores correlate with quantitative CT (QCT) and pulmonary function test (PFT) parameters.

METHODS

Five hundred ninety-eight subjects from the COSYCONET study (median age = 67 (60-72)) at risk for COPD or with GOLD1-4 underwent PFT, same-day paired inspiratory/expiratory CT, and structural and contrast-enhanced MRI. QCT assessed total lung volume (TLV), emphysema, and air trapping by parametric response mapping (PRMEmph, PRMfSAD) and airway disease by wall percentage (WP). MRI was analyzed using a semiquantitative visual scoring system for parenchymal defects, perfusion defects, and airway abnormalities. Descriptive statistics, Spearman correlations, and ANOVA analyses were performed.

RESULTS

TLV, PRMEmph, and MRI scores for parenchymal and perfusion defects were all higher with each GOLD grade, reflecting the extension of emphysema (all p < 0.001). Airway analysis showed the same trends with higher WP and higher MRI large airway disease scores in GOLD3 and lower WP and MRI scores in GOLD4 (p = 0.236 and p < 0.001). Regional heterogeneity was less evident on MRI, while PRMEmph and MRI perfusion defect scores were higher in the upper lobes, and WP and MRI large airway disease scores were higher in the lower lobes. MRI parenchymal and perfusion scores correlated moderately with PRMEmph (r = 0.61 and r = 0.60) and moderately with FEV1/FVC (r = -0.56).

CONCLUSION

Multi-center semiquantitative MRI assessments of parenchymal and airway disease in COPD matched GOLD grade-specific imaging features on QCT and detected regional disease heterogeneity. MRI parenchymal disease scores were correlated with QCT and lung function parameters.

KEY POINTS

Question Do MRI-based scores correlate with QCT and PFT parameters for GOLD-grade specific disease characterization of COPD? Findings MRI can visualize the parenchymal and airway disease features of COPD. Clinical relevance Lung MRI is suitable for GOLD-grade specific disease characterization of COPD and may serve as a radiation-free imaging modality in scientific and clinical settings, given careful consideration of its potential and limitations.

Association between dietary niacin intake and chronic obstructive pulmonary disease among American middle-aged and older individuals: A cross-section study

BACKGROUND

The attention towards the relationship between chronic obstructive pulmonary disease (COPD) and dietary intake is escalating. However, the effects of dietary niacin on COPD in middle and older individuals remains unclear. This study aimed to illuminate the connection between dietary niacin intake and COPD.

METHODS

This cross-sectional study analyzed 7,170 participants from the National Health and Nutrition Examination Survey (NHANES) spanning the years 2013 to 2018. Participants were categorized into four groups based on quartiles of dietary niacin intake. To examine the association between covariates, dietary niacin intake, and COPD, we employed univariate analysis and multivariate logistic regression equations. Additionally, restricted cubic splines were utilized to assess linearity. Furthermore, we conducted stratified and interaction analyses to evaluate the stability of the relationship in diverse subgroups.

RESULTS

Among the 7,170 participants, 11.6% (834/7170) were diagnosed with COPD. The multivariable adjusted odds ratios (ORs) and 95% confidence intervals (CIs) for COPD were 0.96 (95% CI: 0.77-1.19, p = 0.706), 0.78 (95% CI: 0.62-0.99, p = 0.038), and 0.76 (95% CI: 0.57-1.00, p = 0.047), respectively, when comparing the second, third, and fourth quartiles of niacin intake levels to the lowest quartile (p for trend = 0.017). An inverse association was observed between the occurrence of COPD and dietary niacin intake (nonlinear: p = 0.347). Stratified analyses revealed no significant differences or interactions.

CONCLUSION

Our findings suggest a potential link between increased dietary niacin intake and a decreased prevalence of COPD.

Assessing the Efficacy of Modified CT Severity Index Versus Conventional CT Severity Index in Determining Severity and Clinical Outcomes of Chronic Obstructive Pulmonary Disease (COPD)

Aims The aim of this study was to evaluate the severity of chronic obstructive pulmonary disease (COPD) using the computed tomography severity index (CTSI) and the modified CTSI (MCTSI) and to assess their correlation with clinical outcome measures. Additionally, the study aimed to compare the diagnostic performance of these indices in predicting moderate to severe COPD, based on patient outcomes. Materials and methods In this prospective study, conducted between November 2023 and March 2024, two radiologists, blinded to clinical outcomes, independently assessed CTSI and MCTSI. Clinical outcomes evaluated included the duration of hospital stay, intensive care unit (ICU) stay, organ failure (OF), evidence of infection, need for intervention, and mortality. Results The study included 60 COPD patients, with a majority being male (40, 66.7%) and a mean age of 65.4 ± 8.6 years. Based on CTSI, severity was classified as mild in 25 (41.7%), moderate in 20 (33.3%), and severe in 15 (25.0%) cases. According to MCTSI, severity was classified as mild in 22 (36.7%), moderate in 15 (25.0%), and severe in 23 (38.3%) cases. MCTSI showed concordance with the Global Initiative for Chronic Obstructive Lung Disease (GOLD) classification in 54 (90.0%) cases, while CTSI was concordant in 47 (78.3%) cases. For predicting moderate to severe COPD, CTSI demonstrated a sensitivity of 91.4%, specificity of 96.0%, positive predictive value (PPV) of 97.1%, and overall accuracy of 98.3%. MCTSI showed a sensitivity of 94.4%, specificity of 92.0%, PPV of 94.4%, and accuracy of 96.7%. Both indices correlated significantly with clinical outcomes, including OF, need for intervention, infection, and mortality (p < 0.001), although no significant correlation was found with ICU stay. Conclusion Both CTSI and MCTSI demonstrated a strong correlation with clinical outcomes in COPD patients and showed good concordance with severity assessments. MCTSI exhibited higher sensitivity, while CTSI demonstrated greater specificity for differentiating mild from moderate/severe COPD cases. These indices can be valuable tools in guiding clinical decision-making and assessing disease severity in COPD.

[Management of adult bronchiectasis - Consensus-based Guidelines for the German Respiratory Society (DGP) e. V. (AWMF registration number 020-030)]

Bronchiectasis is an etiologically heterogeneous, chronic, and often progressive respiratory disease characterized by irreversible bronchial dilation. It is frequently associated with significant symptom burden, multiple complications, and reduced quality of life. For several years, there has been a marked global increase in the prevalence of bronchiectasis, which is linked to a substantial economic burden on healthcare systems. This consensus-based guideline is the first German-language guideline addressing the management of bronchiectasis in adults. The guideline emphasizes the importance of thoracic imaging using CT for diagnosis and differentiation of bronchiectasis and highlights the significance of etiology in determining treatment approaches. Both non-drug and drug treatments are comprehensively covered. Non-pharmacological measures include smoking cessation, physiotherapy, physical training, rehabilitation, non-invasive ventilation, thoracic surgery, and lung transplantation. Pharmacological treatments focus on the long-term use of mucolytics, bronchodilators, anti-inflammatory medications, and antibiotics. Additionally, the guideline covers the challenges and strategies for managing upper airway involvement, comorbidities, and exacerbations, as well as socio-medical aspects and disability rights. The importance of patient education and self-management is also emphasized. Finally, the guideline addresses special life stages such as transition, family planning, pregnancy and parenthood, and palliative care. The aim is to ensure comprehensive, consensus-based, and patient-centered care, taking into account individual risks and needs.

Quantitative CT analysis of idiopathic pulmonary fibrosis and correlation with lung function study

BACKGROUND

Idiopathic Pulmonary Fibrosis (IPF) is a progressive fibrotic lung disease. However, the field of quantitative CT scan analysis in conjunction with pulmonary function test for IPF patients remains relatively understudied. In this study, we evaluated the diagnostic value of features derived high-resolution computed tomography (HRCT) for patients with IPF and correlated them with pulmonary function tests.

METHODS

We retrospectively analyzed the chest HRCT images and pulmonary function test results of 52 patients with IPF during the same period (1 week) and selected 52 healthy individuals, matched for sex, age, and body mass index (BMI) and with normal chest HRCT as controls. HRCT scans were performed using a Philips 256-row Brilliance iCT scanner with standardized parameters. Lung function tests were performed using a Jaeger volumetric tracer for forced vital capacity (FVC), total lung capacity (TLC), forced expiratory volume in first second (FEV1), FEV1/FVC, carbon monoxide diffusing capacity (DLCO), and maximum ventilation volume (MVV) metrics. CT quantitative analysis, including tissue segmentation and threshold-based quantification of lung abnormalities, was performed using 3D-Slicer software to calculate the percentage of normal lung areas (NL%), percentage of ground-glass opacity areas (GGO%), percentage of fibrotic area (F%) and abnormal lesion area percentage (AA%). Semi-quantitative analyses were performed by two experienced radiologists to assess disease progression. The aortic-to-sternal distance (ASD) was measured on axial images as a standardized parameter. Spearman or Pearson correlation analysis and multivariate stepwise linear regression were used to analyze the relationship between the data in each group, and the ROC curve was used to determine the optimal quantitative CT metrics for identifying IPF and controls.

RESULTS

ROC curve analysis showed that F% distinguished the IPF patient group from the control group with the largest area under the curve (AUC) of 0.962 (95% confidence interval: 0.85-0.96). Additionally, with F% = 4.05% as the threshold, the Youden's J statistic was 0.827, with a sensitivity of 92.3% and a specificity of 90.4%. The ASD was significantly lower in the late stage of progression than in the early stage (t = 5.691, P < 0.001), with a mean reduction of 2.45% per month. Quantitative CT indices correlated with all pulmonary function parameters except FEV1/FVC, with the highest correlation coefficients observed for F% and TLC%, FEV1%, FVC%, MVV% (r = - 0.571, - 0.520, - 0.521, - 0.555, respectively, all P-values < 0.001), and GGO% was significantly correlated with DLCO% (r = - 0.600, P < 0.001). Multiple stepwise linear regression analysis showed that F% was the best predictor of TLC%, FEV1%, FVC%, and MVV% (R2 = 0.301, 0.301, 0.300, and 0.302, respectively, all P-values < 0.001), and GGO% was the best predictor of DLCO% (R2 = 0.360, P < 0.001).

CONCLUSIONS

Quantitative CT analysis can be used to diagnose IPF and assess lung function impairment. A decrease in the ASD may indicate disease progression.

Comparative sensitivity of early cystic fibrosis lung disease detection tools in school aged children

BACKGROUND

Effective detection of early lung disease in cystic fibrosis (CF) is critical to understanding early pathogenesis and evaluating early intervention strategies. We aimed to compare ability of several proposed sensitive functional tools to detect early CF lung disease as defined by CT structural disease in school aged children.

METHODS

50 CF subjects (mean±SD 11.2 ± 3.5y, range 5-18y) with early lung disease (FEV1≥70 % predicted: 95.7 ± 11.8 %) performed spirometry, Multiple breath washout (MBW, including trapped gas assessment), oscillometry, cardiopulmonary exercise testing (CPET) and simultaneous spirometer-directed low-dose CT imaging. CT data were analysed using well-evaluated fully quantitative software for bronchiectasis and air trapping (AT).

RESULTS

CT bronchiectasis and AT occurred in 24 % and 58 % of patients, respectively. Of the functional tools, MBW detected the highest rates of abnormality: Scond 82 %, MBWTG RV 78 %, LCI 74 %, MBWTG IC 68 % and Sacin 51 %. CPET VO2peak detected slightly higher rates of abnormality (9 %) than spirometry-based FEV1 (2 %). For oscillometry AX (14 %) performed better than Rrs (2 %) whereas Xrs and R5-19 failed to detect any abnormality. LCI and Scond correlated with bronchiectasis (r = 0.55-0.64, p < 0.001) and AT (r = 0.73-0.74, p < 0.001). MBW-assessed trapped gas was detectable in 92 % of subjects and concordant with CT-assessed AT in 74 %.

CONCLUSIONS

Significant structural and functional deficits occur in early CF lung disease, as detected by CT and MBW. For MBW, additional utility, beyond that offered by LCI, was suggested for Scond and MBW-assessed gas trapping. Our study reinforces the complementary nature of these tools and the limited utility of conventional oscillometry and CPET in this setting.

Artificial intelligence in COPD CT images: identification, staging, and quantitation

Chronic obstructive pulmonary disease (COPD) stands as a significant global health challenge, with its intricate pathophysiological manifestations often demanding advanced diagnostic strategies. The recent applications of artificial intelligence (AI) within the realm of medical imaging, especially in computed tomography, present a promising avenue for transformative changes in COPD diagnosis and management. This review delves deep into the capabilities and advancements of AI, particularly focusing on machine learning and deep learning, and their applications in COPD identification, staging, and imaging phenotypes. Emphasis is laid on the AI-powered insights into emphysema, airway dynamics, and vascular structures. The challenges linked with data intricacies and the integration of AI in the clinical landscape are discussed. Lastly, the review casts a forward-looking perspective, highlighting emerging innovations in AI for COPD imaging and the potential of interdisciplinary collaborations, hinting at a future where AI doesn't just support but pioneers breakthroughs in COPD care. Through this review, we aim to provide a comprehensive understanding of the current state and future potential of AI in shaping the landscape of COPD diagnosis and management.

A Multicenter Study of COPD and Cognitive Impairment: Unraveling the Interplay of Quantitative CT, Lung Function, HIF-1α, and Clinical Variables

Purpose

The exact link between cognitive impairment (CI) and chronic obstructive pulmonary disease (COPD) is still limited. Thus, we aim to find the relationship and interaction of quantitative CT (QCT), lung function, HIF-1α, and clinical factors with the development of CI among COPD patients.

Patients and Methods

A cross-sectional multicentre study was conducted from January 2022 to December 2023. We collected clinical data, spirometry, CT images, and venous blood samples from 114 COPD participants. Cognitive impairment assessment using the Montreal Cognitive Assessment Indonesian version (MoCA-Ina) with a cutoff value 26. The QCT analysis consists of lung density, airway wall thickness, pulmonary artery-to-aorta ratio (PA:A), and pectoralis muscles using 3D Slicer software. Serum HIF-1α analysis was performed using ELISA.

Results

We found significant differences between %LAA-950, age, COPD duration, BMI, FEV1 pp, and FEV1/FVC among GOLD grades I-IV. Only education duration was found to correlate with CI (r = 0.40; p < 0.001). We found no significant difference in HIF-1α among GOLD grades (p = 0.149) and no correlation between HIF-1α and CI (p = 0.105). From multiple linear regression, we observed that the MoCA-Ina score was influenced mainly by %LAA-950 (p = 0.02) and education duration (p = 0.01). The path analysis model showed both %LAA and education duration directly and indirectly through FEV1 pp contributing to CI.

Conclusion

We conclude that the utilization of QCT parameters is beneficial as it can identify abnormalities and contribute to the development of CI, indicating its potential utility in clinical decision-making. The MoCA-Ina score in COPD is mainly affected by %LAA-950 and education duration. Contrary to expectations, this study concludes that HIF-1α does not affect CI among COPD patients.

Predicting Acute Exacerbation Phenotype in Chronic Obstructive Pulmonary Disease Patients Using VGG-16 Deep Learning

INTRODUCTION

Exacerbations of chronic obstructive pulmonary disease (COPD) have a significant impact on hospitalizations, morbidity, and mortality of patients. This study aimed to develop a model for predicting acute exacerbation in COPD patients (AECOPD) based on deep-learning (DL) features.

METHODS

We performed a retrospective study on 219 patients with COPD who underwent inspiratory and expiratory HRCT scans. By recording the acute respiratory events of the previous year, these patients were further divided into non-AECOPD group and AECOPD group according to the presence of acute exacerbation events. Sixty-nine quantitative CT (QCT) parameters of emphysema and airway were calculated by NeuLungCARE software, and 2,000 DL features were extracted by VGG-16 method. The logistic regression method was employed to identify AECOPD patients, and 29 patients of external validation cohort were used to access the robustness of the results.

RESULTS

The model 3-B achieved an area under the receiver operating characteristic curve (AUC) of 0.933 and 0.865 in the testing cohort and external validation cohort, respectively. Model 3-I obtained AUC of 0.895 in the testing cohort and AUC of 0.774 in the external validation cohort. Model 7-B combined clinical characteristics, QCT parameters, and DL features achieved the best performance with an AUC of 0.979 in the testing cohort and demonstrating robust predictability with an AUC of 0.932 in the external validation cohort. Likewise, model 7-I achieved an AUC of 0.938 and 0.872 in the testing cohort and external validation cohort, respectively.

CONCLUSIONS

DL features extracted from HRCT scans can effectively predict acute exacerbation phenotype in COPD patients.

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).

COPD stage detection: leveraging the auto-metric graph neural network with inspiratory and expiratory chest CT images

Chronic obstructive pulmonary disease (COPD) is a common lung disease that can lead to restricted airflow and respiratory problems, causing a significant health, economic, and social burden. Detecting the COPD stage can provide a timely warning for prompt intervention in COPD patients. However, existing methods based on inspiratory (IN) and expiratory (EX) chest CT images are not sufficiently accurate and efficient in COPD stage detection. The lung region images are autonomously segmented from IN and EX chest CT images to extract the 1 , 781 × 2 lung radiomics and 13 , 824 × 2 3D CNN features. Furthermore, a strategy for concatenating and selecting features was employed in COPD stage detection based on radiomics and 3D CNN features. Finally, we combine all the radiomics, 3D CNN features, and factor risks (age, gender, and smoking history) to detect the COPD stage based on the Auto-Metric Graph Neural Network (AMGNN). The AMGNN with radiomics and 3D CNN features achieves the best performance at 89.7 % of accuracy, 90.9 % of precision, 89.5 % of F1-score, and 95.8 % of AUC compared to six classic machine learning (ML) classifiers. Our proposed approach demonstrates high accuracy in detecting the stage of COPD using both IN and EX chest CT images. This method can potentially establish an efficient diagnostic tool for patients with COPD. Additionally, we have identified radiomics and 3D CNN as more appropriate biomarkers than Parametric Response Mapping (PRM). Moreover, our findings indicate that expiration yields better results than inspiration in detecting the stage of COPD.

Association Between Serum Klotho and Chronic Obstructive Pulmonary Disease in US Middle-Aged and Older Individuals: A Cross-Sectional Study from NHANES 2013-2016

Purpose

This study sought to examine the potential association between serum Klotho levels and the prevalence of COPD in the United States.

Patients and Methods

This study was a cross-sectional analysis involving 4361 adults aged 40-79 years participating in the US National Health and Nutrition Examination Survey (NHANES) conducted between 2013 and 2016. Our investigation utilized multivariate logistic regression and restricted cubic spline (RCS) regression to explore the potential correlation between serum Klotho concentrations and the prevalence of COPD. Additionally, we conducted stratified and interaction analyses to evaluate the consistency and potential modifiers of this relationship.

Results

In this study encompassing 4631 patients (with an average age of 57.6 years, 47.5% of whom were male), 445 individuals (10.2%) were identified as having COPD. In the fully adjusted model, ln-transformed serum Klotho was negatively associated with COPD (OR = 0.71; 95% CI: 0.51-0.99; p = 0.043). Meanwhile, compared with quartile 1, serum Klotho levels in quartiles 2-4 yielded odds ratios (ORs) (95% CI) for COPD were 0.84 (0.63~1.11), 0.76 (0.56~1.02), 0.84 (0.62~1.13), respectively. A negative relationship was observed between the ln-transformed serum Klotho and occurrence of COPD (nonlinear: p = 0.140). the association between ln-transformed serum Klotho and COPD were stable in stratified analyses.

Conclusion

Serum Klotho was negatively associated with the incidence of COPD, when ln-transformed Klotho concentration increased by 1 unit, the risk of COPD was 29% lower.

Early Diagnosis of High-Risk Chronic Obstructive Pulmonary Disease Based on Quantitative High-Resolution Computed Tomography Measurements

Purpose

Quantitative computed tomography (QCT) techniques, focusing on airway anatomy and emphysema, may help to detect early structural changes of COPD disease. This retrospective study aims to identify high-risk COPD participants by using QCT measurements.

Patients and Methods

We enrolled 140 participants from the Second Affiliated Hospital of Shenyang Medical College who completed inspiratory high-resolution CT scans, pulmonary function tests (PFTs), and clinical characteristics recorded. They were diagnosed Non-COPD by PFT value of FEV1/FVC >70% and divided into two groups according percentage predicted FEV1 (FEV1%), low-risk COPD group: FEV1% ≥ 95%, high-risk group: 80% < FEV1% < 95%. The QCT measurements were analyzed by the Student's t-test (or Mann-Whitney U-test) method. Then, feature candidates were identified using the LASSO method. Meanwhile, the correlation between QCT measurements and PFTs was assessed by the Spearman rank correlation test. Furthermore, support vector machine (SVM) was performed to identify high-risk COPD participants. The performance of the models was evaluated in terms of accuracy (ACC), sensitivity (SEN), specificity (SPE), F1-score, and area under the ROC curve (AUC), with p <0.05 considered statistically significant.

Results

The SVM based on QCT measurements achieved good performance in identifying high-risk COPD patients with 85.71% of ACC, 88.34% of SEN, 84.00% of SPE, 83.33% of F1-score, and 0.93 of AUC. Further, QCT measurements integration of clinical data improved the performance with an ACC of 90.48%. The emphysema index (%LAA-950) of left lower lung was negatively correlated with PFTs (P < 0.001). The airway anatomy indexes of lumen diameter (LD) were correlated with PFTs.

Conclusion

QCT measurements combined with clinical information could provide an effective tool for an early diagnosis of high-risk COPD. The QCT indexes can be used to assess the pulmonary function status of high-risk COPD.

Development of machine learning model to predict pulmonary function with low-dose CT-derived parameter response mapping in a community-based chest screening cohort

PURPOSE

To construct and evaluate the performance of a machine learning-based low dose computed tomography (LDCT)-derived parametric response mapping (PRM) model for predicting pulmonary function test (PFT) results.

MATERIALS AND METHODS

A total of 615 subjects from a community-based screening population (40-74 years old) with PFT parameters, including the ratio of the first second forced expiratory volume to forced vital capacity (FEV1/FVC), the percentage of forced expiratory volume in the one second predicted (FEV1%), and registered inspiration-to-expiration chest CT scanning were enrolled retrospectively. Subjects were classified into a normal, high risk, and COPD group based on PFT. Data of 72 PRM-derived quantitative parameters were collected, including volume and volume percentage of emphysema, functional-small airways disease, and normal lung tissue. A machine-learning with random forest regression model and a multilayer perceptron (MLP) model were constructed and tested on PFT prediction, which was followed by evaluation of classification performance based on the PFT predictions.

RESULTS

The machine-learning model based on PRM parameters showed better performance for predicting PFT than MLP, with a coefficient of determination (R2 ) of 0.749 and 0.792 for FEV1/FVC and FEV1%, respectively. The Mean Squared Errors (MSE) for FEV1/FVC and FEV1% are 0.0030 and 0.0097 for the random forest model, respectively. The Root Mean Squared Errors (RMSE) for FEV1/FVC and FEV1% are 0.055 and 0.098, respectively. The sensitivity, specificity, and accuracy for differentiating between the normal group and high-risk group were 34/40 (85%), 65/72 (90%), and 99/112 (88%), respectively. For differentiating between the non-COPD group and COPD group, the sensitivity, specificity, and accuracy were 8/9 (89%), 112/112 (100%), 120/121 (99%), respectively.

CONCLUSIONS

The machine learning-based random forest model predicts PFT results in a community screening population based on PRM, and it identifies high risk COPD from normal populations with high sensitivity and reliably predicts of high-risk COPD.

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.

Quantification of airway wall contrast enhancement on virtual monoenergetic images from spectral computed tomography

OBJECTIVES

Quantitative computed tomography (CT) plays an increasingly important role in phenotyping airway diseases. Lung parenchyma and airway inflammation could be quantified by contrast enhancement at CT, but its investigation by multiphasic examinations is limited. We aimed to quantify lung parenchyma and airway wall attenuation in a single contrast-enhanced spectral detector CT acquisition.

METHODS

For this cross-sectional retrospective study, 234 lung-healthy patients who underwent spectral CT in four different contrast phases (non-enhanced, pulmonary arterial, systemic arterial, and venous phase) were recruited. Virtual monoenergetic images were reconstructed from 40-160 keV, on which attenuations of segmented lung parenchyma and airway walls combined for 5th-10th subsegmental generations were assessed in Hounsfield Units (HU) by an in-house software. The spectral attenuation curve slope between 40 and 100 keV (λHU) was calculated.

RESULTS

Mean lung density was higher at 40 keV compared to that at 100 keV in all groups (p < 0.001). λHU of lung attenuation was significantly higher in the systemic (1.7 HU/keV) and pulmonary arterial phase (1.3 HU/keV) compared to that in the venous phase (0.5 HU/keV) and non-enhanced (0.2 HU/keV) spectral CT (p < 0.001). Wall thickness and wall attenuation were higher at 40 keV compared to those at 100 keV for the pulmonary and systemic arterial phase (p ≤ 0.001). λHU for wall attenuation was significantly higher in the pulmonary arterial (1.8 HU/keV) and systemic arterial (2.0 HU/keV) compared to that in the venous (0.7 HU/keV) and non-enhanced (0.3 HU/keV) phase (p ≤ 0.002).

CONCLUSIONS

Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition, and may separate arterial and venous enhancement. Further studies are warranted to analyze spectral CT for inflammatory airway diseases.

KEY POINTS

• Spectral CT may quantify lung parenchyma and airway wall enhancement with a single contrast phase acquisition. • Spectral CT may separate arterial and venous enhancement of lung parenchyma and airway wall. • The contrast enhancement can be quantified by calculating the spectral attenuation curve slope from virtual monoenergetic images.

GOLD stage-specific phenotyping of emphysema and airway disease using quantitative computed tomography

Background

In chronic obstructive pulmonary disease (COPD) abnormal lung function is related to emphysema and airway obstruction, but their relative contribution in each GOLD-stage is not fully understood. In this study, we used quantitative computed tomography (QCT) parameters for phenotyping of emphysema and airway abnormalities, and to investigate the relative contribution of QCT emphysema and airway parameters to airflow limitation specifically in each GOLD stage.

Methods

Non-contrast computed tomography (CT) of 492 patients with COPD former GOLD 0 COPD and COPD stages GOLD 1-4 were evaluated using fully automated software for quantitative CT. Total lung volume (TLV), emphysema index (EI), mean lung density (MLD), and airway wall thickness (WT), total diameter (TD), lumen area (LA), and wall percentage (WP) were calculated for the entire lung, as well as for all lung lobes separately. Results from the 3rd-8th airway generation were aggregated (WT3-8, TD3-8, LA3-8, WP3-8). All subjects underwent whole-body plethysmography (FEV1%pred, VC, RV, TLC).

Results

EI was higher with increasing GOLD stages with 1.0 ± 1.8% in GOLD 0, 4.5 ± 9.9% in GOLD 1, 19.4 ± 15.8% in GOLD 2, 32.7 ± 13.4% in GOLD 3 and 41.4 ± 10.0% in GOLD 4 subjects (p < 0.001). WP3-8 showed no essential differences between GOLD 0 and GOLD 1, tended to be higher in GOLD 2 with 52.4 ± 7.2%, and was lower in GOLD 4 with 50.6 ± 5.9% (p = 0.010 - p = 0.960). In the upper lobes WP3-8 showed no significant differences between the GOLD stages (p = 0.824), while in the lower lobes the lowest WP3-8 was found in GOLD 0/1 with 49.9 ± 6.5%, while higher values were detected in GOLD 2 with 51.9 ± 6.4% and in GOLD 3/4 with 51.0 ± 6.0% (p < 0.05). In a multilinear regression analysis, the dependent variable FEV1%pred can be predicted by a combination of both the independent variables EI (p < 0.001) and WP3-8 (p < 0.001).

Conclusion

QCT parameters showed a significant increase of emphysema from GOLD 0-4 COPD. Airway changes showed a different spatial pattern with higher values of relative wall thickness in the lower lobes until GOLD 2 and subsequent lower values in GOLD3/4, whereas there were no significant differences in the upper lobes. Both, EI and WP5-8 are independently correlated with lung function decline.

Respiratory microbiota and radiomics features in the stable COPD patients

BACKGROUNDS

The respiratory microbiota and radiomics correlate with the disease severity and prognosis of chronic obstructive pulmonary disease (COPD). We aim to characterize the respiratory microbiota and radiomics features of COPD patients and explore the relationship between them.

METHODS

Sputa from stable COPD patients were collected for bacterial 16 S rRNA gene sequencing and fungal Internal Transcribed Spacer (ITS) sequencing. Chest computed tomography (CT) and 3D-CT analysis were conducted for radiomics information, including the percentages of low attenuation area below - 950 Hounsfield Units (LAA%), wall thickness (WT), and intraluminal area (Ai). WT and Ai were adjusted by body surface area (BSA) to WT/[Formula: see text] and Ai/BSA, respectively. Some key pulmonary function indicators were collected, which included forced expiratory volume in one second (FEV1), forced vital capacity (FVC), diffusion lung carbon monoxide (DLco). Differences and correlations of microbiomics with radiomics and clinical indicators between different patient subgroups were assessed.

RESULTS

Two bacterial clusters dominated by Streptococcus and Rothia were identified. Chao and Shannon indices were higher in the Streptococcus cluster than that in the Rothia cluster. Principal Co-ordinates Analysis (PCoA) indicated significant differences between their community structures. Higher relative abundance of Actinobacteria was detected in the Rothia cluster. Some genera were more common in the Streptococcus cluster, mainly including Leptotrichia, Oribacterium, Peptostreptococcus. Peptostreptococcus was positively correlated with DLco per unit of alveolar volume as a percentage of predicted value (DLco/VA%pred). The patients with past-year exacerbations were more in the Streptococcus cluster. Fungal analysis revealed two clusters dominated by Aspergillus and Candida. Chao and Shannon indices of the Aspergillus cluster were higher than that in the Candida cluster. PCoA showed distinct community compositions between the two clusters. Greater abundance of Cladosporium and Penicillium was found in the Aspergillus cluster. The patients of the Candida cluster had upper FEV1 and FEV1/FVC levels. In radiomics, the patients of the Rothia cluster had higher LAA% and WT/[Formula: see text] than those of the Streptococcus cluster. Haemophilus, Neisseria and Cutaneotrichosporon positively correlated with Ai/BSA, but Cladosporium negatively correlated with Ai/BSA.

CONCLUSIONS

Among respiratory microbiota in stable COPD patients, Streptococcus dominance was associated with an increased risk of exacerbation, and Rothia dominance was relevant to worse emphysema and airway lesions. Peptostreptococcus, Haemophilus, Neisseria and Cutaneotrichosporon probably affected COPD progression and potentially could be disease prediction biomarkers.

Reproducibility of pulmonary magnetic resonance angiography in adults with muco-obstructive pulmonary disease

BACKGROUND

Recent studies support magnetic resonance angiography (MRA) as a diagnostic tool for pulmonary arterial disease.

PURPOSE

To determine MRA image quality and reproducibility, and the dependence of MRA image quality and reproducibility on disease severity in patients with chronic obstructive pulmonary disease (COPD) and cystic fibrosis (CF).

MATERIAL AND METHODS

Twenty patients with COPD (mean age 66.5 ± 8.9 years; FEV1% = 42.0 ± 13.3%) and 15 with CF (mean age 29.3 ± 9.3 years; FEV1% = 66.6 ± 15.8%) underwent morpho-functional chest magnetic resonance imaging (MRI) including time-resolved MRA twice one month apart (MRI1, MRI2), and COPD patients underwent non-contrast computed tomography (CT). Image quality was assessed visually using standardized subjective 5-point scales. Contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) were measured by regions of interest. Disease severity was determined by spirometry, a well-evaluated chest MRI score, and by computational CT emphysema index (EI) for COPD.

RESULTS

Subjective image quality was diagnostic for all MRA at MRI1 and MRI2 (mean score = 4.7 ± 0.6). CNR and SNR were 4 43.8 ± 8.7 and 50.5 ± 8.7, respectively. Neither image quality score nor CNR or SNR correlated with FEV1% or chest MRI score for COPD and CF (r = 0.239-0.248). CNR and SNR did not change from MRI1 to MRI2 (P = 0.434-0.995). Further, insignificant differences in CNR and SNR between MRA at MRI1 and MRI2 did not correlate with FEV1% nor chest MRI score in COPD and CF (r = -0.238-0.183), nor with EI in COPD (r = 0.100-0.111).

CONCLUSION

MRA achieved diagnostic quality in COPD and CF patients and was highly reproducible irrespective of disease severity. This supports MRA as a robust alternative to CT in patients with underlying muco-obstructive lung disease.

Quantitative CT analysis of lung parenchyma to improve malignancy risk estimation in incidental pulmonary nodules

Objectives

To assess the value of quantitative computed tomography (QCT) of the whole lung and nodule-bearing lobe regarding pulmonary nodule malignancy risk estimation.

Methods

A total of 251 subjects (median [IQR] age, 65 (57–73) years; 37% females) with pulmonary nodules on non-enhanced thin-section CT were retrospectively included. Twenty percent of the nodules were malignant, the remainder benign either histologically or at least 1-year follow-up. CT scans were subjected to in-house software, computing parameters such as mean lung density (MLD) or peripheral emphysema index (pEI). QCT variable selection was performed using logistic regression; selected variables were integrated into the Mayo Clinic and the parsimonious Brock Model.

Results

Whole-lung analysis revealed differences between benign vs. malignant nodule groups in several parameters, e.g. the MLD (−766 vs. −790 HU) or the pEI (40.1 vs. 44.7 %). The proposed QCT model had an area-under-the-curve (AUC) of 0.69 (95%-CI, 0.62−0.76) based on all available data. After integrating MLD and pEI into the Mayo Clinic and Brock Model, the AUC of both clinical models improved (AUC, 0.91 to 0.93 and 0.88 to 0.91, respectively). The lobe-specific analysis revealed that the nodule-bearing lobes had less emphysema than the rest of the lung regarding benign (EI, 0.5 vs. 0.7 %; p < 0.001) and malignant nodules (EI, 1.2 vs. 1.7 %; p = 0.001).

Conclusions

Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant; hereby the nodule-bearing lobes have less emphysema. QCT variables could improve the risk assessment of incidental pulmonary nodules.

Key Points

• Nodules in subjects with higher whole-lung metrics of emphysema and less fibrosis are more likely to be malignant.

• The nodule-bearing lobes have less emphysema compared to the rest of the lung.

• QCT variables could improve the risk assessment of incidental pulmonary nodules.

AI-Supported Comprehensive Detection and Quantification of Biomarkers of Subclinical Widespread Diseases at Chest CT for Preventive Medicine

Automated image analysis plays an increasing role in radiology in detecting and quantifying image features outside of the perception of human eyes. Common AI-based approaches address a single medical problem, although patients often present with multiple interacting, frequently subclinical medical conditions. A holistic imaging diagnostics tool based on artificial intelligence (AI) has the potential of providing an overview of multi-system comorbidities within a single workflow. An interdisciplinary, multicentric team of medical experts and computer scientists designed a pipeline, comprising AI-based tools for the automated detection, quantification and characterization of the most common pulmonary, metabolic, cardiovascular and musculoskeletal comorbidities in chest computed tomography (CT). To provide a comprehensive evaluation of each patient, a multidimensional workflow was established with algorithms operating synchronously on a decentralized Joined Imaging Platform (JIP). The results of each patient are transferred to a dedicated database and summarized as a structured report with reference to available reference values and annotated sample images of detected pathologies. Hence, this tool allows for the comprehensive, large-scale analysis of imaging-biomarkers of comorbidities in chest CT, first in science and then in clinical routine. Moreover, this tool accommodates the quantitative analysis and classification of each pathology, providing integral diagnostic and prognostic value, and subsequently leading to improved preventive patient care and further possibilities for future studies.

Automated quantification of airway wall thickness on chest CT using retina U-Nets - Performance evaluation and application to a large cohort of chest CTs of COPD patients

PURPOSE

Airway wall thickening is a consequence of chronic inflammatory processes and usually only qualitatively described in CT radiology reports. The purpose of this study is to automatically quantify airway wall thickness in multiple airway generations and assess the diagnostic potential of this parameter in a large cohort of patients with Chronic Obstructive Pulmonary Disease (COPD).

MATERIALS AND METHODS

This retrospective, single-center study included a series of unenhanced chest CTs. Inclusion criteria were the mentioning of an explicit COPD GOLD stage in the written radiology report and time period (01/2019-12/2021). A control group included chest CTs with completely unremarkable lungs according to the report. The DICOM images of all cases (axial orientation; slice-thickness: 1 mm; soft-tissue kernel) were processed by an AI algorithm pipeline consisting of (A) a 3D-U-Net for det detection and tracing of the bronchial tree centerlines (B) extraction of image patches perpendicular to the centerlines of the bronchi, and (C) a 2D U-Net for segmentation of airway walls on those patches. The performance of centerline detection and wall segmentation was assessed. The imaging parameter average wall thickness was calculated for bronchus generations 3-8 (AWT_3-8) across the lungs. Mean AWT_3-8 was compared between five groups (control, COPD Gold I-IV) using non-parametric statistics. Furthermore, the established emphysema score %LAV-950 was calculated and used to classify scans (normal vs. COPD) alone and in combination with AWT_3-8. RESULTS: A total of 575 chest CTs were processed. Algorithm performance was very good (airway centerline detection sensitivity: 86.9%; airway wall segmentation Dice score: 0.86). AWT_3-8 was statistically significantly greater in COPD patients compared to controls (2.03 vs. 1.87 mm, p < 0.001) and increased with COPD stage. The classifier that combined %LAV-950 and AWT_3-8 was superior to the classifier using only %LAV-950 (AUC = 0.92 vs. 0.79).

CONCLUSION

Airway wall thickness increases in patients suffering from COPD and is automatically quantifiable. AWT_3-8 could become a CT imaging parameter in COPD complementing the established emphysema biomarker %LAV-950.

CLINICAL RELEVANCE STATEMENT

Quantitative measurements considering the complete visible bronchial tree instead of qualitative description could enhance radiology reports, allow for precise monitoring of disease progression and diagnosis of early stages of disease.

Deep Learning-Based Computed Tomography Features in Evaluating Early Screening and Risk Factors for Chronic Obstructive Pulmonary Disease

This research aimed to investigate the diagnostic effect of computed tomography (CT) images based on a deep learning double residual convolution neural network (DRCNN) model on chronic obstructive pulmonary disease (COPD) and the related risk factors for COPD. The questionnaire survey was conducted among 980 permanent residents aged ≥ 40 years old. Among them, 84 patients who were diagnosed with COPD and volunteered to participate in the experiment and 25 healthy people were selected as the research subjects, and all of them underwent CT imaging scans. At the same time, an image noise reduction model based on the DRCNN was proposed to process CT images. The results showed that 84 of 980 subjects were diagnosed with COPD, and the overall prevalence of COPD in this epidemiological survey was 8.57%. Multivariate logistic regression model analysis showed that the regression coefficients of COPD with age, family history of COPD, and smoking were 0.557, 0.513, and 0.717, respectively (P < 0.05). The diagnostic sensitivity, specificity, and accuracy of DRCNN-based CT for COPD were greatly superior to those of single CT and the difference was considerable (P < 0.05). In summary, advanced age, family history of COPD, and smoking were independent risk factors for COPD. CT based on the DRCNN model can improve the diagnostic accuracy of simple CT images for COPD and has good performance in the early screening of COPD.

Quantitative Computed Tomography: What Clinical Questions Can it Answer in Chronic Lung Disease?

Quantitative computed tomography (QCT) has recently gained an important role in the functional assessment of chronic lung disease. Its capacity in diagnostic, staging, and prognostic evaluation in this setting is similar to that of traditional pulmonary function testing. Furthermore, it can demonstrate lung injury before the alteration of pulmonary function test parameters, and it enables the classification of disease phenotypes, contributing to the customization of therapy and performance of comparative studies without the intra- and inter-observer variation that occurs with qualitative analysis. In this review, we address technical issues with QCT analysis and demonstrate the ability of this modality to answer clinical questions encountered in daily practice in the management of patients with chronic lung disease.

µCT to quantify muco-obstructive lung disease and effects of neutrophil elastase knockout in mice

Muco-obstructive lung diseases are characterized by airway obstruction and hyperinflation, which can be quantified by imaging. Our aim was to evaluate µCT for longitudinal quantification of muco-obstructive lung disease in β-epithelial Na⁺ channel overexpressing (Scnn1b-TG) mice and of the effects of neutrophil elastase (NE) knockout on its progression. Lungs from wild-type (WT), NE^-/-, Scnn1b-TG, and Scnn1b-TG/NE^-/- mice were scanned with 9 µm resolution at 0, 5, 14 and 60 days of age, and airway and parenchymal disease was quantified. Mucus adhesion lesions (MAL) were persistently increased in Scnn1b-TG compared to WT mice from 0 days (20.25±6.50 vs. 9.60±2.07, P<0.05), and this effect was attenuated in Scnn1b-TG/NE^-/- mice (5.33±3.67, P<0.001). Airway wall area percentage (WA%) was increased in Scnn1b-TG mice compared to WT from 14 days onward (59.2±6.3% vs. 49.8±9.0%, P<0.001) but was similar in Scnn1b-TG/NE^-/- compared to WT at 60 days (46.4±9.2% vs. 45.4±11.5%, P=0.97). Air proportion (Air%) and mean linear intercept (Lm) were persistently increased in Scnn1b-TG compared to WT from 5 days on (53.9±4.5% vs. 30.0±5.5% and 78.82±8.44µm vs. 65.66±4.15µm, respectively, P<0.001), whereas in Scnn1b-TG/NE^-/- Air% and Lm were similar to WT from birth (27.7±5.5% vs.27.2±5.9%, P =0.92 and 61.48±9.20µm vs. 61.70±6.73µm, P=0.93, respectively). Our results suggest that µCT is sensitive to detect the onset and progression of muco-obstructive lung disease and effects of genetic deletion of NE on morphology of airways and lung parenchyma in Scnn1b-TG mice, and that it may serve as a sensitive endpoint for preclinical studies of novel therapeutic interventions for muco-obstructive lung diseases.

Association of acrylamide hemoglobin biomarkers with chronic obstructive pulmonary disease in the general population in the US: NHANES 2013-2016

Background: Acrylamide is a well-known potential carcinogenic compound formed as an intermediate in the Maillard reaction during heat treatment, mainly from high-temperature frying, and is found in baked goods and coffee, as well as resulting from water treatment, textiles and paper processing. The effects of acrylamide on lung disease in humans remains unclear. We aimed to investigate the association between blood acrylamide and glycidamide and chronic obstructive pulmonary disease (COPD) in the United States of America (U.S.) population using PROC logistic regression models. Results: 2744 participants aged 20 to 80 from the 2013-2016 National Health and Nutrition Examination Survey (NHANES) were enrolled. After adjusting for demographic data, health factors and serum cotinine, the ratio of HbGA to HbAA (HbGA/HbAA) significantly increased the risk of COPD (P for trend = 0.022). The odds ratio (OR) with a 95% confidence interval (95% CI) for HbGA/HbAA in the third tile was 2.45 (1.12-5.31), compared with the lowest tile. The restricted cubic spline (RCS) curve showed a positive linear correlation between the log (HbGA/HbAA) and the risk of COPD (P = 0.030). Conclusion: The ratio of glycidamide and acrylamide (HbGA/HbAA) was associated with COPD. This association was more prominent in males, obese individuals, people with a poverty income ratio (PIR) < 1.85 or people who never exercise. However, null associations were observed between HbAA, HbGA and HbAA + HbGA, and COPD.

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.

Computed tomography-identified phenotypes of small airway obstructions in chronic obstructive pulmonary disease

ABSTRACT

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease characteristic of small airway inflammation, obstruction, and emphysema. It is well known that spirometry alone cannot differentiate each separate component. Computed tomography (CT) is widely used to determine the extent of emphysema and small airway involvement in COPD. Compared with the pulmonary function test, small airway CT phenotypes can accurately reflect disease severity in patients with COPD, which is conducive to improving the prognosis of this disease. CT measurement of central airway morphology has been applied in clinical, epidemiologic, and genetic investigations as an inference of the presence and severity of small airway disease. This review will focus on presenting the current knowledge and methodologies in chest CT that aid in identifying discrete COPD phenotypes.

A pilot study: Quantify lung volume and emphysema extent directly from two-dimensional scout images

PURPOSE

The potential to compute volume metrics of emphysema from planar scout images was investigated in this study. The successful implementation of this concept will have a wide impact in different fields, and specifically, maximize the diagnostic potential of the planar medical images.

METHODS

We investigated our premise using a well-characterized chronic obstructive pulmonary disease (COPD) cohort. In this cohort, planar scout images from computed tomography (CT) scans were used to compute lung volume and percentage of emphysema. Lung volume and percentage of emphysema were quantified on the volumetric CT images and used as the "ground truth" for developing the models to compute the variables from the corresponding scout images. We trained two classical convolutional neural networks (CNNs), including VGG19 and InceptionV3, to compute lung volume and the percentage of emphysema from the scout images. The scout images (n = 1,446) were split into three subgroups: (1) training (n = 1,235), (2) internal validation (n = 99), and (3) independent test (n = 112) at the subject level in a ratio of 8:1:1. The mean absolute difference (MAD) and R-square (R2) were the performance metrics to evaluate the prediction performance of the developed models.

RESULTS

The lung volumes and percentages of emphysema computed from a single planar scout image were significantly linear correlated with the measures quantified using volumetric CT images (VGG19: R2 = 0.934 for lung volume and R2 = 0.751 for emphysema percentage, and InceptionV3: R2 = 0.977 for lung volume and R2 = 0.775 for emphysema percentage). The mean absolute differences (MADs) for lung volume and percentage of emphysema were 0.302 ± 0.247L and 2.89 ± 2.58%, respectively, for VGG19, and 0.366 ± 0.287L and 3.19 ± 2.14, respectively, for InceptionV3.

CONCLUSIONS

Our promising results demonstrated the feasibility of inferring volume metrics from planar images using CNNs.

Serum Concentration of the Phytohormone Abscisic Acid Is Associated With Immune-Regulatory Mediators and Is a Potential Biomarker of Disease Severity in Chronic Obstructive Pulmonary Disease

COPD and asthma are two distinct but sometimes overlapping diseases exhibiting varying degrees and types of inflammation on different stages of the disease. Although several biomarkers are defined to estimate the inflammatory endotype and stages in these diseases, there is still a need for new markers and potential therapeutic targets. We investigated the levels of a phytohormone, abscisic acid (ABA) and its receptor, LANCL2, in COPD patients and asthmatics. In addition, PPAR-γ that is activated by ABA in a ligand-binding domain-independent manner was also included in the study. In this study, we correlated ABA with COPD-propagating factors to define the possible role of ABA, in terms of immune regulation, inflammation, and disease stages. We collected blood from 101 COPD patients, 52 asthmatics, and 57 controls. Bronchoscopy was performed on five COPD patients and 29 controls. We employed (i) liquid chromatography–tandem mass spectrometry and HPLC to determine the ABA and indoleamine 2,3-dioxygenase levels, respectively; (ii) real-time PCR to quantify the gene expression of LANCL2 and PPAR-γ; (iii) Flow cytometry to quantify adipocytokines; and (iv) immunoturbidimetry and ELISA to measure CRP and cytokines, respectively. Finally, a multinomial regression model was used to predict the probability of using ABA as a biomarker. Blood ABA levels were significantly reduced in COPD patients and asthmatics compared to age- and gender-matched normal controls. However, PPAR-γ was elevated in COPD patients. Intriguingly, ABA was positively correlated with immune-regulatory factors and was negatively correlated with inflammatory markers, in COPD. Of note, ABA was increased in advanced COPD stages. We thereby conclude that ABA might be involved in regulation of COPD pathogenesis and might be regarded as a potential biomarker for COPD stages.

Comparison of histological and computed tomographic measurements of pig lung bronchi

AIM

Light microscopy is used as template in the evaluation and further development of medical imaging methods. Tissue shrinkage caused by histological processing is known to influence lung tissue dimensions. In diagnosis of COPD, computed tomography (CT) is widely used for automated airway measurement. The aim of this study was to compare histological and computed tomographic measurements of pig lung bronchi.

METHODS

Airway measurements of pig lungs were performed after freezing under controlled inflation pressure in a liquid nitrogen bath. The wall thickness of seven bronchi was measured via Micro-CT and CT using the integral-based method (IBM) and the full-width-at-half-maximum method (FWHM) automatically and histologically on frozen and paraffin sections. Statistical analysis was performed using the Wilcoxon test, Pearson's correlation coefficient with a significance level at p<0.05, scatter plots and Bland-Altman plots.

RESULTS

Bronchial wall thickness was smallest in frozen sections (median 0.71 mm) followed by paraffin sections (median 0.75 mm), Micro-CT (median 0.84 mm), and CT measurements using IBM (median 0.68 mm) and FWHM (median 1.69 mm). Statistically significant differences were found among all tested groups (p<0.05) except for CT IBM and paraffin and frozen sections and Micro-CT. There was high correlation between all parameters with statistical significance (p<0.05).

CONCLUSIONS

Significant differences in airway measurement were found among the different methods. The absolute measurements with CT IBM were closest to the histological results followed by Micro-CT, whereas CT FWHM demonstrated a distinct divergence from the other groups.

Optimizing airway wall segmentation and quantification by reducing the influence of adjacent vessels and intravascular contrast material with a modified integral-based algorithm in quantitative computed tomography

Introduction

Quantitative analysis of multi-detector computed tomography (MDCT) plays an increasingly important role in assessing airway disease. Depending on the algorithms used, airway dimensions may be over- or underestimated, primarily if contrast material was used. Therefore, we tested a modified integral-based method (IBM) to address this problem.

Methods

Temporally resolved cine-MDCT was performed in seven ventilated pigs in breath-hold during iodinated contrast material (CM) infusion over 60s. Identical slices in non-enhanced (NE), pulmonary-arterial (PA), systemic-arterial (SA), and venous phase (VE) were subjected to an in-house software using a standard and a modified IBM. Total diameter (TD), lumen area (LA), wall area (WA), and wall thickness (WT) were measured for ten extra- and six intrapulmonary airways.

Results

The modified IBM significantly reduced TD by 7.6%, LA by 12.7%, WA by 9.7%, and WT by 3.9% compared to standard IBM on non-enhanced CT (p<0.05). Using standard IBM, CM led to a decrease of all airway parameters compared to NE. For example, LA decreased from 80.85±49.26mm² at NE, to 75.14±47.96mm² (-7.1%) at PA (p<0.001), 74.96±48.55mm² (-7.3%) at SA (p<0.001), and to 78.95±48.94mm² (-2.4%) at VE (p = 0.200). Using modified IBM, the differences were reduced to -3.1% at PA, -2.9% at SA and -0.7% at VE (p<0.001; p<0.001; p = 1.000).

Conclusions

The modified IBM can optimize airway wall segmentation and reduce the influence of CM on quantitative CT. This allows a more precise measurement as well as potentially the comparison of enhanced with non-enhanced scans in inflammatory airway disease.