Optimal threshold in CT quantification of emphysema

Prediction of acute lung injury assessed by chest computed tomography, oxygen saturation/fraction of inspired oxygen ratio, and serum lactate dehydrogenase in patients with COVID-19

INTRODUCTION

In treating acute hypoxemic respiratory failure (AHRF) caused by coronavirus disease 2019 (COVID-19), clinicians choose respiratory therapies such as low-flow nasal cannula oxygenation, high-flow nasal cannula oxygenation, or mechanical ventilation after assessment of the patient's condition. Chest computed tomography (CT) imaging contributes significantly to diagnosing COVID-19 pneumonia. However, the costs and potential harm to patients from radiation exposure need to be considered. This study was performed to predict the quantitative extent of COVID-19 acute lung injury using clinical indicators such as an oxygenation index and blood test results.

METHODS

We analyzed data from 192 patients with COVID-19 AHRF. Multiple logistic regression was used to determine correlations between the lung infiltration volume (LIV) and other pathophysiological or biochemical laboratory parameters.

RESULTS

Among 13 clinical parameters, we identified the oxygen saturation/fraction of inspired oxygen ratio (SF ratio) and serum lactate dehydrogenase (LD) concentration as factors associated with the LIV. In the binary classification of an LIV of ≥20 % or not and with the borderline LD = 2.2 × [SF ratio]-182.4, the accuracy, precision, diagnostic odds ratio, and area under the summary receiver operating characteristic curve were 0.828, 0.818, 23.400, and 0.870, respectively.

CONCLUSIONS

These data suggest that acute lung injury due to COVID-19 pneumonia can be estimated using the SF ratio and LD concentration without a CT scan. These findings may provide significant clinical benefit by allowing clinicians to predict acute lung injury levels using simple, minimally invasive assessment of oxygenation capacity and biochemical blood tests.

Artificial Intelligence-Aided Selection of Needle Pathways: Proof-of-Concept in Percutaneous Lung Biopsies

PURPOSE

To evaluate the concordance between lung biopsy puncture pathways determined by artificial intelligence (AI) and those determined by expert physicians.

MATERIALS AND METHODS

An AI algorithm was created to choose optimal lung biopsy pathways based on segmented thoracic anatomy and emphysema in volumetric lung computed tomography (CT) scans combined with rules derived from the medical literature. The algorithm was validated using pathways generated from CT scans of randomly selected patients (n = 48) who had received percutaneous lung biopsies and had noncontrast CT scans of 1.25-mm thickness available in picture archiving and communication system (PACS) (n = 28, mean age, 68.4 years ± 9.2; 12 women, 16 men). The algorithm generated 5 potential pathways per scan, including the computer-selected best pathway and 4 random pathways (n = 140). Four experienced physicians rated each pathway on a 1-5 scale, where scores of 1-3 were considered safe and 4-5 were considered unsafe. Concordance between computer and physician ratings was assessed using Cohen's κ.

RESULTS

The algorithm ratings were statistically equivalent to the physician ratings (safe vs unsafe: κ¯=0.73; ordinal scale: κ¯=0.62). The computer and physician ratings were identical in 57.9% (81/140) of cases and differed by a median of 0 points. All least-cost "best" pathways generated by the algorithm were considered safe by both computer and physicians (28/28) and were judged by physicians to be ideal or near ideal.

CONCLUSIONS

AI-generated lung biopsy puncture paths were concordant with expert physician reviewers and considered safe. A prospective comparison between computer- and physician-selected puncture paths appears indicated in addition to expansion to other anatomic locations and procedures.

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

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

Clinical and Radiological Features Between Patients with Stable COPD from Plateau and Flatlands: A Comparative Study

Background

COPD patients living in Tibet are exposed to specific environments and different risk factors and probably have different characteristics of COPD from those living in flatlands. We aimed to describe the distinction between stable COPD patients permanently residing at the Tibet plateau and those in flatlands.

Methods

We conducted an observational cross-sectional study that enrolled stable COPD patients from Tibet Autonomous Region People's Hospital (Plateau Group) and Peking University Third Hospital (Flatland Group), respectively. Their demographic information, clinical features, spirometry test, blood routine and high-resolution chest CT were collected and evaluated.

Results

A total of 182 stable COPD patients (82 from plateau and 100 from flatland) were consecutively enrolled. Compared to those in flatlands, patients in plateau had a higher proportion of females, more biomass fuel use and less tobacco exposure. CAT score and frequency of exacerbation in the past year were higher in plateau patients. The blood eosinophil count was lower in plateau patients, with fewer patients having an eosinophil count ≥300/μL. On CT examination, the proportions of previous pulmonary tuberculosis and bronchiectasis were higher in plateau patients, but emphysema was less common and milder. The ratio of diameters of pulmonary artery to aorta ≥1 was more often in plateau patients.

Conclusion

Patients with COPD living at Tibet Plateau had a heavier respiratory burden, lower blood eosinophil count, less emphysema but more bronchiectasis and pulmonary hypertension. Biomass exposure and previous tuberculosis were more common in these patients.

Prediction of exercise-induced desaturation in COPD patients without resting hypoxemia: a retrospective study

Background

There is no universally accepted criterion for assessing exercise-induced desaturation (EID). The purpose of this study is to compare the two methods regularly used for determining EID in COPD patients, as well as to explore the risk factors and predictors related to EID.

Methods

The 6MWT was performed with continuous SpO₂ monitoring on patients with stable COPD. Using two methods (method A: “SpO_2rest–SpO_2min ≥ 4% and/or SpO_2min < 90%”, method B: “SpO_2rest–SpO_2end ≥ 4% and/or SpO_2end < 90%”) as EID determination criteria to assess the incidence of EID. The differences and consistency of the two methods are compared. Moreover, we collected data through the pulmonary function test, mMRC dyspnea score, COPD assessment test, BODE index and CT-defined emphysema. Univariate and multivariate logistic regression analyses were used to identify factors affecting the EID. For the parameters that predict EID in 6MWT, a receiver operating characteristic (ROC) curve analysis was employed.

Results

The analysis included 124 patients. The overall incidence of EID was 62.1% by using method A as the criterion and 51.6% by method B. All of the EID patients found by method B were included in the EID patients identified by method A, as well as 13 new-EID patients. The difference in diagnostic outcomes between the two approaches was not statistically significant (P > 0.05), but they were in excellent agreement (Kappa = 0.807, P = 0.001). Logistic regression analyses found that D_LCO SB% pred, D_LCO/VA% pred, CAT score, mean density, PD15, emphysema volume and %LAA were significant determinants of the EID. For predicting EID, the ROC analysis produced AUC and cutoffs of 0.689 and 50.45% (D_LCO SB% pred), 0.707 and 75.0% (D_LCO/VA% pred), 0.727 and 15 points (CAT score), 0.691 and − 955.00HU (PD15), 0.671 and − 856.46HU (mean density), 0.668 and 338.14 ml (emphysema volume) and 0.656 and 7.63% (%LAA), respectively.

Conclusions

Two methods evaluating EID in this research are in a good agreement, method A can find more EID patients by focusing on SpO_2min. When conditions are constrained, it is also sufficient to assess EID in COPD patients by method B. In terms of the predictors of EID, D_LCO SB% pred, D_LCO/VA% pred, CAT score and CT-defined emphysema are all statistically significant test variables to determine EID.

How Artificial Intelligence in Imaging Can Better Serve Patients with Bronchial and Parenchymal Lung Diseases?

With the rapid development of computing today, artificial intelligence has become an essential part of everyday life, with medicine and lung health being no exception. Big data-based scientific research does not mean simply gathering a large amount of data and letting the machines do the work by themselves. Instead, scientists need to identify problems whose solution will have a positive impact on patients’ care. In this review, we will discuss the role of artificial intelligence from both physiological and anatomical standpoints, starting with automatic quantitative assessment of anatomical structures using lung imaging and considering disease detection and prognosis estimation based on machine learning. The evaluation of current strengths and limitations will allow us to have a broader view for future developments.

Performance of virtual non-contrast images generated on clinical photon-counting detector CT for emphysema quantification: proof of concept

OBJECTIVE

To evaluate the performance of virtual non-contrast images (VNC) compared to true non-contrast (TNC) images in photon-counting detector computed tomography (PCD-CT) for the evaluation of lung parenchyma and emphysema quantification.

METHODS

65 (mean age 73 years; 48 male) consecutive patients who underwent a three-phase (non-contrast, arterial and venous) chest/abdomen CT on a first-generation dual-source PCD-CT were retrospectively included. Scans were performed in the multienergy (QuantumPlus) mode at 120 kV with 70 ml intravenous contrast agent at an injection rate of 4 ml s-1. VNC were reconstructed from the arterial (VNCart) and venous phase (VNCven). TNC and VNC images of the lung were assessed quantitatively by calculating the global noise index (GNI) and qualitatively by two independent, blinded readers (overall image quality and emphysema assessment). Emphysema quantification was performed using a commercially available software tool at a threshold of -950 HU for all data sets. TNC images served as reference standard for emphysema quantification. Low attenuation values (LAV) were compared in a Bland-Altman plot.

RESULTS

GNI was similar in VNCart (103.0 ± 30.1) and VNCven (98.2 ± 22.2) as compared to TNC (100.9 ± 19.0, p = 0.546 and p = 0.272, respectively). Subjective image quality (emphysema assessment and overall image quality) was highest for TNC (p = 0.001), followed by VNCven and VNCart. Both, VNCart and VNCven showed no significant difference in emphysema quantification as compared to TNC (p = 0.409 vs. p = 0.093; respectively).

CONCLUSION

Emphysema evaluation is feasible using virtual non-contrast images from PCD-CT.

ADVANCES IN KNOWLEDGE

Emphysema quantification is feasible and accurate using VNC images in PCD-CT. Based on these findings, additional TNC scans for emphysema quantification could be omitted in the future.

Factors associated with coronary heart disease in COPD patients and controls

Background

COPD and coronary heart disease (CHD) frequently co-occur, yet which COPD phenotypes are most prone to CHD is poorly understood. The aim of this study was to see whether COPD patients did have a true higher risk for CHD than subjects without COPD, and to examine a range of potential factors associated with CHD in COPD patients and controls.

Methods

347 COPD patients and 428 non-COPD controls, were invited for coronary computed tomography angiography (CCTA) and pulmonary CT. Arterial blood gas, bioelectrical impedance and lung function was measured, and a detailed medical history taken. The CCTA was evaluated for significant coronary stenosis and calcium score (CaSc), and emphysema defined as >10% of total area <-950 Hounsfield units.

Results

12.6% of the COPD patients and 5.7% of the controls had coronary stenosis (p<0.01), whereas 55.9% of the COPD patients had a CaSc>100 compared to 31.6% of the controls (p<0.01). In a multivariable model adjusting for sex, age, body composition, pack-years, CRP, cholesterol/blood pressure lowering medication use and diabetes mellitus, the OR (95% CI) for having significant stenosis was 1.80 (0.86–3.78) in COPD patients compared with controls. In a similar model, the OR (95% CI) for having CaSc>100 was 1.68 (1.12–2.53) in COPD patients compared with controls. Examining the risk of significant stenosis and CaSc>100 among COPD patients, no variable was associated with significant stenosis, whereas male sex [OR 2.85 (1.56–5.21)], age [OR 3.74 (2.42–5.77)], statin use [OR 2.23 (1.23–4.50)] were associated with CaSc>100, after adjusting for body composition, pack-years, C-reactive protein, use of angiotensin converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs), diabetes, emphysema score, GOLD category, exacerbation frequency, eosinophilia, and hypoxemia.

Conclusion

COPD patients were more likely to have CHD, but neither emphysema score, lung function, exacerbation frequency, nor hypoxemia predicted presence of either coronary stenosis or CaSc>100.

Utility of Noncancerous Chest CT Features for Predicting Overall Survival and Noncancer Death in Patients With Stage I Lung Cancer Treated With Stereotactic Body Radiotherapy

Background: Noncancerous imaging markers can be readily derived from pretreatment diagnostic and radiotherapy planning chest CT examinations. Objective: To explore the ability of noncancerous features on chest CT to predict overall survival (OS) and noncancer-related death in patients with stage I lung cancer treated with stereotactic body radiation therapy (SBRT). Methods: This retrospective study included 282 patients (168 female, 114 male; median age, 75 years) with stage I lung cancer treated with SBRT between January 2009 and June 2017. Pretreatment chest CT was used to quantify coronary artery calcium (CAC) score, pulmonary artery (PA)-to-aorta ratio, emphysema, and body composition in terms of the cross-sectional area and attenuation of skeletal muscle and subcutaneous adipose tissue at the T5, T8, and T10 vertebral levels. Associations of clinical and imaging features with OS were quantified using a multivariable Cox proportional hazards (PH) model. Penalized multivariable Cox PH models to predict OS were constructed using clinical features only and using both clinical and imaging features. Models' discriminatory ability was assessed by constructing time-varying ROC curves and computing AUC at prespecified times. Results: After a median OS of 60.8 months (95% CI 55.8-68.9), 148 (52.5%) patients died, including 83 (56.1%) with noncancer deaths. Higher CAC score (11-399: hazard ratio [HR] 1.83 [95% CI 1.15-2.91], P=.01; ≥400: HR 1.63 [95% CI 1.01-2.63], P=.04), higher PA-to-aorta ratio (HR 1.33 [95% CI 1.16-1.52], P<.001, per 0.1-unit increase), and lower thoracic skeletal muscle index (HR 0.88 [95% CI 0.79-0.98], P=.02, per 10 cm²/m² increase) were independently associated with shorter OS. Discriminatory ability for 5-year OS was greater for the model including clinical and imaging features than for the model including clinical features only (AUC, 0.75 [95% CI 0.68-0.83] versus 0.61 [95% CI 0.53-0.70], p < .01). The model's most important clinical or imaging feature based on mean standardized regression coefficients was the PA-to-aorta ratio. Conclusions: In patients undergoing SBRT for stage I lung cancer, higher CAC score, higher PA-to-aorta ratio, and lower thoracic skeletal muscle index independently predicted worse OS. Clinical Impact: Noncancerous imaging features on chest CT performed before SBRT improve survival prediction compared with clinical features alone.

Computed tomography on lung cancer screening is useful for adjuvant comorbidity diagnosis in developing countries

Purpose

The aim of this study was to analyse and quantify the prevalence of six comorbidities from lung cancer screening (LCS) on computed tomography (CT) scans of patients from developing countries.

Methods

For this retrospective study, low-dose CT scans (n=775) were examined from patients who underwent LCS in a tertiary hospital between 2016 and 2020. An age- and sex-matched control group was obtained for comparison (n=370). Using the software, coronary artery calcification (CAC), the skeletal muscle area, interstitial lung abnormalities, emphysema, osteoporosis and hepatic steatosis were accessed. Clinical characteristics of each participant were identified. A t-test and Chi-squared test were used to examine differences between these values. Interclass correlation coefficients (ICCs) and interobserver agreement (assessed by calculating kappa coefficients) were calculated to assess the correlation of measures interpreted by two observers. p-values <0.05 were considered significant.

Results

One or more comorbidities were identified in 86.6% of the patients and in 40% of the controls. The most prevalent comorbidity was osteoporosis, present in 44.2% of patients and in 24.8% of controls. New diagnoses of cardiovascular disease, emphysema and osteoporosis were made in 25%, 7% and 46% of cases, respectively. The kappa coefficient for CAC was 0.906 (p<0.001). ICCs for measures of liver, spleen and bone density were 0.88, 0.93 and 0.96, respectively (p<0.001).

Conclusions

CT data acquired during LCS led to the identification of previously undiagnosed comorbidities. The LCS is useful to facilitate comorbidity diagnosis in developing countries, providing opportunities for its prevention and treatment.

Lung cancer screening is useful to facilitate comorbidity diagnosis in developing countries, providing opportunities for its prevention and treatmenthttps://bit.ly/3KEdGuW

Association between plasma sRAGE and emphysema according to the genotypes of AGER gene

Background

Higher soluble receptor for advanced glycation end product (sRAGE) levels are considered to be associated with severe emphysema. However, the relationship remains uncertain when the advanced glycation end-product specific receptor (AGER) gene is involved. We aimed to analyse the association between sRAGE levels and emphysema according to the genotypes of rs2070600 in the AGER gene.

Methods

We genotyped rs2070600 and measured the plasma concentration of sRAGE in each participant. Emphysema was quantified based on the chest computed tomography findings. We compared sRAGE levels based on the presence or absence and severity of emphysema in each genotype. Multiple logistic and linear regression models were used for the analyses.

Results

A total of 436 participants were included in the study. Among them, 64.2% had chronic obstructive pulmonary disease and 34.2% had emphysema. Among the CC-genotyped participants, the sRAGE level was significantly higher in participants without emphysema than in those with emphysema (P < 0.001). In addition, sRAGE levels were negatively correlated with emphysema severity in CC-genotyped patients (r = − 0.268 P < 0.001). Multiple regression analysis revealed that sRAGE was an independent protective factor for the presence of emphysema (adjusted odds ratio, 0.24; 95% confidence interval (CI) 0.11–0.51) and severity of emphysema (β = − 3.28, 95% CI − 4.86 to − 1.70) in CC-genotyped participants.

Conclusion

Plasma sRAGE might be a biomarker with a protective effect on emphysema among CC-genotyped patients of rs2070600 on the AGER gene. This is important in determining the target group for the future prediction and treatment of emphysema.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01848-9.

Quantitative measurements of emphysema in ultra-high resolution computed tomography using model-based iterative reconstruction in comparison to that using hybrid iterative reconstruction

The percentage of low attenuation volume ratio (LAVR), which is measured using computed tomography (CT), is an index of the severity of emphysema. For LAVR evaluation, ultra-high-resolution (U-HR) CT images are useful. To improve the image quality of U-HRCT, iterative reconstruction is used. There are two types of iterative reconstruction: hybrid iterative reconstruction (HIR) and model-based iterative reconstruction (MBIR). In this study, we physically and clinically evaluated U-HR images reconstructed with HIR and MBIR, and demonstrated the usefulness of U-HR images with MBIR for quantitative measurements of emphysema. Both images were reconstructed with a slice thickness of 0.25 mm and an image matrix size of 1024 × 1024 pixels. For physical evaluation, the modulation transfer function (MTF) and noise power spectrum (NPS) of HIR and MBIR were compared. For clinical evaluation, LAVR calculated from HIR and MBIR were compared using the Wilcoxon matched-pairs signed-rank test. In addition, the correlation between LAVR and forced expiratory volume in one second (FEV₁%) was evaluated using the Spearman rank correlation test. The MTFs of HIR and MBIR were comparable. The NPS of MBIR was lower than that of HIR. The mean LAVR values calculated from HIR and MBIR were 19.5 ± 12.6% and 20.4 ± 11.7%, respectively (p = 0.84). The correlation coefficients between LAVR and FEV₁% that were taken from HIR and MBIR were 0.64 and 0.74, respectively (p < 0.01). MBIR is more useful than HIR for the quantitative measurements of emphysema with U-HR images.

Bullous Parametric Response Map for Functional Localization of COPD

Advanced bronchoscopic lung volume reduction treatment (BLVR) is now a routine care option for treating patients with severe emphysema. Patterns of low attenuation clusters indicating emphysema and functional small airway disease (fSAD) on paired CT, which may provide additional insights to the target selection of the segmental or subsegmental lobe of the treatments, require further investigation. The low attenuation clusters (LACS) were segmented to identify the scalar and spatial distribution of the lung destructions, in terms of 10 fractions scales of low attenuation density (LAD) located in upper lobes and lower lobes. The LACs of functional small airway disease (fSAD) were delineated by applying the technique of parametric response map (PRM) on the co-registered CT image data. Both emphysematous LACs of inspiratory CT and fSAD LACs on expiratory CT were used to derive the coefficients of the predictive model for estimating the airflow limitation. The voxel-wise severity is then predicted using the regional LACs on the co-registered CT to indicate the functional localization, namely, the bullous parametric response map (BPRM). A total of 100 subjects, 88 patients with mild to very severe COPD and 12 control participants with normal lung functions (FEV₁/FVC % > 70%), were evaluated. Pearson’s correlations between FEV₁/FVC% and LAV%_HU-950 of severe emphysema are − 0.55 comparing to − 0.67 and − 0.62 of LAV%_HU-856 of air-trapping and LAV%_fSAD respectively. Pearson’s correlation between FEV₁/FVC% and FEV₁/FVC% predicted by the proposed model using LAD% of HU-950 and fSAD on BPRM is 0.82 (p < 0.01). The result of the Bullous Parametric Response Map (BPRM) is capable of identifying the less functional area of the lung, where the BLVR treatment is aimed at removing from a hyperinflated area of emphysematous regions.

Qualitative and Quantitative Assessment of Emphysema Using Dark-Field Chest Radiography

Background Dark-field chest radiography allows for assessment of lung alveolar structure by exploiting wave optical properties of x-rays. Purpose To evaluate the qualitative and quantitative features of dark-field chest radiography in participants with pulmonary emphysema as compared with those in healthy control subjects. Materials and Methods In this prospective study conducted from October 2018 to October 2020, participants aged at least 18 years who underwent clinically indicated chest CT were screened for participation. Inclusion criteria were an ability to consent to the procedure and stand upright without help. Exclusion criteria were pregnancy, serious medical conditions, and any lung condition besides emphysema that was visible on CT images. Participants were examined with a clinical dark-field chest radiography prototype that simultaneously acquired both attenuation-based radiographs and dark-field chest radiographs. Dark-field coefficients were tested for correlation with each participant's CT-based emphysema index using the Spearman correlation test. Dark-field coefficients of adjacent groups in the semiquantitative Fleischner Society emphysema grading system were compared using a Wilcoxon Mann-Whitney U test. The capability of the dark-field coefficient to enable detection of emphysema was evaluated with receiver operating characteristics curve analysis. Results A total of 83 participants (mean age, 65 years ± 12 [standard deviation]; 52 men) were studied. When compared with images from healthy participants, dark-field chest radiographs in participants with emphysema had a lower and inhomogeneous dark-field signal intensity. The locations of focal signal intensity loss on dark-field images corresponded well with emphysematous areas found on CT images. The dark-field coefficient was negatively correlated with the quantitative CT-based emphysema index (r = -0.54, P < .001). Participants with Fleischner Society grades of mild, moderate, confluent, or advanced destructive emphysema exhibited a lower dark-field coefficient than those without emphysema (eg, 1.3 m^-1 ± 0.6 for participants with confluent or advanced destructive emphysema vs 2.6 m^-1 ± 0.4 for participants without emphysema; P < .001). The area under the receiver operating characteristic curve for detection of mild emphysema was 0.79. Conclusion Pulmonary emphysema leads to reduced signal intensity on dark-field chest radiographs, showing the technique has potential as a diagnostic tool in the assessment of lung diseases. © RSNA, 2022 See also the editorial by Hatabu and Madore in this issue.

Forced Expiratory Flow at 25%-75% Links COPD Physiology to Emphysema and Disease Severity in the SPIROMICS Cohort

BACKGROUND

Forced expiratory volume in 1 second (FEV₁) is central to the diagnosis of chronic obstructive pulmonary disease (COPD) but is imprecise in classifying disease burden. We examined the potential of the maximal mid-expiratory flow rate (forced expiratory flow rate between 25% and 75% [FEF_25%-75%]) as an additional tool for characterizing pathophysiology in COPD.

OBJECTIVE

To determine whether FEF_25%-75% helps predict clinical and radiographic abnormalities in COPD.

STUDY DESIGN AND METHODS

The SubPopulations and InteRediate Outcome Measures In COPD Study (SPIROMICS) enrolled a prospective cohort of 2978 nonsmokers and ever-smokers, with and without COPD, to identify phenotypes and intermediate markers of disease progression. We used baseline data from 2771 ever-smokers from the SPIROMICS cohort to identify associations between percent predicted FEF_25%-75% (%predFEF_25%-75%) and both clinical markers and computed tomography (CT) findings of smoking-related lung disease.

RESULTS

Lower %predFEF_25-75% was associated with more severe disease, manifested radiographically by increased functional small airways disease, emphysema (most notably with homogeneous distribution), CT-measured residual volume, total lung capacity (TLC), and airway wall thickness, and clinically by increased symptoms, decreased 6-minute walk distance, and increased bronchodilator responsiveness (BDR). A lower %predFEF_25-75% remained significantly associated with increased emphysema, functional small airways disease, TLC, and BDR after adjustment for FEV₁ or forced vital capacity (FVC).

INTERPRETATION

The %predFEF_25-75% provides additional information about disease manifestation beyond FEV₁. These associations may reflect loss of elastic recoil and air trapping from emphysema and intrinsic small airways disease. Thus, %predFEF_25-75% helps link the anatomic pathology and deranged physiology of COPD.

Sex-related Differences in Airway Dimensions: A Study Based on Quantitative Computed Tomography among Chinese Population

Sex-dependent radiation injury may be related to the differences in physiological characteristics between the sexes. This study aimed to better understand variations in airway dimensions among male and female Chinese non-smokers. This study included 970 adults and 45 children who underwent chest CT. All participants were non-smokers, without current or former chronic pulmonary disease, and all underwent CT examination. The CT images were quantitatively assessed, providing airway dimensions. The differences in inner diameter, wall thickness, wall area (WA), and WA% for each airway were compared between male and female patients. Sex is an important influencing factor in airway morphological parameters. These parameters are different between men and women: men have a larger airway diameter (P < 0.05) and smaller wall area (WA%, P < 0.05) compared with women. Younger women (<35 years) have a greater diameter and smaller WA% compared with older women (P < 0.05). Sex-related differences in airway morphology were not observed in pediatric participants. Significant differences were found in quantitative CT measures of WA% and an internal diameter among non-smokers of varying sex. The differences found in this study might explain, in part, sex-dependency of radiation injury and a possible radiological protection scheme.

The role of imaging in the diagnosis and management of idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive disease lacking a definite etiology, characterized by the nonspecific symptoms of dyspnea and dry cough. Due to its poor prognosis, imaging techniques play an essential role in diagnosing and managing IPF. High resolution computed tomography (HRCT) has been shown to be the most sensitive modality for the diagnosis of pulmonary fibrosis. It is the primary imaging modality used for the assessment and follow-up of patients with IPF. Other not commonly used imaging methods are under research, such as ultrasound, magnetic resonance imaging and positron emission tomography-computed tomography are alternative imaging techniques. This literature review aims to provide a brief overview of the imaging of IPF-related alterations.

Role of the Emphysema Index Combined with the Chronic Obstructive Pulmonary Disease Assessment Test Score in the Evaluation of Chronic Obstructive Pulmonary Disease

Background

This study aimed to evaluate the efficacy of the emphysema index (EI) in distinguishing chronic bronchitis (CB) from chronic obstructive pulmonary disease (COPD) and its role, combined with the COPD Assessment Test (CAT) score, in the evaluation of COPD.

Methods

A total of 92 patients with CB and 277 patients with COPD were enrolled in this study. Receiver operating characteristic (ROC) curves were analyzed to evaluate whether the EI can preliminarily distinguish chronic bronchitis from COPD. Considering the heterogeneity of COPD, there might be missed diagnosis of some patients with bronchitis type when differentiating COPD patients only by EI. Therefore, patients with COPD were classified according to the CAT score and EI into four groups: Group 1 (EI < 16%, CAT < 10), Group 2 (EI < 16%, CAT ≥ 10), Group 3 (EI ≥ 16%, CAT < 10), and Group 4 (EI ≥ 16%, CAT ≥ 10). The records of pulmonary function and quantitative computed tomography findings were retrospectively analyzed.

Results

ROC curve analysis showed that EI = 16.2% was the cutoff value for distinguishing COPD from CB. Groups 1 and 2 exhibited significantly higher maximal voluntary ventilation (MVV) percent predicted (pred), forced expiratory volume in 1 second (FEV1)/forced vital capacity (FVC), maximal midexpiratory flow of 25-75% pred, carbon monoxide-diffusing capacity (DLCO)/alveolar ventilation (VA), FEV1 % pred (p ≤ 0.013), and maximal expiratory flow 50% pred (all p < 0.05) than Group 4. FEV1/FVC and DLCO/VA were significantly lower in Group 3 than in Group 2 (p=0.002 and p < 0.001, respectively). The residual volume/total lung capacity was higher in Group 3 than in Groups 1 and 2 (p < 0.05).

Conclusions

The combination of EI and CAT was effective in the evaluation of COPD.

Pulmonary Emphysema Regional Distribution and Extent Assessed by Chest Computed Tomography Is Associated With Pulmonary Function Impairment in Patients With COPD

Objective: This study aimed to evaluate how emphysema extent and its regional distribution quantified by chest CT are associated with clinical and functional severity in patients with chronic obstructive pulmonary disease (COPD).

Methods/Design: Patients with a post-bronchodilator forced expiratory volume in one second (FEV₁)/forced vital capacity (FVC) < 0.70, without any other obstructive airway disease, who presented radiological evidence of emphysema on visual CT inspection were retrospectively enrolled. A Quantitative Lung Imaging (QUALI) system automatically quantified the volume of pulmonary emphysema and adjusted this volume to the measured (EmphCT_LV) or predicted total lung volume (TLV) (EmphP_LV) and assessed its regional distribution based on an artificial neural network (ANN) trained for this purpose. Additionally, the percentage of lung volume occupied by low-attenuation areas (LAA) was computed by dividing the total volume of regions with attenuation lower or equal to −950 Hounsfield units (HU) by the predicted [LAA (%P_LV)] or measured CT lung volume [LAA (%CT_LV)]. The LAA was then compared with the QUALI emphysema estimations. The association between emphysema extension and its regional distribution with pulmonary function impairment was then assessed.

Results: In this study, 86 patients fulfilled the inclusion criteria. Both EmphCT_LV and EmphP_LV were significantly lower than the LAA indices independently of emphysema severity. CT-derived TLV significantly increased with emphysema severity (from 6,143 ± 1,295 up to 7,659 ± 1,264 ml from mild to very severe emphysema, p < 0.005) and thus, both EmphCT_LV and LAA significantly underestimated emphysema extent when compared with those values adjusted to the predicted lung volume. All CT-derived emphysema indices presented moderate to strong correlations with residual volume (RV) (with correlations ranging from 0.61 to 0.66), total lung capacity (TLC) (from 0.51 to 0.59), and FEV₁ (~0.6) and diffusing capacity for carbon monoxide DL_CO (~0.6). The values of FEV₁ and DL_CO were significantly lower, and RV (p < 0.001) and TLC (p < 0.001) were significantly higher with the increasing emphysema extent and when emphysematous areas homogeneously affected the lungs.

Conclusions: Emphysema volume must be referred to the predicted and not to the measured lung volume when assessing the CT-derived emphysema extension. Pulmonary function impairment was greater in patients with higher emphysema volumes and with a more homogeneous emphysema distribution. Further studies are still necessary to assess the significance of CTpLV in the clinical and research fields.

Clinical and Radiological Features of COPD Patients Living at ≥3000 m Above Sea Level in the Tibet Plateau

Background

COPD at high altitude may have different risk factors and unique clinical and radiological phenotypes. We aimed to investigate the demographic data, clinical and radiological features of COPD patients permanently residing at the Tibet Plateau (≥3000 meters above sea level).

Methods

We conducted an observational cross-sectional study which consecutively enrolled COPD patients visiting the outpatient of Respiratory Medicine at Tibet Autonomous Region People's Hospital from January 2018 to March 2021. All patients were Tibetan permanent residents aging ≥40 years and met the diagnosis of COPD according to Global Initiative for Chronic Obstructive Lung Disease (GOLD) guidelines. Data including demographic characteristics, altitude of residence, risk factors, respiratory symptoms, comorbidities and medications, as well as computed tomography (CT) measurements were collected.

Results

Eighty-four patients with definite COPD were enrolled for analysis. Their mean age was 64.7 (±9.1) years. All patients lived at ≥3000 m above sea level and 34.5% of them lived at ≥4000 m. About 8.3% of the patients were current smokers and 44.0% were ex-smokers. Up to 88.1% of the patients reported long-term exposure to indoor biomass fuels. Most of the patients were classified as having mild-to-moderate (GOLD I: 27.4%; GOLD II: 51.2%) COPD, while 89.3% had a CAT score ≥10. Only 36.9% of the patients received regular long-term medications for COPD in the past year, in whom ICS/LABA and oral theophylline were the most common used pharmacological therapy. On CT scanning, the majority of our patients (70.7%) showed no or minimal emphysema, while signs of previous tuberculosis were found in 45.1% of the patients.

Conclusion

COPD patients living at the Tibet Plateau had a heavy respiratory symptom burden, but most of them did not receive adequate pharmacological treatment. Indoor biomass fuel exposure and previous tuberculosis were prevalent, while the emphysema phenotype was less common in this population.

CT-defined emphysema in COPD patients and risk for change in desaturation status in 6-min walk test

BACKGROUND

Emphysema and exercise induced desaturation (EID) are both related to poorer COPD prognosis. More knowledge of associations between emphysema and desaturation is needed for more efficient disease management.

RESEARCH QUESTION

Is emphysema a risk factor for both new and repeated desaturation, and is emphysema of more or less importance than other known risk factors?

METHODS

283 COPD patients completed a 6-min walk test (6MWT) at baseline and one year later in the Bergen COPD cohort study 2006-2011. Degree of emphysema was assessed as percent of low attenuation areas (%LAA) under -950 Hounsfield units using high-resolution computed tomography at baseline. We performed multinomial logistic regression analysis, receiver operating curves (ROC) and area under the curve (AUC) estimations. Dominance analysis was used to rank emphysema and risk factors in terms of importance.

RESULTS

A one percent increase in %LAA increases the relative risk (RR) of new desaturation by 10 % (RR 1.1 (95%CI 1.1, 1.2)) and for repeated desaturation by 20 % (RR 1.2 (95%CI 1.1, 1.3)). Compared with other important desaturation risk factors, %LAA ranked as number one in the dominance analysis, accounting for 50 % and 37 % of the predicted variance for new and repeated desaturators, respectively. FEV₁% predicted accounted for 9 % and 24 %, and resting SpO₂ accounted for 22 % and 21 % for new and repeated desaturation.

CONCLUSION

Emphysema increases the risk of developing and repeatedly experiencing EID. Emphysema seems to be a more important risk factor for desaturation than FEV₁% predicted and resting saturation.

Evolution of CT Findings and Lung Residue in Patients with COVID-19 Pneumonia: Quantitative Analysis of the Disease with a Computer Automatic Tool

Purpose: the purpose of this study was to assess the evolution of computed tomography (CT) findings and lung residue in patients with COVID-19 pneumonia, via quantified evaluation of the disease, using a computer aided tool. Materials and methods: we retrospectively evaluated 341 CT examinations of 140 patients (68 years of median age) infected with COVID-19 (confirmed by real-time reverse transcriptase polymerase chain reaction (RT-PCR)), who were hospitalized, and who received clinical and CT examinations. All CTs were evaluated by two expert radiologists, in consensus, at the same reading session, using a computer-aided tool for quantification of the pulmonary disease. The parameters obtained using the computer tool included the healthy residual parenchyma, ground glass opacity, consolidation, and total lung volume. Results: statistically significant differences (p value ≤ 0.05) were found among quantified volumes of healthy residual parenchyma, ground glass opacity (GGO), consolidation, and total lung volume, considering different clinical conditions (stable, improved, and worsened). Statistically significant differences were found among quantified volumes for healthy residual parenchyma, GGO, and consolidation (p value ≤ 0.05) between dead patients and discharged patients. CT was not performed on cadavers; the death was an outcome, which was retrospectively included to differentiate findings of patients who survived vs. patients who died during hospitalization. Among discharged patients, complete disease resolutions on CT scans were observed in 62/129 patients with lung disease involvement ≤5%; lung disease involvement from 5% to 15% was found in 40/129 patients, while 27/129 patients had lung disease involvement between 16 and 30%. Moreover, 8–21 days (after hospital admission) was an “advanced period” with the most severe lung disease involvement. After the extent of involvement started to decrease—particularly after 21 days—the absorption was more obvious. Conclusions: a complete disease resolution on chest CT scans was observed in 48.1% of discharged patients using a computer-aided tool to quantify the GGO and consolidation volumes; after 16 days of hospital admission, the abnormalities identified by chest CT began to improve; in particular, the absorption was more obvious after 21 days.

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.

Automated Diseased Lung Volume Percentage Calculation in Quantitative CT Evaluation of Chronic Obstructive Pulmonary Disease and Idiopathic Pulmonary Fibrosis

OBJECTIVE

Several software-based quantitative computed tomography (CT) analysis methods have been developed for assessing emphysema and interstitial lung disease. Although the texture classification method appeared to be more successful than the other methods, the software programs are not commercially available, to our knowledge. Therefore, this study aimed to investigate the usefulness of a commercially available software program for quantitative CT analyses.

METHODS

This prospective cohort study included 80 patients with chronic obstructive pulmonary disease (COPD) or idiopathic pulmonary fibrosis (IPF).

RESULTS

The percentage of low attenuation volume and high attenuation volume had high sensitivity and high specificity for detecting emphysema and pulmonary fibrosis, respectively. The percentage of diseased lung volume (DLV%) was significantly correlated with the lung diffusion capacity for carbon monoxide in all patients with COPD and IPF patients.

CONCLUSIONS

The quantitative CT analysis may improve the precision of the assessment of DLV%, which itself could be a useful tool in predicting lung diffusion capacity in patients with the clinical diagnosis of COPD or IPF.

Automated CT Lung Density Analysis of Viral Pneumonia and Healthy Lungs Using Deep Learning-Based Segmentation, Histograms and HU Thresholds

CT patterns of viral pneumonia are usually only qualitatively described in radiology reports. Artificial intelligence enables automated and reliable segmentation of lungs with chest CT. Based on this, the purpose of this study was to derive meaningful imaging biomarkers reflecting CT patterns of viral pneumonia and assess their potential to discriminate between healthy lungs and lungs with viral pneumonia. This study used non-enhanced and CT pulmonary angiograms (CTPAs) of healthy lungs and viral pneumonia (SARS-CoV-2, influenza A/B) identified by radiology reports and RT-PCR results. After deep learning segmentation of the lungs, histogram-based and threshold-based analyses of lung attenuation were performed and compared. The derived imaging biomarkers were correlated with parameters of clinical and biochemical severity (modified WHO severity scale; c-reactive protein). For non-enhanced CTs (n = 526), all imaging biomarkers significantly differed between healthy lungs and lungs with viral pneumonia (all p < 0.001), a finding that was not reproduced for CTPAs (n = 504). Standard deviation (histogram-derived) and relative high attenuation area [600-0 HU] (HU-thresholding) differed most. The strongest correlation with disease severity was found for absolute high attenuation area [600-0 HU] (r = 0.56, 95% CI = 0.46-0.64). Deep-learning segmentation-based histogram and HU threshold analysis could be deployed in chest CT evaluation for the differentiating of healthy lungs from AP lungs.

Ultra-low-dose CT combined with noise reduction techniques for quantification of emphysema in COPD patients: An intra-individual comparison study with standard-dose CT

PURPOSE

Phantom studies in CT emphysema quantification show that iterative reconstruction and deep learning-based noise reduction (DLNR) allow lower radiation dose. We compared emphysema quantification on ultra-low-dose CT (ULDCT) with and without noise reduction, to standard-dose CT (SDCT) in chronic obstructive pulmonary disease (COPD).

METHOD

Forty-nine COPD patients underwent ULDCT (third generation dual-source CT; 70ref-mAs, Sn-filter 100kVp; median CTDIvol 0.38 mGy) and SDCT (64-multidetector CT; 40mAs, 120kVp; CTDIvol 3.04 mGy). Scans were reconstructed with filtered backprojection (FBP) and soft kernel. For ULDCT, we also applied advanced modelled iterative reconstruction (ADMIRE), levels 1/3/5, and DLNR, levels 1/3/5/9. Emphysema was quantified as Low Attenuation Value percentage (LAV%, ≤-950HU). ULDCT measures were compared to SDCT as reference standard.

RESULTS

For ULDCT, the median radiation dose was 84 % lower than for SDCT. Median extent of emphysema was 18.6 % for ULD-FBP and 15.4 % for SDCT (inter-quartile range: 11.8-28.4 % and 9.2 %-28.7 %, p = 0.002). Compared to SDCT, the range in limits of agreement of emphysema quantification as measure of variability was 14.4 for ULD-FBP, 11.0-13.1 for ULD-ADMIRE levels and 10.1-13.9 for ULD-DLNR levels. Optimal settings were ADMIRE 3 and DLNR 3, reducing variability of emphysema quantification by 24 % and 27 %, at slight underestimation of emphysema extent (-1.5 % and -2.9 %, respectively).

CONCLUSIONS

Ultra-low-dose CT in COPD patients allows dose reduction by 84 %. State-of-the-art noise reduction methods in ULDCT resulted in slight underestimation of emphysema compared to SDCT. Noise reduction methods (especially ADMIRE 3 and DLNR 3) reduced variability of emphysema quantification in ULDCT by up to 27 % compared to FBP.

Quantitative CT imaging and advanced visualization methods: potential application in novel coronavirus disease 2019 (COVID-19) pneumonia

Increasingly, quantitative lung computed tomography (qCT)-derived metrics are providing novel insights into chronic inflammatory lung diseases, including chronic obstructive pulmonary disease, asthma, interstitial lung disease, and more. Metrics related to parenchymal, airway, and vascular anatomy together with various measures associated with lung function including regional parenchymal mechanics, air trapping associated with functional small airways disease, and dual-energy derived measures of perfused blood volume are offering the ability to characterize disease phenotypes associated with the chronic inflammatory pulmonary diseases. With the emergence of COVID-19, together with its widely varying degrees of severity, its rapid progression in some cases, and the potential for lengthy post-COVID-19 morbidity, there is a new role in applying well-established qCT-based metrics. Based on the utility of qCT tools in other lung diseases, previously validated supervised classical machine learning methods, and emerging unsupervised machine learning and deep-learning approaches, we are now able to provide desperately needed insight into the acute and the chronic phases of this inflammatory lung disease. The potential areas in which qCT imaging can be beneficial include improved accuracy of diagnosis, identification of clinically distinct phenotypes, improvement of disease prognosis, stratification of care, and early objective evaluation of intervention response. There is also a potential role for qCT in evaluating an increasing population of post-COVID-19 lung parenchymal changes such as fibrosis. In this work, we discuss the basis of various lung qCT methods, using case-examples to highlight their potential application as a tool for the exploration and characterization of COVID-19, and offer scanning protocols to serve as templates for imaging the lung such that these established qCT analyses have the best chance at yielding the much needed new insights.

Clinical and laboratory data, radiological structured report findings and quantitative evaluation of lung involvement on baseline chest CT in COVID-19 patients to predict prognosis

OBJECTIVE

To evaluate by means of regression models the relationships between baseline clinical and laboratory data and lung involvement on baseline chest CT and to quantify the thoracic disease using an artificial intelligence tool and a visual scoring system to predict prognosis in patients with COVID-19 pneumonia.

MATERIALS AND METHODS

This study included 103 (41 women and 62 men; 68.8 years of mean age-range, 29-93 years) with suspicious COVID-19 viral infection evaluated by reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) test. All patients underwent CT examinations at the time of admission in addition to clinical and laboratory findings recording. All chest CT examinations were reviewed using a structured report. Moreover, using an artificial intelligence tool we performed an automatic segmentation on CT images based on Hounsfield unit to calculate residual healthy lung parenchyma, ground-glass opacities (GGO), consolidations and emphysema volumes for both right and left lungs. Two expert radiologists, in consensus, attributed at the CT pulmonary disease involvement a severity score using a scale of 5 levels; the score was attributed for GGO and consolidation for each lung, and then, an overall radiological severity visual score was obtained summing the single score. Univariate and multivariate regression analysis was performed.

RESULTS

Symptoms and comorbidities did not show differences statistically significant in terms of patient outcome. Instead, SpO2 was significantly lower in patients hospitalized in critical conditions or died while age, HS CRP, leukocyte count, neutrophils, LDH, d-dimer, troponin, creatinine and azotemia, ALT, AST and bilirubin values were significantly higher. GGO and consolidations were the main CT patterns (a variable combination of GGO and consolidations was found in 87.8% of patients). CT COVID-19 disease was prevalently bilateral (77.6%) with peripheral distribution (74.5%) and multiple lobes localizations (52.0%). Consolidation, emphysema and residual healthy lung parenchyma volumes showed statistically significant differences in the three groups of patients based on outcome (patients discharged at home, patients hospitalized in stable conditions and patient hospitalized in critical conditions or died) while GGO volume did not affect the patient's outcome. Moreover, the overall radiological severity visual score (cutoff ≥ 8) was a predictor of patient outcome. The highest value of R-squared (R2 = 0.93) was obtained by the model that combines clinical/laboratory findings at CT volumes. The highest accuracy was obtained by clinical/laboratory and CT findings model with a sensitivity, specificity and accuracy, respectively, of 88%, 78% and 81% to predict discharged/stable patients versus critical/died patients.

CONCLUSION

In conclusion, both CT visual score and computerized software-based quantification of the consolidation, emphysema and residual healthy lung parenchyma on chest CT images were independent predictors of outcome in patients with COVID-19 pneumonia.

Correlation between quantitative multi-detector computed tomography lung analysis and pulmonary function tests in chronic obstructive pulmonary disease patients

Background

Chronic obstructive pulmonary disease [COPD] is a very common disease in developing as well as in developed countries. Using CT has a growing interest to give a phenotypic classification helping the clinical characterization of COPD patients. So, the aim of the present study was to evaluate whether there was a significant correlation between quantitative computed tomography lung analysis and pulmonary function tests in chronic obstructive pulmonary disease patients.

Results

The study included 50 male patients with a mean age of 62.82 years ± 8.65 years standard deviation [SD]. Significant correlation was found between the pulmonary function tests [FEV1 and FEV1/FVC ratio], and all parameters of quantitative assessment with – 950 HU [the percentage of low-attenuation areas (% LAA)]. Pulmonary function tests according to GOLD [Global Initiative for Chronic Obstructive Lung Disease] guidelines revealed that 4% had normal pulmonary function, 8% had mild obstructive defect, 32% had moderate obstructive defect, 26% had severe obstructive defect, and 30% had very severe obstructive defect.

Conclusion

Automated CT densitometry defining the emphysema severity was significantly correlated with the parameters of pulmonary function tests and providing an alternative, quick, simple, non-invasive study for evaluation of emphysema severity. Its main importance was the determination of the extent and distribution of affected emphysematous parts of the lungs especially for selecting the patients suitable for the lung volume reduction surgery.

[Quantitative Analysis of Emphysema in Ultra-high-resolution CT by Using Deep Learning Reconstruction: Comparison with Hybrid Iterative Reconstruction]

PURPOSE

The noise generated in ultra-high-resolution computed tomography (U-HRCT) images affects the quantitative analysis of emphysema. In this study, we compared the physical properties of reconstructed images for hybrid iterative reconstruction (HIR) and deep learning reconstruction (DLR), which are reconstruction methods for reducing image noise. Using clinical evaluation, we evaluated the correlation between low attenuation volume (LAV) % obtained by CT and forced expiratory volume in 1 s per forced vital capacity (FEV₁/FVC) obtained by respiratory function tests.

MATERIALS AND METHODS

CT data obtained by HIR and DLR were used for analysis (matrix size: 1024´1024, slice thickness: 0.25 mm). The physical characteristics were evaluated for the modulation transfer function (MTF) and noise power spectrum (NPS). Display-field of view (D-FOV) was analyzed by varying between 300 mm and 400 mm. The clinical data evaluated the relationship between LAV% and FEV₁/FVC by Spearman's correlation coefficient.

RESULT

The 10% MTFs were 1.3 cycles/mm (HIR) and 1.3 cycles/mm (DLR) at D-FOV 300 mm, and 1.2 cycles/mm (HIR) and 1.1 cycles/mm (DLR) at D-FOV 400 mm. The NPS had less noise in DLR than HIR in all frequency ranges. The correlation coefficients between LAV% and FEV₁/FVC were 0.64 and 0.71, respectively, in HIR and DLR.

CONCLUSION

There was no difference in the resolution characteristics of HIR and DLR. DLR had better noise characteristics than HIR. The correlation between LAV% measured by HIR and DLR and FEV₁/FVC is equivalent. The noise characteristics of the DLR enable the reduction of exposure to emphysema quantitative analysis by CT.

COVID-19 pneumonia: computer-aided quantification of healthy lung parenchyma, emphysema, ground glass and consolidation on chest computed tomography (CT)

Objective

To calculate by means of a computer-aided tool the volumes of healthy residual lung parenchyma, of emphysema, of ground glass opacity (GGO) and of consolidation on chest computed tomography (CT) in patients with suspected viral pneumonia by COVID-19.

Materials and methods

This study included 116 patients that for suspected COVID-19 infection were subjected to the reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) test. A computer-aided tool was used to calculate on chest CT images healthy residual lung parenchyma, emphysema, GGO and consolidation volumes for both right and left lung. Expert radiologists, in consensus, assessed the CT images using a structured report and attributed a radiological severity score at the disease pulmonary involvement using a scale of five levels. Nonparametric test was performed to assess differences statistically significant among groups.

Results

GGO was the most represented feature in suspected CT by COVID-19 infection; it is present in 102/109 (93.6%) patients with a volume percentage value of 19.50% and a median value of 0.64 L, while the emphysema and consolidation volumes were low (0.01 L and 0.03 L, respectively). Among quantified volume, only GGO volume had a difference statistically significant between the group of patients with suspected versus non-suspected CT for COVID-19 (p < < 0.01). There were differences statistically significant among the groups based on radiological severity score in terms of healthy residual parenchyma volume, of GGO volume and of consolidations volume (p < < 0.001).

Conclusion

We demonstrated that, using a computer-aided tool, the COVID-19 pneumonia was mirrored with a percentage median value of GGO of 19.50% and that only GGO volume had a difference significant between the patients with suspected or non-suspected CT for COVID-19 infection.

Differentiating combined pulmonary fibrosis and emphysema from pure emphysema: utility of late gadolinium-enhanced MRI

Background

Differentiating combined pulmonary fibrosis with emphysema (CPFE) from pure emphysema can be challenging on high-resolution computed tomography (HRCT). This has antifibrotic therapy implications.

Methods

Twenty patients with suspected CPFE underwent late gadolinium-enhanced (LGE) thoracic magnetic resonance imaging (LGE-MRI) and HRCT. Data from twelve healthy control subjects from a previous study who underwent thoracic LGE-MRI were included for comparison. Quantitative LGE signal intensity (SI) was retrospectively compared in regions of fibrosis and emphysema in CPFE patients to similar lung regions in controls. Qualitative comparisons for the presence/extent of reticulation, honeycombing, and traction bronchiectasis between LGE-MRI and HRCT were assessed by two readers in consensus.

Results

There were significant quantitative differences in fibrosis SI compared to emphysema SI in CPFE patients (25.8, IQR 18.4–31.0 versus 5.3, IQR 5.0–8.1, p < 0.001). Significant differences were found between LGE-MRI and HRCT in the extent of reticulation (12.5, IQR 5.0–20.0 versus 25.0, IQR 15.0–26.3, p = 0.038) and honeycombing (5.0, IQR 0.0–10.0 versus 20.0, IQR 10.6–20.0, p = 0.001) but not traction bronchiectasis (10.0, IQR 5–15 versus 15.0, IQR 5–15, p = 0.878). Receiver operator curve analysis of fibrosis SI compared to similarly located regions in control subjects showed an area under the curve of 0.82 (p = 0.002). A SI cutoff of 19 yielded a sensitivity of 75% and specificity of 86% in differentiating fibrosis from similarly located regions in control subjects.

Conclusion

LGE-MRI can differentiate CPFE from pure emphysema and may be a useful adjunct test to HRCT in patients with suspected CPFE.

Bronchoscopic lung volume reduction: status quo

Emphysema is associated with irreversible loss of lung compliance leading to gas trapping and hyperinflation. Surgical lung volume reduction has proven to improve lung function, exercise capacity, cardiac health and survival in patients with advanced emphysema; however, this procedure is associated with significant morbidity and mortality. Bronchoscopic lung volume reduction (BLVR) has emerged as an alternative approach for these patients. In this article, we review the different techniques used for the purpose of this procedure, its advantages and disadvantages. In addition, we discuss in length valve therapy and the studies that led to its recent FDA approval. Finally, we provide thought-provoking challenges that may be topics for further future investigation to enhance the efficacy and benefit of this technique.

CT kernel conversions using convolutional neural net for super-resolution with simplified squeeze-and-excitation blocks and progressive learning among smooth and sharp kernels

PURPOSE

Computed tomography (CT) volume sets reconstructed with different kernels are helping to increase diagnostic accuracy. However, several CT volumes reconstructed with different kernels are difficult to sustain, due to limited storage and maintenance issues. A CT kernel conversion method is proposed using convolutional neural networks (CNN).

METHODS

A total of 3289 CT images from ten patients (five men and five women; mean age, 63.0 ± 8.6 years) were obtained in May 2016 (Somatom Sensation 16, Siemens Medical Systems, Forchheim, Germany). These CT images were reconstructed with various kernels, including B10f (very smooth), B30f (medium smooth), B50f (medium sharp), and B70f (very sharp) kernels. Smooth kernel images were converted into sharp kernel images using super-resolution (SR) network with Squeeze-and-Excitation (SE) blocks and auxiliary losses, and vice versa. In this study, the single-conversion model and multi-conversion model were presented. In case of the single-conversion model, for the one corresponding output image (e.g., B10f to B70), SE-Residual blocks were stacked. For the multi-conversion model, to convert an image into several output images (e.g., B10f to B30f, B50f, and B70f, and vice versa), progressive learning (PL) was employed by calculating auxiliary losses in every four SE-Residual blocks. Through auxiliary losses, the model could learn mutual relationships between different kernel types. The conversion quality was evaluated by the root-mean-square-error (RMSE), structural similarity (SSIM) index and mutual information (MI) between original and converted images.

RESULTS

The RMSE (SSIM index , MI) of the multi-conversion model was 4.541 ± 0.688 (0.998 ± 0.001 , 2.587 ± 0.137), 27.555 ± 5.876 (0.944 ± 0.021 , 1.735 ± 0.137), 72.327 ± 17.387 (0.815 ± 0.053 , 1.176 ± 0.096), 8.748 ± 1.798 (0.996 ± 0.002 , 2.464 ± 0.121), 9.470 ± 1.772 (0.994 ± 0.003 , 2.336 ± 0.133), and 9.184 ± 1.605 (0.994 ± 0.002 , 2.342 ± 0.138) in conversion between B10f-B30f, B10f-B50f, B10f-B70f, B70f-B50f, B70f-B30f, and B70f-B10f, respectively, which showed significantly better image quality than the conventional model.

CONCLUSIONS

We proposed deep learning-based CT kernel conversion using SR network. By introducing simplified SE blocks and PL, the model performance was significantly improved.

Quantitative CT Analysis in Patients with Pulmonary Emphysema: Do Calculated Differences Between Full Inspiration and Expiration Correlate with Lung Function?

Purpose

The aim of this retrospective study was to evaluate correlations between parameters of quantitative computed tomography (QCT) analysis, especially the 15th percentile of lung attenuation (P15), and parameters of clinical tests in a large group of patients with pulmonary emphysema.

Patients and Methods

One hundred and seventy-two patients with pulmonary emphysema and chronic obstructive pulmonary disease (COPD) global initiative for chronic obstructive lung disease (GOLD) stage 3 or 4 were assessed by nonenhanced thin-section CT scans in full inspiratory and expiratory breath-hold, pulmonary function test (PFT), a 6-minute walk test (6MWT), and quality of life questionnaires (SGRQ and CAT). QCT parameters included total lung volume (TLV), total emphysema score (TES), and P15, all measured at inspiration (IN) and expiration (EX). Differences between inspiration and expiration were calculated for TLV (TLV_Diff), TES (TES_Diff), and P15 (P15_Diff). Spearman correlation analysis was performed.

Results

CT-measured lung volume in inspiration (TLV_IN) correlated strongly with spirometry-measured total lung capacity (TLC) (r=0.81, p<0.001) and moderately to strongly with residual volume (RV), forced vital capacity (FVC), and forced expiratory volume in 1 second (FEV₁)/FVC (r=0.60, 0.56, and −0.49, each p<0.001). Lung volume in expiration (TLV_EX) correlated moderately to strongly with TLC, RV and FEV₁/FVC ratio (r=0.75, 0.66, and −0.43, each p<0.001). TES and P15 showed stronger correlations with the carbon monoxide transfer coefficient (KCO%) (r= −0.42, 0.44, both p<0.001), when measured during expiration. P15_Diff correlated moderately with KCO% and carbon monoxide diffusing capacity (DLCO%) (r= 0.41, 0.40, both p<0.001). The 6MWT and most QCT parameters showed significant differences between COPD GOLD 3 and 4 groups.

Conclusion

Our results suggest that QCT can help predict the severity of lung function decrease in patients with pulmonary emphysema and COPD GOLD 3 or 4. Some QCT parameters, including P15_EX and P15_Diff, correlated moderately to strongly with parameters of pulmonary function tests.

Impact of quantitative pulmonary emphysema score on the rate of pneumothorax and chest tube insertion in CT-guided lung biopsies

The aim of this study was to evaluate the risk of pneumothorax and need for chest tube insertion in CT-guided lung biopsies and identify predictors focusing on pulmonary emphysema determined with quantitative computed tomography. To that end, we retrospectively analysed the incidence of pneumothorax and chest tube insertion in 371 CT-guided lung biopsies with respect to the quantitative emphysema score determined with the density mask technique. Other possible impact factors considered were lesion diameter, length of biopsy pathway within the lung parenchyma, lung lobe, needle size, puncture technique, patient positioning and interventionalist's level of experience. Quantitative emphysema scores of the lung were significantly higher in patients who developed instant pneumothorax (27%, p < 0.0001), overall pneumothorax (38%, p = 0.001) and had chest tube insertion (9%, p = 0.006) compared to those who did not when analysed with the Mann-Whitney U-test. In logistic regression analysis with inclusion of the other possible impact factors, the quantitative emphysema score remained a statistically significant predictor for all three output parameters. This was confirmed with least absolute shrinkage and selection operator (Lasso) regression analysis. In conclusion, quantitatively determined pulmonary emphysema is a positive predictor of the pneumothorax rate in CT-guided lung biopsy and likelihood of chest tube insertion.

Optimal threshold in low-dose CT quantification of emphysema

OBJECTIVE

Low-dose CT is now widely used in the screening of lung cancer and the detection of pulmonary nodules. There has also been increasing interest in using Low-dose CT for evaluating emphysema. In conventional dose CT, the threshold of -950HU is a common threshold for density-based emphysema quantification for worldwide population. However, the optimal threshold for assessing emphysema at low-dose CT has not been determined. The purpose of this study is to determine the optimal threshold for low-dose CT quantification of emphysema for Chinese population.

MATERIALS AND METHODS

In this study, 548 low-dose chest CT examinations acquired from different subjects (119 none, 49 mild, 163 moderate, 152 severe, and 65 very severe obstruction) are collected. At the level of the entire lung and individual lobes, the extent of emphysema was quantified by the percentage of the low attenuation area (LAA%) at a wide range of thresholds from -850HU to -1000HU. Both Pearson and Spearman's rank correlation coefficients were used to assess the correlations between 1) LAA% and pulmonary functions and 2) LAA% and the five-category classification. The statistical significance of the difference between correlation coefficients were evaluated using Steiger'Z test.

RESULTS

LAA% had a good correlation with both pulmonary function (|r| = 0.1-0.600, p < 0.001) and the five-category classification (r = 0.163-0.602, p < 0.001) in both the entire lung and individual lobes under different thresholds. The highest correlation coefficient is obtained at -940HU instead of -950HU.

CONCLUSION

Low-dose CT can be used for quantitative assessment of emphysema, and the threshold of -940HU is a suitable threshold for quantifying emphysema in low-dose CT images for Chinese population.

Deep learning in interstitial lung disease-how long until daily practice

Interstitial lung diseases are a diverse group of disorders that involve inflammation and fibrosis of interstitium, with clinical, radiological, and pathological overlapping features. These are an important cause of morbidity and mortality among lung diseases. This review describes computer-aided diagnosis systems centered on deep learning approaches that improve the diagnostic of interstitial lung diseases. We highlighted the challenges and the implementation of important daily practice, especially in the early diagnosis of idiopathic pulmonary fibrosis (IPF). Developing a convolutional neuronal network (CNN) that could be deployed on any computer station and be accessible to non-academic centers is the next frontier that needs to be crossed. In the future, early diagnosis of IPF should be possible. CNN might not only spare the human resources but also will reduce the costs spent on all the social and healthcare aspects of this deadly disease.

Key Points

• Deep learning algorithms are used in pattern recognition of different interstitial lung diseases.

• High-resolution computed tomography plays a central role in the diagnosis and in the management of all interstitial lung diseases, especially fibrotic lung disease.

• Developing an accessible algorithm that could be deployed on any computer station and be used in non-academic centers is the next frontier in the early diagnosis of idiopathic pulmonary fibrosis.

Calculating air volume fractions from computed tomography images for chronic obstructive pulmonary disease diagnosis

Quantitative evaluation using image biomarkers calculated from threshold-segmented low-attenuation areas on chest computed tomography (CT) images for diagnosing chronic obstructive pulmonary diseases (COPD) has been widely investigated. However, the segmentation results depend on the applied threshold and slice thickness of the CT images because of the partial volume effect (PVE). In this study, the air volume fraction (AV/TV) of lungs was calculated from CT images using a two-compartment model (TCM) for COPD diagnosis. A relative air volume histogram (RAVH) was constructed using the AV/TV values to describe the air content characteristics of lungs. In phantom studies, the TCM accurately calculated total cavity volumes and foam masses with percent errors of less than 8% and ±4%, respectively. In patient studies, the relative volumes of normal and damaged lung tissues and the damaged-to-normal RV ratio were defined and calculated from the RAVHs as image biomarkers, which correctly differentiated COPD patients from controls in 2.5- and 5-mm-thick images with areas under receiver operating characteristic curves of >0.94. The AV/TV calculated using the TCM can prevent the effect of slice thickness, and the image biomarkers calculated from the RAVH are reliable for diagnosing COPD

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

OBJECTIVES

Chronic obstructive pulmonary disease (COPD) is characterized by variable contributions of emphysema and airway disease on computed tomography (CT), and still little is known on their temporal evolution. We hypothesized that quantitative CT (QCT) is able to detect short-time changes in a cohort of patients with very severe COPD.

METHODS

Two paired in- and expiratory CT each from 70 patients with avg. GOLD stage of 3.6 (mean age = 66 ± 7.5, mean FEV1/FVC = 35.28 ± 7.75) were taken 3 months apart and analyzed by fully automatic software computing emphysema (emphysema index (EI), mean lung density (MLD)), air-trapping (ratio expiration to inspiration of mean lung attenuation (E/I MLA), relative volume change between - 856 HU and - 950 HU (RVC_856-950)), and parametric response mapping (PRM) parameters for each lobe separately and the whole lung. Airway metrics measured were wall thickness (WT) and lumen area (LA) for each airway generation and the whole lung.

RESULTS

The average of the emphysema parameters (EI, MLD) increased significantly by 1.5% (p < 0.001) for the whole lung, whereas air-trapping parameters (E/I MLA, RVC_856-950) were stable. PRM_Emph increased from 34.3 to 35.7% (p < 0.001), whereas PRM_Normal decrased from 23.6% to 22.8% (p = 0.012). WT decreased significantly from 1.17 ± 0.18 to 1.14 ± 0.19 mm (p = 0.036) and LA increased significantly from 25.08 ± 4.49 to 25.84 ± 4.87 mm² (p = 0.041) for the whole lung. The generation-based analysis showed heterogeneous results.

CONCLUSION

QCT detects short-time progression of emphysema in severe COPD. The changes were partly different among lung lobes and airway generations, indicating that QCT is useful to address the heterogeneity of COPD progression.

KEY POINTS

• QCT detects short-time progression of emphysema in severe COPD in a 3-month period. • QCT is able to quantify even slight parenchymal changes, which were not detected by spirometry. • QCT is able to address the heterogeneity of COPD, revealing inconsistent changes individual lung lobes and airway generations.

Predictive Modelling of Lung Function using Emphysematous Density Distribution

Target lung tissue selection remains a challenging task to perform for treating severe emphysema with lung volume reduction (LVR). In order to target the treatment candidate, the percentage of low attenuation volume (LAV%) representing the proportion of emphysema volume to whole lung volume is measured using computed tomography (CT) images. Although LAV% have shown to have a correlation with lung function in patients with chronic obstructive pulmonary disease (COPD), similar measurements of LAV% in whole lung or lobes may have large variations in lung function due to emphysema heterogeneity. The functional information of regional emphysema destruction is required for supporting the choice of optimal target. The purpose of this study is to develop an emphysema heterogeneity descriptor for the three-dimensional emphysematous bullae according to the size variations of emphysematous density (ED) and their spatial distribution. The second purpose is to derive a predictive model of airflow limitation based on the regional emphysema heterogeneity. Deriving the bullous representation and grouping them into four scales in the upper and lower lobes, a predictive model is computed using the linear model fitting to estimate the severity of lung function. A total of 99 subjects, 87 patients with mild to very severe COPD (Global Initiative for Chronic Obstructive Lung Disease (GOLD) stage I~IV) and 12 control participants with normal lung functions (forced expiratory volume in one second (FEV₁)/forced vital capacity (FVC) > 0.7) were evaluated. The final model was trained with stratified cross-validation on randomly selected 75% of the dataset (n = 76) and tested on the remaining dataset (n = 23). The dispersed cases of LAV% inconsistent with their lung function outcome were evaluated, and the correlation study suggests that comparing to LAV of larger bullae, the widely spread smaller bullae with equivalent LAV has a larger impact on lung function. The testing dataset has the correlation of r = -0.76 (p < 0.01) between the whole lung LAV% and FEV₁/FVC, whereas using two ED % of scales and location-dependent variables to predict the emphysema-associated FEV₁/FVC, the results shows their correlation of 0.82 (p < 0.001) with clinical FEV₁/FVC.

Significance of the lobe-specific emphysema index to predict prolonged air leak after anatomical segmentectomy

Prolonged air leak (PAL) is a major complication of pulmonary resection. Emphysema quantification with computed tomography is regarded as an important predictor of PAL for patients undergoing lobectomy. Therefore, we investigated whether this predictor might be applicable for segmentectomy. Herein, we characterized the factors that influence PAL in early stage lung cancer patients undergoing anatomical segmentectomy. Forty-one patients who underwent anatomical segmentectomy for early lung cancer between January 2014 and July 2017 were included for analysis. Several baseline and surgical variables were evaluated. In particular, the emphysema index (EI, %) and lobe-specific emphysema index (LEI, %) were assessed by using three-dimensional volumetric CT scan. PAL was observed in 13 patients (31.7%). There were statistically significant differences in DLCO (97.3% ± 18.3% vs. 111.7% ± 15.9%, p = 0.014), EI (4.61% ± 4.66% vs. 1.17% ± 1.76%, p = 0.023), and LEI (5.81% ± 5.78% vs. 0.76% ± 1.17%, p = 0.009) between patients with and without PAL. According to logistic regression analysis, both EI and LEI were significantly associated with PAL (p = 0.028 and p < 0.001, respectively). We found that EI and LEI significantly influenced the development of PAL after pulmonary resection. In particular, LEI showed stronger association with PAL, compared with EI, suggesting the importance of LEI in the prediction of PAL after anatomical segmentectomy.

CT-based Airway Flow Model to Assess Ventilation in Chronic Obstructive Pulmonary Disease: A Pilot Study

Background The lack of functional information in thoracic CT remains a limitation of its use in the clinical management of chronic obstructive pulmonary disease (COPD). Purpose To compare the distribution of pulmonary ventilation assessed by a CT-based full-scale airway network (FAN) flow model with hyperpolarized xenon 129 (¹²⁹Xe) MRI (hereafter, ¹²⁹Xe MRI) and technetium 99m-diethylenetriaminepentaacetic acid aerosol SPECT ventilation imaging (hereafter, V-SPECT) in participants with COPD. Materials and Methods In this prospective study performed between May and August 2017, pulmonary ventilation in participants with COPD was computed by using the FAN flow model. The modeled pulmonary ventilation was compared with functional imaging data from breath-hold time-series ¹²⁹Xe MRI and V-SPECT. FAN-derived ventilation images on the coronal plane and volumes of interest were compared with functional lung images. Percentage lobar ventilation estimated by the FAN model was compared with that measured at ¹²⁹Xe MRI and V-SPECT. The statistical significance of ventilation distribution between FAN and functional images was demonstrated with the Spearman correlation coefficient and χ² distance. Results For this study, nine participants (seven men [mean age, 65 years ± 5 {standard deviation}] and two women [mean age, 63 years ± 7]) with COPD that was Global Initiative for Chronic Obstructive Lung Disease stage II-IV were enrolled. FAN-modeled ventilation profile showed strong positive correlation with images from ¹²⁹Xe MRI (ρ = 0.67; P < .001) and V-SPECT (ρ = 0.65; P < .001). The χ² distances of the ventilation histograms in the volumes of interest between the FAN and ¹²⁹Xe MRI and FAN and V-SPECT were 0.16 ± 0.08 and 0.28 ± 0.14, respectively. The ratios of lobar ventilations in the models were linearly correlated to images from ¹²⁹Xe MRI (ρ = 0.67; P < .001) and V-SPECT (ρ = 0.59; P < .001). Conclusion A CT-based full-scale airway network flow model provided regional pulmonary ventilation information for chronic obstructive pulmonary disease and correlates with hyperpolarized xenon 129 MRI and technetium 99m-diethylenetriaminepentaacetic acid aerosol SPECT ventilation imaging. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Schiebler and Parraga in this issue.

Quantitative Emphysema Measurement On Ultra-High-Resolution CT Scans

Purpose

To evaluate the advantages of ultra-high-resolution computed tomography (U-HRCT) scans for the quantitative measurement of emphysematous lesions over conventional HRCT scans.

Materials and methods

This study included 32 smokers under routine clinical care who underwent chest CT performed by a U-HRCT scanner. Chronic obstructive pulmonary disease (COPD) was diagnosed in 13 of the 32 participants. Scan data were reconstructed by 2 different protocols: i) U-HRCT mode with a 1024×1024 matrix and 0.25-mm slice thickness and ii) conventional HRCT mode with a 512×512 matrix and 0.5-mm slice thickness. On both types of scans, lesions of emphysema were quantitatively assessed as percentage of low attenuation volume (LAV%, <-950 Hounsfield units). LAV% values determined for scan data from the U-HRCT and conventional HRCT modes were compared by the Wilcoxon matched-pairs signed rank test. The association between LAV% and forced expiratory volume in 1 s per forced vital capacity (FEV₁/FVC) was assessed by the Spearman rank correlation test.

Results

Mean values for LAV% determined for the U-HRCT and conventional HRCT modes were 8.9 ± 8.8% and 7.3 ± 8.4%, respectively (P<0.0001). The correlation coefficients for LAV% and FEV₁/FVC on the U-HRCT and conventional HRCT modes were 0.50 and 0.49, respectively (both P<0.01).

Conclusion

Compared with conventional HRCT scans, U-HRCT scans reveal emphysematous lesions in greater detail, and provide slightly increased correlation with airflow limitation.

The relationship of airway structural changes to blood and bronchoalveolar lavage biomarkers, and lung function abnormalities in asthma

BACKGROUND

Airway structural changes are important in asthma pathology and require further investigations.

OBJECTIVE

We sought to evaluate which computed tomography (CT) indices, bronchial histological traits, or blood and bronchoalveolar lavage (BAL) biomarkers correlate best with lung function abnormalities in asthma.

METHODS

In 105 white adult asthmatics (53 with a component of fixed airflow obstruction), we determined airway cross-sectional geometry of two proximal (the right upper lobe apical segmental and the left apicoposterior) and two distal (the right and the left basal posterior) bronchi, quantified the low-attenuation lung area (LAA%), and analysed clusters based on airway CT-metrics. We also performed bronchofiberoscopy with BAL and endobronchial biopsy, assessed blood and BAL biomarkers, including interleukin (IL)-4, IL-5, IL-6, IL-10, IL-12p70, IL-17A, IL-23, interferon (INF)γ and periostin, together with circulating a disintegrin and metalloproteinase domain-containing protein (ADAM)33, and investigated interplays between analysed variables.

RESULTS

Patients with fixed airflow limitation were characterized by lower lumen area and increased wall area and wall thickness ratios in distal airways, accompanied by raised LAA%. They had also higher blood neutrophilia, blood and BAL eosinophilia, increased circulating fibrinogen, periostin, and ADAM33. Blood neutrophilia, serum high density lipoproteins, thyroid-stimulating hormone, and shortened activated partial thromboplastin time were determinants of thicker reticular basement membrane (RBM). BAL eosinophilia was the only positive predictor of collagen I accumulation. Surprisingly, we observed a negative correlation between RBM thickening and collagen I deposit. Cluster analysis based on CT-metrics of the right lower lobe basal posterior bronchus revealed three well-separated clusters similar in age, asthma duration, and BMI, but different in RBM thickness, collagen I accumulation, and inflammatory markers.

CONCLUSIONS AND CLINICAL RELEVANCE

Airway remodelling traits are mainly related to the Th₂ profile, higher circulating ADAM33, and blood neutrophilia. Lung function abnormalities and RBM thickening correlate better with CT-metrics of distal than proximal airways.

Lung Function, Coronary Artery Disease, and Mortality in HIV

Rationale: Impaired lung function is a potent independent predictor of coronary artery disease (CAD) in individuals without human immunodeficiency virus (HIV) infection; however, the relationship between lung function and CAD in HIV remains undefined. Objectives: To examine the relationship between lung function, CAD, mortality, and circulating biomarkers in HIV. Methods: Spirometry, diffusing capacity of the lung for carbon monoxide (DlCO), emphysema, coronary artery calcium, mortality, cause of death, and biomarkers were examined in HIV-infected and uninfected individuals enrolled in a cohort study at the University of Pittsburgh. Results were then validated in the Multicenter AIDS Cohort Study (MACS) cohort. Results: We examined data on 234 participants in the Pittsburgh cohort. The mean ± standard deviation age was 49.5 ± 10.2 years old, 82.1% were male, and 67.5% were ever smokers. Among the 177 of 234 individuals with HIV infection, lower DlCO (not forced expiratory volume in 1 second or emphysema) was independently associated with greater coronary artery calcium (odds ratio, 1.43 per 10% lower DlCO; 95% confidence interval, 1.14-1.81). HIV-infected individuals with both reduced DlCO and coronary artery calcium had a much higher mortality than those with either low DlCO or coronary calcium alone or with neither condition. Endothelin-1, a circulating biomarker of endothelial dysfunction, was associated with both lower DlCO and greater coronary artery calcium in those with HIV infection. Results were reproducible in 144 individuals enrolled in the MACS cohort; intercellular adhesion molecule 1 was the biomarker of endothelial dysfunction assessed in the MACS cohort. Conclusions: Impaired DlCO and CAD were associated with each other and with higher mortality in individuals with HIV infection.

Role of Quantitative Computed Tomographic Scan Analysis in Lung Volume Reduction for Emphysema

Recent advances in bronchoscopic lung volume reduction (BLVR) offer new therapeutic alternatives for patients with emphysema and hyperinflation. Endobronchial valves and coils are 2 potential BLVR techniques which have been shown to improve pulmonary function and the quality of life in patients with emphysema. Current patient selection for LVR procedures relies on 3 main inclusion criteria: low attenuation area (in %), also known as emphysema score, heterogeneity score, and fissure integrity score. Volumetric analysis in combination with densitometric analysis of the affected lung lobe or segment with quantitative CT to determine emphysema severity play an important role in treatment planning and post-operative assessment. Due to the variations in lung anatomy, manual corrections are often required to ensure successful and accurate lobe segmentation for pathological and post-treatment CT scan analysis. The advanced development and utilisation of quantitative CT do not simply represent regional changes in pulmonary function but aids in analysis for better patient selection with severe emphysema who are most likely to benefit from BLVR.