CT-Definable Subtypes of Chronic Obstructive Pulmonary Disease: A Statement of the Fleischner Society

Unravelling young COPD and pre-COPD in the general population

Background

Chronic obstructive pulmonary disease (COPD) is commonly diagnosed when the airflow limitation is well established and symptomatic. We aimed to identify individuals at risk of developing COPD according to the concept of pre-COPD and compare their clinical characteristics with 1) those who have developed the disease at a young age, and 2) the overall population with and without COPD.

Methods

The EPISCAN II study is a cross-sectional, population-based study that aims to investigate the prevalence of COPD in Spain in subjects ≥40 years of age. Pre-COPD was defined as the presence of emphysema >5% and/or bronchial thickening by computed chromatography (CT) scan and/or diffusing capacity of the lung for carbon monoxide (D _LCO) <80% of predicted in subjects with respiratory symptoms and post-bronchodilator forced expiratory volume in 1 s/forced vital capacity (FEV₁/FVC) >0.70. Young COPD was defined as FEV₁/FVC <0.70 in a subject ≤50 years of age. Demographic and clinical characteristics were compared among pre-COPD, young COPD and the overall population with and without COPD.

Results

Among the 1077 individuals with FEV₁/FVC <0.70, 65 (6.0%) were ≤50 years of age. Among the 8015 individuals with FEV₁/FVC >0.70, 350 underwent both D _LCO testing and chest CT scanning. Of those, 78 (22.3%) subjects fulfilled the definition of pre-COPD. Subjects with pre-COPD were older, predominantly women, less frequently active or ex-smokers, with less frequent previous diagnosis of asthma but with higher symptomatic burden than those with young COPD.

Conclusions

22.3% of the studied population was at risk of developing COPD, with similar symptomatic and structural changes to those with well-established disease without airflow obstruction. This COPD at-risk population is different from those that develop COPD at a young age.

Radiomic phenotyping of the lung parenchyma in a lung cancer screening cohort

High-throughput extraction of radiomic features from low-dose CT scans can characterize the heterogeneity of the lung parenchyma and potentially aid in identifying subpopulations that may have higher risk of lung diseases, such as COPD, and lung cancer due to inflammation or obstruction of the airways. We aim to determine the feasibility of a lung radiomics phenotyping approach in a lung cancer screening cohort, while quantifying the effect of different CT reconstruction algorithms on phenotype robustness. We identified low-dose CT scans (n = 308) acquired with Siemens Healthineers scanners from patients who completed low-dose CT within our lung cancer screening program between 2015 and 2018 and had two different sets of image reconstructions kernel available (i.e., medium (I30f.), sharp (I50f.)) for the same acquisition. Following segmentation of the lung field, a total of 26 radiomic features were extracted from the entire 3D lung-field using a previously validated fully-automated lattice-based software pipeline, adapted for low-dose CT scans. The lattice in-house software was used to extract features including gray-level histogram, co-occurrence, and run-length descriptors. The lattice approach uses non-overlapping windows for traversing along pixels of images and calculates different features. Each feature was averaged for each scan within a range of lattice window sizes (W) of 4, 8 and 20 mm. The extracted imaging features from both datasets were harmonized to correct for differences in image acquisition parameters. Subsequently, unsupervised hierarchical clustering was applied on the extracted features to identify distinct phenotypic patterns of the lung parenchyma, where consensus clustering was used to identify the optimal number of clusters (K = 2). Differences between phenotypes for demographic and clinical covariates including sex, age, BMI, pack-years of smoking, Lung-RADS and cancer diagnosis were assessed for each phenotype cluster, and then compared across clusters for the two different CT reconstruction algorithms using the cluster entanglement metric, where a lower entanglement coefficient corresponds to good cluster alignment. Furthermore, an independent set of low-dose CT scans (n = 88) from patients with available pulmonary function data on lung obstruction were analyzed using the identified optimal clusters to assess associations to lung obstruction and validate the lung phenotyping paradigm. Heatmaps generated by radiomic features identified two distinct lung parenchymal phenotype patterns across different feature extraction window sizes, for both reconstruction algorithms (P < 0.05 with K = 2). Associations of radiomic-based clusters with clinical covariates showed significant differences for BMI and pack-years of smoking (P < 0.05) for both reconstruction kernels. Radiomic phenotype patterns were more similar across the two reconstructed kernels, when smaller window sizes (W = 4 and 8 mm) were used for radiomic feature extraction, as deemed by their entanglement coefficient. Validation of clustering approaches using cluster mapping for the independent sample with lung obstruction also showed two statistically significant phenotypes (P < 0.05) with significant difference for BMI and smoking pack-years. Radiomic analysis can be used to characterize lung parenchymal phenotypes from low-dose CT scans, which appear reproducible for different reconstruction kernels. Further work should seek to evaluate the effect of additional CT acquisition parameters and validate these phenotypes in characterizing lung cancer screening populations, to potentially better stratify disease patterns and cancer risk.

Utility of Lung Perfusion SPECT/CT in Detection of Pulmonary Thromboembolic Disease: Outcome Analysis

Purpose

To evaluate the clinical outcome of Q-SPECT/CT in pulmonary thromboembolic disease.

Methods

From Jan 2020 to Jan 2021, 30 consecutive patients (M:F = 8:22; median age = 52 year (21-89)) suspected of having acute pulmonary embolism (PE) or chronic thromboembolic pulmonary hypertension (CTEPH) were referred for non-contrasted Q-SPECT/CT. All patients were COVID-19 PCR negative. MSKCC Q-SPECT/CT and/or PISAPED criteria were used to determine the presence of thromboembolic disease in Q-SPECT/CT. Final diagnosis was made based on composite reference standards that included at least 2-month clinical cardiorespiratory assessment and follow-up imaging.

Results

Q-SPECT/CT was positive in 19 patients: indeterminate in 1 and 10 were negative. Three false positive cases were observed during follow-up. Of the remaining 16 true positives, all patients' cardiorespiratory symptom were improved or stabilised after treatment with anticoagulants. The overall sensitivity, specificity, PPV, NPV and accuracy of Q-SPECT/CT were 100% (95% CI, 79.41-100%), 78.57% (95% CI, 49.20-95.34%), 84.21% (95% CI, 66.41-93.57%), 100% and 90.00% (95% CI, 73.47-97.89%) respectively.

Conclusions

In the current COVID-19 pandemic, Q-SPECT/CT can be an alternative modality to detect pulmonary thromboembolic disease. Normal Q-SPECT/CT excludes pulmonary thromboembolic disease with high degree of certainty. However, false positive has been observed.

Reporting and management of incidental lung findings on computed tomography: beyond lung nodules

Non-nodular incidental lung findings can broadly be categorised as airway- or airspace-related abnormalities and diffuse parenchymal abnormalities. Airway-related abnormalities include bronchial dilatation and thickening, foci of low attenuation, emphysema, and congenital variants. Diffuse parenchymal abnormalities relate to the spectrum of diffuse parenchymal lung diseases cover a spectrum from interstitial lung abnormalities (ILAs) and pulmonary cysts to established diffuse parenchymal lung abnormalities such as the idiopathic interstitial pneumonias and cystic lung diseases. In this review, we discuss the main manifestations of these incidental findings, paying attention to their prevalence and importance, descriptors to use when reporting, the limits of what can be considered "normal", and conclude each section with some pragmatic reporting recommendations. We also highlight technical and patient factors which can lead to spurious abnormalities.

Predicting severe chronic obstructive pulmonary disease exacerbations using quantitative CT: a retrospective model development and external validation study

BACKGROUND

Quantitative CT is becoming increasingly common for the characterisation of lung disease; however, its added potential as a clinical tool for predicting severe exacerbations remains understudied. We aimed to develop and validate quantitative CT-based models for predicting severe chronic obstructive pulmonary disease (COPD) exacerbations.

METHODS

We analysed the Subpopulations and Intermediate Outcome Measures In COPD Study (SPIROMICS) cohort, a multicentre study done at 12 clinical sites across the USA, of individuals aged 40-80 years from four strata: individuals who never smoked, individuals who smoked but had normal spirometry, individuals who smoked and had mild to moderate COPD, and individuals who smoked and had severe COPD. We used 3-year follow-up data to develop logistic regression classifiers for predicting severe exacerbations. Predictors included age, sex, race, BMI, pulmonary function, exacerbation history, smoking status, respiratory quality of life, and CT-based measures of density gradient texture and airway structure. We externally validated our models in a subset from the Genetic Epidemiology of COPD (COPDGene) cohort. Discriminative model performance was assessed using the area under the receiver operating characteristic curve (AUC), which was also compared with other predictors, including exacerbation history and the BMI, airflow obstruction, dyspnoea, and exercise capacity (BODE) index. We evaluated model calibration using calibration plots and Brier scores.

FINDINGS

Participants in SPIROMICS were enrolled between Nov 12, 2010, and July 31, 2015. Participants in COPDGene were enrolled between Jan 10, 2008, and April 15, 2011. We included 1956 participants from the SPIROMICS cohort who had complete 3-year follow-up data: the mean age of the cohort was 63·1 years (SD 9·2) and 1017 (52%) were men and 939 (48%) were women. Among the 1956 participants, 434 (22%) had a history of at least one severe exacerbation. For the CT-based models, the AUC was 0·854 (95% CI 0·852-0·855) for at least one severe exacerbation within 3 years and 0·931 (0·930-0·933) for consistent exacerbations (defined as ≥1 acute episode in each of the 3 years). Models were well calibrated with low Brier scores (0·121 for at least one severe exacerbation; 0·039 for consistent exacerbations). For the prediction of at least one severe event during 3-year follow-up, AUCs were significantly higher with CT biomarkers (0·854 [0·852-0·855]) than exacerbation history (0·823 [0·822-0·825]) and BODE index 0·812 [0·811-0·814]). 6965 participants were included in the external validation cohort, with a mean age of 60·5 years (SD 8·9). In this cohort, AUC for at least one severe exacerbation was 0·768 (0·767-0·769; Brier score 0·088).

INTERPRETATION

CT-based prediction models can be used for identification of patients with COPD who are at high risk of severe exacerbations. The newly identified CT biomarkers could potentially enable investigation into underlying disease mechanisms responsible for exacerbations.

FUNDING

National Institutes of Health and the National Heart, Lung, and Blood Institute.

Evaluation of emphysema on thoracic low-dose CTs through attention-based multiple instance deep learning

In addition to lung cancer, other thoracic abnormalities, such as emphysema, can be visualized within low-dose CT scans that were initially obtained in cancer screening programs, and thus, opportunistic evaluation of these diseases may be highly valuable. However, manual assessment for each scan is tedious and often subjective, thus we have developed an automatic, rapid computer-aided diagnosis system for emphysema using attention-based multiple instance deep learning and 865 LDCTs. In the task of determining if a CT scan presented with emphysema or not, our novel Transfer AMIL approach yielded an area under the ROC curve of 0.94 ± 0.04, which was a statistically significant improvement compared to other methods evaluated in our study following the Delong Test with correction for multiple comparisons. Further, from our novel attention weight curves, we found that the upper lung demonstrated a stronger influence in all scan classes, indicating that the model prioritized upper lobe information. Overall, our novel Transfer AMIL method yielded high performance and provided interpretable information by identifying slices that were most influential to the classification decision, thus demonstrating strong potential for clinical implementation.

Associations of the distance-saturation product and low-attenuation area percentage in pulmonary computed tomography with acute exacerbation in patients with chronic obstructive pulmonary disease

Background

Chronic obstructive pulmonary disease (COPD) has high global health concerns, and previous research proposed various indicators to predict mortality, such as the distance-saturation product (DSP), derived from the 6-min walk test (6MWT), and the low-attenuation area percentage (LAA%) in pulmonary computed tomographic images. However, the feasibility of using these indicators to evaluate the stability of COPD still remains to be investigated. Associations of the DSP and LAA% with other COPD-related clinical parameters are also unknown. This study, thus, aimed to explore these associations.

Methods

This retrospective study enrolled 111 patients with COPD from northern Taiwan. Individuals' data we collected included results of a pulmonary function test (PFT), 6MWT, life quality survey [i.e., the modified Medical Research Council (mMRC) scale and COPD assessment test (CAT)], history of acute exacerbation of COPD (AECOPD), and LAA%. Next, the DSP was derived by the distance walked and the lowest oxygen saturation recorded during the 6MWT. In addition, the DSP and clinical phenotype grouping based on clinically significant outcomes by previous study approaches were employed for further investigation (i.e., DSP of 290 m%, LAA% of 20%, and AECOPD frequency of ≥1). Mean comparisons and linear and logistic regression models were utilized to explore associations among the assessed variables.

Results

The low-DSP group (<290 m%) had significantly higher values for the mMRC, CAT, AECOPD frequency, and LAA% at different lung volume scales (total, right, and left), whereas it had lower values of the PFT and 6MWT parameters compared to the high-DSP group. Significant associations (with high odds ratios) were observed of the mMRC, CAT, AECOPD frequency, and PFT with low- and high-DSP groupings. Next, the risk of having AECOPD was associated with the mMRC, CAT, DSP, and LAA% (for the total, right, and left lungs).

Conclusion

A lower value of the DSP was related to a greater worsening of symptoms, more-frequent exacerbations, poorer pulmonary function, and more-severe emphysema (higher LAA%). These readily determined parameters, including the DSP and LAA%, can serve as indicators for assessing the COPD clinical course and may can serve as a guide to corresponding treatments.

Peribronchiolar Metaplasia: A Marker of Cigarette Smoke-Induced Small Airway Injury in a Rural Cohort

Background

Peribronchiolar metaplasia (PBM) is considered a reaction to injury characterized by the proliferation of bronchiolar epithelium into immediately adjacent alveolar walls. While an association of PBM with diffuse interstitial lung diseases has been recognized, the clinical significance of PBM remains uncertain.

Methods

A cohort (n = 352) undergoing surgical resection of a lung nodule/mass in a rural area was retrospectively reviewed. Multivariate logistic regression analysis was performed to determine the association of PBM with clinical, physiological, radiographic, and histologic endpoints.

Results

In the total study cohort, 9.1% were observed to have PBM as a histologic finding in resected lung tissue (n = 32). All but one of these patients with PBM were ever-smokers with a median of 42 pack years. Clinical COPD was diagnosed in two-thirds of patients with PBM. Comorbid gastroesophageal reflux disease (GERD) was significantly associated with PBM. All patients with PBM demonstrated radiologic and histologic evidence of emphysema. Measures of pulmonary function were not impacted by PBM. Mortality was not associated with the histologic observation of PBM. In a logistic regression model, centrilobular-ground glass opacity interstitial lung abnormality and traction bronchiectasis on the CT scan of the chest and histologic evidence of fibrosis, desquamative interstitial pneumonia and anthracosis all strongly predicted PBM in the cohort.

Conclusion

A constellation of radiologic and histologic smoking-related abnormalities predicted PBM in study cohort. This confirms a co-existence of lung tissue responses to smoking including PBM, emphysema, and fibrosis. Acknowledging the physiologically "silent" nature of small airway dysfunction on pulmonary function testing, our findings support PBM as a histologic marker of small-airway injury associated with cigarette smoking.

Development and validation of a prediction model of pneumothorax after CT-guided coaxial core needle lung biopsy

BACKGROUND

Pneumothorax is the most common complication of computed tomography-guided coaxial core needle biopsy (CCNB) and may be life-threatening. We aimed to evaluate the risk factors and develop a model for predicting pneumothorax in patients undergoing computed tomography-guided CCNB, and to further determine its clinical utility.

METHODS

Univariate and multivariate logistic regression analyses were conducted to identify independent risk factors for pneumothorax from 18 variables. A predictive model was established using multivariable logistic regression and presented as a nomogram based on a training cohort of 690 patients who underwent computed tomography-guided CCNB. The model was validated in 253 consecutive patients in the validation cohort and 250 patients in the test cohort. The area under the curve was used to determine the predictive accuracy of the proposed model.

RESULTS

The risk factors associated with pneumothorax after computed tomography-guided CCNB were sex, patient position, lung field, lesion contact with the pleura, lesion size, distance from the pleura to the lesion, presence of emphysema adjacent to the biopsy tract, and crossing fissures. The predictive model that incorporated these predictors showed good predictive performance in the training cohort [area under the curve, 0.71 (95% confidence interval: 0.67-0.75)], validation cohort [0.71 (0.64-0.78)], and internal test cohort [0.68 (0.60-0.75)]. The nomogram also provided excellent calibration and discrimination, and decision curve analysis (DCA) demonstrated its clinical utility.

CONCLUSIONS

The predictive model showed good performance for pneumothorax after computed tomography-guided CCNB and may help improve individualized preoperative prediction.

Chronic Obstructive Pulmonary Disease and Small Airways Diseases

The small airways are a common target of injury within the lungs and may be affected by a wide variety of inhaled, systemic, and other disorders. Imaging is critical in the detection and diagnosis of small airways disease since significant injury may occur prior to pulmonary function tests showing abnormalities. The goal of this article is to describe the typical imaging findings and patterns of small airways diseases. An approach which divides the imaging appearances into four categories (tree-in-bud opacities, poorly defined centrilobular nodules, mosaic attenuation, and emphysema) will provide a framework in which to formulate appropriate and focused differential diagnoses.

Unsupervised Learning Identifies Computed Tomographic Measurements as Primary Drivers of Progression, Exacerbation, and Mortality in Chronic Obstructive Pulmonary Disease

Rationale: Chronic obstructive pulmonary disease (COPD) is a heterogeneous syndrome with phenotypic manifestations that tend to be distributed along a continuum. Unsupervised machine learning based on broad selection of imaging and clinical phenotypes may be used to identify primary variables that define disease axes and stratify patients with COPD. Objectives: To identify primary variables driving COPD heterogeneity using principal component analysis and to define disease axes and assess the prognostic value of these axes across three outcomes: progression, exacerbation, and mortality. Methods: We included 7,331 patients between 39 and 85 years old, of whom 40.3% were Black and 45.8% were female smokers with a mean of 44.6 pack-years, from the COPDGene (Genetic Epidemiology of COPD) phase I cohort (2008-2011) in our analysis. Out of a total of 916 phenotypes, 147 continuous clinical, spirometric, and computed tomography (CT) features were selected. For each principal component (PC), we computed a PC score based on feature weights. We used PC score distributions to define disease axes along which we divided the patients into quartiles. To assess the prognostic value of these axes, we applied logistic regression analyses to estimate 5-year (n = 4,159) and 10-year (n = 1,487) odds of progression. Cox regression and Kaplan-Meier analyses were performed to estimate 5-year and 10-year risk of exacerbation (n = 6,532) and all-cause mortality (n = 7,331). Results: The first PC, accounting for 43.7% of variance, was defined by CT measures of air trapping and emphysema. The second PC, accounting for 13.7% of variance, was defined by spirometric and CT measures of vital capacity and lung volume. The third PC, accounting for 7.9% of the variance, was defined by CT measures of lung mass, airway thickening, and body habitus. Stratification of patients across each disease axis revealed up to 3.2-fold (95% confidence interval [CI] 2.4, 4.3) greater odds of 5-year progression, 5.4-fold (95% CI 4.6, 6.3) greater risk of 5-year exacerbation, and 5.0-fold (95% CI 4.2, 6.0) greater risk of 10-year mortality between the highest and lowest quartiles. Conclusions: Unsupervised learning analysis of the COPDGene cohort reveals that CT measurements may bolster patient stratification along the continuum of COPD phenotypes. Each of the disease axes also individually demonstrate prognostic potential, predictive of future forced expiratory volume in 1 second decline, exacerbation, and mortality.

Chest CT Findings in Marijuana Smokers

Background Global consumption of marijuana is increasing, but there is a paucity of evidence concerning associated lung imaging findings. Purpose To use chest CT to investigate the effects of marijuana smoking in the lung. Materials and Methods This retrospective case-control study evaluated results of chest CT examinations (from October 2005 to July 2020) in marijuana smokers, nonsmoker control patients, and tobacco-only smokers. We compared rates of emphysema, airway changes, gynecomastia, and coronary artery calcification. Age- and sex-matched subgroups were created for comparison with tobacco-only smokers older than 50 years. Results were analyzed using χ² tests. Results A total of 56 marijuana smokers (34 male; mean age, 49 years ± 14 [SD]), 57 nonsmoker control patients (32 male; mean age, 49 years ± 14), and 33 tobacco-only smokers (18 male; mean age, 60 years ± 6) were evaluated. Higher rates of emphysema were seen among marijuana smokers (42 of 56 [75%]) than nonsmokers (three of 57 [5%]) (P < .001) but not tobacco-only smokers (22 of 33 [67%]) (P = .40). Rates of bronchial thickening, bronchiectasis, and mucoid impaction were higher among marijuana smokers compared with the other groups (P < .001 to P = .04). Gynecomastia was more common in marijuana smokers (13 of 34 [38%]) than in control patients (five of 32 [16%]) (P = .039) and tobacco-only smokers (two of 18 [11%]) (P = .040). In age-matched subgroup analysis of 30 marijuana smokers (23 male), 29 nonsmoker control patients (17 male), and 33 tobacco-only smokers (18 male), rates of bronchial thickening, bronchiectasis, and mucoid impaction were again higher in the marijuana smokers than in the tobacco-only smokers (P < .001 to P = .006). Emphysema rates were higher in age-matched marijuana smokers (28 of 30 [93%]) than in tobacco-only smokers (22 of 33 [67%]) (P = .009). There was no difference in rate of coronary artery calcification between age-matched marijuana smokers (21 of 30 [70%]) and tobacco-only smokers (28 of 33 [85%]) (P = .16). Conclusion Airway inflammation and emphysema were more common in marijuana smokers than in nonsmokers and tobacco-only smokers, although variable interobserver agreement and concomitant cigarette smoking among the marijuana-smoking cohort limits our ability to draw strong conclusions. © RSNA, 2022 See also the editorial by Galvin and Franks in this issue.

Computed Tomography Evaluation of In Vivo Pulmonary Cryoablation Zone Sizes

PURPOSE

To evaluate ablation zone sizes in patients undergoing pulmonary tumor cryoablation with 14-gauge cryoablation probes.

MATERIALS AND METHODS

A single-center retrospective analysis of all consecutive patients who underwent cryoablation of pulmonary tumors with 1 or more 14-gauge probes (August 2017 to June 2020) was performed. Intraprocedural and 1-2-month postprocedural chest computed tomography (CT) scans were evaluated to characterize pulmonary lesions, ice balls, and ablation zones. Single-probe 14-gauge ablation zone volumes were compared with manufacturer reference isotherms and single- and 2-probe ablation zones from a prior investigation of 17-gauge probes. Overall survival and local recurrence-free survival were calculated to 3 years.

RESULTS

Forty-seven pulmonary malignancies in 42 patients (women, 50%; mean age, 75.2 years ± 11.5) underwent cryoablation with 1 (n = 35), 2 (n = 10), or 3 (n = 2) cryoablation probes. One- to 2-month follow-up CT images were available for 30 of the 42 patients. The mean cryoablation zone volumes at 1-2 months when 1 (n = 21), 2 (n = 8), and 3 (n = 1) probes were used were 5.0 cm³ ± 2.3, 37.5 cm³ ± 20.5, and 28.4 cm³, respectively. The mean single-probe follow-up ablation zone volume was larger than that previously reported for 17-gauge probes (3.0 cm³ ± 0.3) (P < .001) but smaller than manufacturer-reported isotherms (11.6 cm³ for -40 °C isotherm) and the 2-probe ablation zone volume with 17-gauge devices (12.9 cm³ ± 2.4) (for all, P < 001). The 3-year overall survival and local recurrence-free survival were 69% (95% confidence interval [CI], 53%-89%) and 87% (95% CI, 74%-100%), respectively.

CONCLUSIONS

Fourteen-gauge probes generate larger ablation volumes than those generated by 17-gauge probes. Manufacturer-reported isotherms are significantly larger than actual cryoablation zones. Cryoablation can attain low rates of local recurrence.

Two in one: Overlapping CT findings of COVID-19 and underlying lung diseases

Coronavirus disease 2019 (COVID-19) is associated with pneumonia and has various pulmonary manifestations on computed tomography (CT). Although COVID-19 pneumonia is usually seen as bilateral predominantly peripheral ground-glass opacities with or without consolidation, it can present with atypical radiological findings and resemble the imaging findings of other lung diseases. Diagnosis of COVID-19 pneumonia is much more challenging for both clinicians and radiologists in the presence of pre-existing lung disease. The imaging features of COVID-19 and underlying lung disease can overlap and obscure the findings of each other. Knowledge of the radiological findings of both diseases and possible complications, correct diagnosis, and multidisciplinary consensus play key roles in the appropriate management of diseases. In this pictorial review, the chest CT findings are presented of patients with underlying lung diseases and overlapping COVID-19 pneumonia and the various reasons for radiological lung abnormalities in these patients are discussed.

Radiographic Phenotypes Affect the Risk of Inhaled Corticosteroid-Associated Pneumonia in Patients with COPD

Purpose

Few studies have reported the association between the radiographic characteristics and the development of pneumonia in patients with chronic obstructive pulmonary disease (COPD) treated with inhaled corticosteroids (ICSs). Our study aimed to assess the effect of radiographic phenotypes on the risk of pneumonia in patients treated with ICSs.

Patients and Methods

This study retrospectively analysed all patients with COPD treated with ICSs in a subset of the Korea Chronic Obstructive Pulmonary Disorders Subgroup Study registry between January 2017 and December 2019. The association between radiographic phenotypes including the presence and severity of emphysema, airway wall thickening, or bronchiectasis on chest computed tomography were determined visually/qualitatively and the risk of pneumonia was analyzed using the Cox regression model.

Results

Among the 90 patients with COPD treated with ICSs, 41 experienced pneumonia more than once during the median follow-up of 29 (interquartile range, 8–35) months. In univariate Cox regression analysis, older age, longer use of ICSs, use of fluticasone propionate or metered dose inhaler, and severe exacerbation events increased the risk of pneumonia. In multivariate analysis, the presence of emphysema (adjusted hazard ratio [aHR]=3.73, P=0.033), severity measured using the visual sum score (mild-to-moderate, aHR=8.58, P=0.016; severe, aHR=3.58, P=0.042), Goddard sum score (mild-to-moderate, aHR=3.31, P=0.058; severe, aHR=5.38, P=0.014), and the upper lobe distribution of emphysema (aHR=3.76, P=0.032) were associated with a higher risk of pneumonia. Subtypes of centrilobular and panlobular emphysema had a higher risk of pneumonia compared with paraseptal emphysema (aHR=3.98, P=0.033; HR=3.91, P=0.041 vs HR=2.74, P=0.304). The presence of bronchiectasis (aHR=2.41, P=0.02) and emphysema/bronchiectasis overlap phenotype (aHR=2.19, P=0.053) on chest CT was a risk factor for pneumonia in this population. However, severity of bronchiectasis and the presence or severity of bronchial wall thickening according to the visual sum score were not associated with the risk of pneumonia.

Conclusion

Among patients with COPD treated with ICSs, radiographic phenotypes including the presence of emphysema, bronchiectasis or emphysema/bronchiectasis overlap phenotype, severity with emphysema, subtypes of centrilobular or panlobular emphysema, and upper lobe distribution of emphysema may help predict the risk of pneumonia.

Lung Radiomics Features Selection for COPD Stage Classification Based on Auto-Metric Graph Neural Network

Chronic obstructive pulmonary disease (COPD) is a preventable, treatable, progressive chronic disease characterized by persistent airflow limitation. Patients with COPD deserve special consideration regarding treatment in this fragile population for preclinical health management. Therefore, this paper proposes a novel lung radiomics combination vector generated by a generalized linear model (GLM) and Lasso algorithm for COPD stage classification based on an auto-metric graph neural network (AMGNN) with a meta-learning strategy. Firstly, the parenchyma images were segmented from chest high-resolution computed tomography (HRCT) images by ResU-Net. Second, lung radiomics features are extracted from the parenchyma images by PyRadiomics. Third, a novel lung radiomics combination vector (3 + 106) is constructed by the GLM and Lasso algorithm for determining the radiomics risk factors (K = 3) and radiomics node features (d = 106). Last, the COPD stage is classified based on the AMGNN. The results show that compared with the convolutional neural networks and machine learning models, the AMGNN based on constructed novel lung radiomics combination vector performs best, achieving an accuracy of 0.943, precision of 0.946, recall of 0.943, F1-score of 0.943, and ACU of 0.984. Furthermore, it is found that our method is effective for COPD stage classification.

[Computed tomographic imaging in chronic obstructive pulmonary disease : What pulmonologists and thoracic surgeons want to know]

Chronic obstructive pulmonary disease (COPD) begins with chronic inflammation of the bronchial system and leads to the development of emphysema in many patients. COPD patients are characterized by reduced performance, dyspnea in the context of an obstructive respiratory disorder and increased susceptibility to infections. COPD has a major impact on public health, as it is very common and many patients die from it. The most important preventable cause of COPD is tobacco smoke inhalation, which is why consistent smoking cessation is the most important component of any COPD treatment. There is no causal therapy, but in severely symptomatic patients with advanced emphysema, respiratory mechanics can be improved by lung volume reduction if all conservative treatment options have been exhausted. Diagnostic imaging is of great importance in the care of COPD patients. This article summarizes which indications warrant the performance of computed tomography (CT) and what we should pay special attention to during image analysis in order to provide optimal advice to our clinical partners.

Emphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study

Background Visual assessment remains the standard for evaluating emphysema at CT; however, it is time consuming, is subjective, requires training, and is affected by variability that may limit sensitivity to longitudinal change. Purpose To evaluate the clinical and imaging significance of increasing emphysema severity as graded by a deep learning algorithm on sequential CT scans in cigarette smokers. Materials and Methods A secondary analysis of the prospective Genetic Epidemiology of Chronic Obstructive Pulmonary Disease (COPDGene) study participants was performed and included baseline and 5-year follow-up CT scans from 2007 to 2017. Emphysema was classified automatically according to the Fleischner emphysema grading system at baseline and 5-year follow-up using a deep learning model. Baseline and change in clinical and imaging parameters at 5-year follow-up were compared in participants whose emphysema progressed versus those who did not. Kaplan-Meier analysis and multivariable Cox regression were used to assess the relationship between emphysema score progression and mortality. Results A total of 5056 participants (mean age, 60 years ± 9 [SD]; 2566 men) were evaluated. At 5-year follow-up, 1293 of the 5056 participants (26%) had emphysema progression according to the Fleischner grading system. This group demonstrated progressive airflow obstruction (forced expiratory volume in 1 second [percent predicted]: -3.4 vs -1.8), a greater decline in 6-minute walk distance (-177 m vs -124 m), and greater progression in quantitative emphysema extent (adjusted lung density: -1.4 g/L vs 0.5 g/L; percentage of lung voxels with CT attenuation less than -950 HU: 0.6 vs 0.2) than those with nonprogressive emphysema (P < .001 for each). Multivariable Cox regression analysis showed a higher mortality rate in the group with emphysema progression, with an estimated hazard ratio of 1.5 (95% CI: 1.2, 1.8; P < .001). Conclusion An increase in Fleischner emphysema grade on sequential CT scans using an automated deep learning algorithm was associated with increased functional impairment and increased risk of mortality. ClinicalTrials.gov registration no. NCT00608764 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Grenier in this issue.

Syndrome of Combined Pulmonary Fibrosis and Emphysema: An Official ATS/ERS/JRS/ALAT Research Statement

Background: The presence of emphysema is relatively common in patients with fibrotic interstitial lung disease. This has been designated combined pulmonary fibrosis and emphysema (CPFE). The lack of consensus over definitions and diagnostic criteria has limited CPFE research. Goals: The objectives of this task force were to review the terminology, definition, characteristics, pathophysiology, and research priorities of CPFE and to explore whether CPFE is a syndrome. Methods: This research statement was developed by a committee including 19 pulmonologists, 5 radiologists, 3 pathologists, 2 methodologists, and 2 patient representatives. The final document was supported by a focused systematic review that identified and summarized all recent publications related to CPFE. Results: This task force identified that patients with CPFE are predominantly male, with a history of smoking, severe dyspnea, relatively preserved airflow rates and lung volumes on spirometry, severely impaired DlCO, exertional hypoxemia, frequent pulmonary hypertension, and a dismal prognosis. The committee proposes to identify CPFE as a syndrome, given the clustering of pulmonary fibrosis and emphysema, shared pathogenetic pathways, unique considerations related to disease progression, increased risk of complications (pulmonary hypertension, lung cancer, and/or mortality), and implications for clinical trial design. There are varying features of interstitial lung disease and emphysema in CPFE. The committee offers a research definition and classification criteria and proposes that studies on CPFE include a comprehensive description of radiologic and, when available, pathological patterns, including some recently described patterns such as smoking-related interstitial fibrosis. Conclusions: This statement delineates the syndrome of CPFE and highlights research priorities.

Acute exacerbations of chronic obstructive pulmonary disease in a cohort of Chinese never smokers goes along with decreased risks of recurrent acute exacerbation, emphysema and comorbidity of lung cancer as well as decreased levels of circulating eosinophils and basophils

Acute exacerbations show a significant impact on disease morbidity and mortality in chronic obstructive pulmonary disease (COPD). In contrast to stable COPD, the association of smoking status with clinical and laboratory characteristics in patients with acute exacerbations of COPD (AECOPD) has not been well studied. In this retrospective study, we compared never smokers and ever smokers on their demographic, clinical, and laboratory characteristics in a Chinese clinical cohort of AECOPD. In this cohort comprising 1,034 consecutive patients with AECOPD, never smokers were older (75 vs 70.5 years, padjusted &lt; 0.001) and had a higher body mass index than smokers (21.1 ± 4.0 vs 20.3 ± 3.4, padjusted = 0.028). Furthermore, never smokers showed a decreased risk of recurrent acute exacerbation (13.0 vs 21.8%, padjusted = 0.029), a lower risk of development of emphysema (77.8 vs 89.1%, padjusted &lt; 0.001), a lower prevalence of the co-morbidity of lung cancer (0.5 vs 6.6%, padjusted &lt; 0.001), lower levels of circulating eosinophils (EO; 0.04 × 109/L vs 0.10 × 109/L, padjusted = 0.007) and basophils (BA; 0.02 × 109/L vs 0.03 × 109/L, padjusted = 0.019), and a higher plasma levels of D-dimer (0.62 μg/ml vs 0.51 μg/ml, padjusted = 0.02). Furthermore, multivariate logistic regression analysis identified several risk factor for the recurrent acute exacerbation, such as smoking [odds ratio (OR) = 1.84, 95% CI: 1.03–3.40, p = 0.044], urban residential area (OR = 1.43, 95% CI: 1.01–2.05, p = 0.045), and the presence of emphysema (OR = 2.31, 95% CI: 1.25–4.69, p = 0.012). In conclusion, this study demonstrates that the smoking status of patients is associated with recurrent acute exacerbations, emphysema, lung cancer, and levels of circulating EO and BA in AECOPD. Identification of cigarette smoking as a risk factor for recurrent acute exacerbation supports behavioral intervention of smoking cessation in the management of patients with AECOPD.

Design and Rationale of the HANSE Study: A Holistic German Lung Cancer Screening Trial Using Low-Dose Computed Tomography

BACKGROUND

 Despite the high prevalence and mortality of lung cancer and proven effectiveness of low-dose computed tomography (LDCT) to reduce mortality, Germany still lacks a national screening program. The German Institute for Quality and Efficiency in Health Care (IQWiG) and the Federal Office for Radiation Protection (BfS) both published positive scientific evaluations recommending a quality-controlled national screening program. IQWiG underlined the importance of a clear risk definition, integrated smoking cessation programs, and quality assurance, highlighting the necessity of procedural optimization.

METHODS AND OBJECTIVES

 In the HANSE study, former and current smokers aged 55-79 years are assessed for their lung cancer risk by the NELSON and PLCO_M2012 risk scores. 5000 high-risk participants, defined as PLCO_M2012 6-year risk ≥ 1.58 % or fulfilling NELSON risk inclusion criteria, will be screened by LDCT at baseline and after 12 months. Lung nodules are analyzed by a modified Lung-RADS 1.1 score of the HANSE study, and values of emphysema and coronary calcium are determined and randomly reported to the participants. 7100 low-risk participants serve as a control. All patients are followed-up for up to 10 years. The sensitivity and specificity of the two risk assessments and LDCT screening, effects of the randomized LDCT reporting, efficiency of lung nodule management, and several other factors are assessed to analyze the success and quality of the holistic screening program.

CONCLUSION

 The HANSE study is designed as a holistic lung cancer screening study in northern Germany to answer pressing questions for a successful implementation of an effective German lung cancer screening program.

KEY POINTS

  · HANSE is designed to address pressing questions for the implementation of lung cancer screening in Germany.. · HANSE compares NELSON and PLCOM2012 risk assessments for optimal definition of the high-risk group. . · HANSE integrates cardiac calcium and pulmonary emphysema scoring in a holistic screening approach..

CITATION FORMAT

· Vogel-Claussen J, Lasch F, Bollmann B et al. Design and Rationale of the HANSE Study: A Holistic German Lung Cancer Screening Trial Using Low-Dose Computed Tomography. Fortschr Röntgenstr 2022; DOI: 10.1055/a-1853-8291.

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.

Early detection of COPD based on graph convolutional network and small and weakly labeled data

Chronic obstructive pulmonary disease (COPD) is a common disease with high morbidity and mortality, where early detection benefits the population. However, the early diagnosis rate of COPD is low due to the absence or slight early symptoms. In this paper, a novel method based on graph convolution network (GCN) for early detection of COPD is proposed, which uses small and weakly labeled chest computed tomography image data from the publicly available Danish Lung Cancer Screening Trial database. The key idea is to construct a graph using regions of interest randomly selected from the segmented lung parenchyma and then input it into the GCN model for COPD detection. In this way, the model can not only extract the feature information of each region of interest but also the topological structure information between regions of interest, that is, graph structure information. The proposed GCN model achieves an acceptable performance with an accuracy of 0.77 and an area under a curve of 0.81, which is higher than the previous studies on the same dataset. GCN model also outperforms several state-of-the-art methods trained at the same time. As far as we know, it is also the first time using the GCN model on this dataset for COPD detection.

Precision medicine in chronic obstructive pulmonary disease

Over the last 20 years, it has become possible to use a precision medicine approach to the management of chronic obstructive pulmonary disease (COPD). Clinical and physiological features as well as a blood biomarker can be used to target treatments to patients most likely to benefit and avoid treatment in patients less likely to benefit. Future advances in a precision medicine approach to COPD will depend on more precise characterization of individual patients, possibly using quantitative imaging, new physiological techniques, novel biomarkers and genetic profiling. Precision medicine has led to significant improvements in the management of COPD and clinicians should use all available information to optimize the treatment of individual patients.

The Normal Lung Index From Quantitative Computed Tomography for the Evaluation of Obstructive and Restrictive Lung Disease

PURPOSE

Our objective was to evaluate whether the normal lung index (NLI) from quantitative computed tomography (QCT) analysis can be used to predict mortality as well as pulmonary function tests (PFTs) in patients with chronic obstructive pulmonary disease (COPD) and interstitial lung disease (ILD).

MATERIALS AND METHODS

Normal subjects (n=20) and patients with COPD (n=172) and ILD (n=114) who underwent PFTs and chest CT were enrolled retrospectively in this study. QCT measures included the NLI, defined as the ratio of the lung with attenuation between -950 and -700 Hounsfield units (HU) over the total lung volume (-1024 to -250 HU, mL), high-attenuation area (-700 to -250 HU, %), emphysema index (>6% of pixels < -950 HU), skewness, kurtosis, and mean lung attenuation. Coefficients of correlation between QCT measurements and PFT results in all subjects were calculated. Univariate and multivariate survival analyses were performed to assess mortality prediction by disease.

RESULTS

The Pearson correlation analysis showed that the NLI correlated moderately with the forced expiratory volume in 1 second in subjects with COPD (r=0.490, P<0.001) and the forced vital capacity in subjects with ILD (r=0.452, P<0.001). Multivariate analysis revealed that the NLI of <70% was a significant independent predictor of mortality in subjects with COPD (hazard ratio=3.14, P=0.034) and ILD (hazard ratio=2.72, P=0.005).

CONCLUSION

QCT analysis, specifically the NLI, can also be used to predict mortality in individuals with COPD and ILD.

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.

A Challenge for Emphysema Quantification Using a Deep Learning Algorithm With Low-dose Chest Computed Tomography

PURPOSE

We aimed to identify clinically relevant deep learning algorithms for emphysema quantification using low-dose chest computed tomography (LDCT) through an invitation-based competition.

MATERIALS AND METHODS

The Korean Society of Imaging Informatics in Medicine (KSIIM) organized a challenge for emphysema quantification between November 24, 2020 and January 26, 2021. Seven invited research teams participated in this challenge. In total, 558 pairs of computed tomography (CT) scans (468 pairs for the training set, and 90 pairs for the test set) from 9 hospitals were collected retrospectively or prospectively. CT acquisition followed the hospitals' protocols to reflect the real-world clinical setting. Using the training set, each team developed an algorithm that generated converted LDCT by changing the pixel values of LDCT to simulate those of standard-dose CT (SDCT). The agreement between SDCT and LDCT was evaluated using the intraclass correlation coefficient (ICC; 2-way random effects, absolute agreement, and single rater) for the percentage of low-attenuated area below -950 HU (LAA-950 HU), κ value for emphysema categorization (LAA-950 HU, <5%, 5% to 10%, and ≥10%) and cosine similarity of LAA-950 HU.

RESULTS

The mean LAA-950 HU of the test set was 14.2%±10.5% for SDCT, 25.4%±10.2% for unconverted LDCT, and 12.9%±10.4%, 11.7%±10.8%, and 12.4%±10.5% for converted LDCT (top 3 teams). The agreement between the SDCT and converted LDCT of the first-place team was 0.94 (95% confidence interval: 0.90, 0.97) for ICC, 0.71 (95% confidence interval: 0.58, 0.84) for categorical agreement, and 0.97 (interquartile range: 0.94 to 0.99) for cosine similarity.

CONCLUSIONS

Emphysema quantification with LDCT was feasible through deep learning-based CT conversion strategies.

The diagnosis of early pneumoconiosis in dust-exposed workers: comparison of chest radiography and computed tomography

BACKGROUND

Chest radiography (CR) is employed as the evaluation of pneumoconiosis; however, we sometimes encounter cases in which computed tomography (CT) is more effective in detecting subtle pathological changes or cases in which CR yields false-positive results.

PURPOSE

To compare CR to CT in the diagnosis of early-stage pneumoconiosis.

MATERIAL AND METHODS

CR and CT were performed for 132 workers with an occupational history of mining. We excluded 23 cases of arc-welder's lung. Five readers who were experienced chest radiologists or pulmonologists independently graded the pulmonary small opacities on CR of the remaining 109 cases. We then excluded 37 cases in which the CT data were not sufficient for grading. CT images of the remaining 72 cases were graded by the five readers. We also assessed the degree of pulmonary emphysema in those cases.

RESULTS

The grade of profusion on CR (CR score) of all five readers was identical in only 5 of 109 cases (4.6%). The CR score coincided with that on CT in 40 of 72 cases (56%). The CT score was higher than that on CR in 13 cases (18%). On the other hand, the CT score was lower than that on CR in 19 cases (26%). The incidence of pulmonary emphysema was significantly higher in patients whose CR score was higher than their CT score.

CONCLUSION

CT is more sensitive than CR in the evaluation of early-stage pneumoconiosis. In cases with emphysema, the CR score tends to be higher in comparison to that on CT.

Discordant Spirometry and Impulse Oscillometry Assessments in the Diagnosis of Small Airway Dysfunction

Background and objective: Spirometry is commonly used to assess small airway dysfunction (SAD). Impulse oscillometry (IOS) can complement spirometry. However, discordant spirometry and IOS in the diagnosis of SAD were not uncommon. We examined the association between spirometry and IOS within a large cohort of subjects to identify variables that may explain discordant spirometry and IOS findings. Methods: 1,836 subjects from the ECOPD cohort underwent questionnaires, symptom scores, spirometry, and IOS, and 1,318 subjects were examined by CT. We assessed SAD with R₅-R₂₀ > the upper limit of normal (ULN) by IOS and two of the three spirometry indexes (maximal mid-expiratory flow (MMEF), forced expiratory flow (FEF)_50%, and FEF_75%) < 65% predicted. Multivariate regression analysis was used to analyze factors associated with SAD diagnosed by only spirometry but not IOS (spirometry-only SAD) and only IOS but not spirometry (IOS-only SAD), and line regression was used to assess CT imaging differences. Results: There was a slight agreement between spirometry and IOS in the diagnosis of SAD (kappa 0.322, p < 0.001). Smoking status, phlegm, drug treatment, and family history of respiratory disease were factors leading to spirometry-only SAD. Spirometry-only SAD had more severe emphysema and gas-trapping than IOS-only SAD in abnormal lung function. However, in normal lung function subjects, there was no statistical difference in emphysema and gas-trapping between discordant groups. The number of IOS-only SAD was nearly twice than that of spirometry. Conclusion: IOS may be more sensitive than spirometry in the diagnosis of SAD in normal lung function subjects. But in patients with abnormal lung function, spirometry may be more sensitive than IOS to detect SAD patients with clinical symptoms and CT lesions.

[Alpha 1-antitrypsin deficiency]

INTRODUCTION

Pulmonary emphysema and liver disease are the clinical expressions of alpha 1-antitrypsin deficiency, an autosomal recessive genetic disease.

STATE OF THE ART

Alpha 1-antitrypsin deficiency is usually associated with the homozygous Z variant of the SERPINA1 gene. Its clinical expression always consists in a substantial reduction of alpha 1-antitrypsin serum concentration and its variants are analyzed by isoelectric focalization or molecular techniques. Assessed by CO transfer alteration and CT scan, risk of pulmonary emphysema is increased by tobacco consumption. Assessed by transient elastography and liver ultrasound, risk of liver disease is increased by alcohol consumption or obesity. Treatment of COPD-associated alpha 1-antitrypsin deficiency does not differ from that of other forms of COPD. In patients presenting with severe deficiency, augmentation therapy with plasma-derived alpha 1-antitrypsin reduces the progression of emphysema, as shown in terms of CT-based lung density metrics. Patients with alpha 1-antitrypsin deficiency with a ZZ genotype should refrain from alcohol or tobacco consumption, and watch their weight; so should their close relatives.

PERSPECTIVES

Modulation of alpha 1-antitrypsin liver production offers an interesting new therapeutic perspective.

CONCLUSION

Homozygous (Z) variants of the SERPINA1 gene confer an increased risk of pulmonary emphysema and liver disease, particularly among smokers, drinkers and obese persons.

Association between visual emphysema and lung nodules on low-dose CT scan in a Chinese Lung Cancer Screening Program (Nelcin-B3)

OBJECTIVES

This study aimed to evaluate the association between visual emphysema and the presence of lung nodules, and Lung-RADS category with low-dose CT (LDCT).

METHODS

Baseline LDCT scans of 1162 participants from a lung cancer screening study (Nelcin-B3) performed in a Chinese general population were included. The presence, subtypes, and severity of emphysema (at least trace) were visually assessed by one radiologist. The presence, size, and classification of non-calcified lung nodules (≥ 30 mm³) and Lung-RADS category were independently assessed by another two radiologists. Multivariable logistic regression and stratified analyses were performed to estimate the association between emphysema and lung nodules, Lung-RADS category, after adjusting for age, sex, BMI, smoking status, pack-years, and passive smoking.

RESULTS

Emphysema and lung nodules were observed in 674 (58.0%) and 424 (36.5%) participants, respectively. Participants with emphysema had a 71% increased risk of having lung nodules (adjusted odds ratios, aOR: 1.71, 95% CI: 1.26-2.31) and 70% increased risk of positive Lung-RADS category (aOR: 1.70, 95% CI: 1.09-2.66) than those without emphysema. Participants with paraseptal emphysema (n = 47, 4.0%) were at a higher risk for lung nodules than those with centrilobular emphysema (CLE) (aOR: 2.43, 95% CI: 1.32-4.50 and aOR: 1.60, 95% CI: 1.23-2.09, respectively). Only CLE was associated with positive Lung-RADS category (p = 0.02). CLE severity was related to a higher risk of lung nodules (ranges aOR: 1.44-2.61, overall p < 0.01).

CONCLUSION

In a Chinese general population, visual emphysema based on LDCT is independently related to the presence of lung nodules (≥ 30 mm³) and specifically CLE subtype is related to positive Lung-RADS category. The risk of lung nodules increases with CLE severity.

KEY POINTS

• Participants with emphysema had an increased risk of having lung nodules, especially smokers. • Participants with PSE were at a higher risk for lung nodules than those with CLE, but nodules in participants with CLE had a higher risk of positive Lung-RADS category. • The risk of lung nodules increases with CLE severity.

AI-Driven Model for Automatic Emphysema Detection in Low-Dose Computed Tomography Using Disease-Specific Augmentation

The objective of this study is to evaluate the feasibility of a disease-specific deep learning (DL) model based on minimum intensity projection (minIP) for automated emphysema detection in low-dose computed tomography (LDCT) scans. LDCT scans of 240 individuals from a population-based cohort in the Netherlands (ImaLife study, mean age ± SD = 57 ± 6 years) were retrospectively chosen for training and internal validation of the DL model. For independent testing, LDCT scans of 125 individuals from a lung cancer screening cohort in the USA (NLST study, mean age ± SD = 64 ± 5 years) were used. Dichotomous emphysema diagnosis based on radiologists' annotation was used to develop the model. The automated model included minIP processing (slab thickness range: 1 mm to 11 mm), classification, and detection maps generation. The data-split for the pipeline evaluation involved class-balanced and imbalanced settings. The proposed DL pipeline showed the highest performance (area under receiver operating characteristics curve) for 11 mm slab thickness in both the balanced (ImaLife = 0.90 ± 0.05) and the imbalanced dataset (NLST = 0.77 ± 0.06). For ImaLife subcohort, the variation in minIP slab thickness from 1 to 11 mm increased the DL model's sensitivity from 75 to 88% and decreased the number of false-negative predictions from 10 to 5. The minIP-based DL model can automatically detect emphysema in LDCTs. The performance of thicker minIP slabs was better than that of thinner slabs. LDCT can be leveraged for emphysema detection by applying disease specific augmentation.

Prognostic factors of worse outcome for hospitalized COVID-19 patients, with emphasis on chest computed tomography data: a retrospective study

OBJECTIVE

To evaluate anthropometric and clinical data, muscle mass, subcutaneous fat, spine bone mineral density, extent of acute pulmonary disease related to COVID-19, quantification of pulmonary emphysema, coronary calcium, and hepatic steatosis using chest computed tomography of hospitalized patients with confirmed diagnosis of COVID-19 pneumonia and verify its association with disease severity.

METHODS

A total of 123 adults hospitalized due to COVID-19 pneumonia were enrolled in the present study, which evaluated the anthropometric, clinical and chest computed tomography data (pectoral and paravertebral muscle area and density, subcutaneous fat, thoracic vertebral bodies density, degree of pulmonary involvement by disease, coronary calcium quantification, liver attenuation measurement) and their association with poorer prognosis characterized through a combined outcome of intubation and mechanical ventilation, need of intensive care unit, and death.

RESULTS

Age (p=0.013), body mass index (p=0.009), lymphopenia (p=0.034), and degree of pulmonary involvement of COVID-19 pneumonia (p<0.001) were associated with poor prognosis. Extent of pulmonary involvement by COVID-19 pneumonia had an odds ratio of 1,329 for a poor prognosis and a cutoff value of 6.5 for increased risk, with a sensitivity of 64.9% and specificity of 67.1%.

CONCLUSION

The present study found an association of high body mass index, older age, extent of pulmonary involvement by COVID-19, and lymphopenia with severity of COVID-19 pneumonia in hospitalized patients.

The prevalence and physiological impacts of centrilobular and paraseptal emphysema on CT in smokers with Preserved Ratio Impaired Spirometry

Centrilobular emphysema (CLE) and paraseptal emphysema (PSE) are observed in smokers with Preserved Ratio Impaired Spirometry (PRISm, defined as the ratio of forced expiratory volume in 1 s (FEV1) to forced vital capacity (FVC)≥0.7 and FEV1<80%), but their prevalence and physiological impacts remain unestablished. This multicenter study aimed to investigate its prevalence and to test whether emphysema subtypes are differently associated with physiological impairments in smokers with PRISm.

Both never and ever smokers aged at ≥40 years who underwent CT for lung cancer screening and spirometry were retrospectively and consecutively enrolled at three hospitals and a clinic. Emphysema subtypes were visually classified according to the Fleischner system. Air-trapping was assessed as the ratio of FVC to total lung capacity on CT (FVC/TLCCT).

Of 1046 never-smokers and 772 smokers with >10 pack-years, the prevalence of PRISm was 8.2% and 11.3%, respectively. The prevalence of PSE and CLE in smokers with PRISm was comparable to that in smokers with normal spirometry (PSE 43.7% versus 36.2%, p=1.00, CLE 46.0% versus 31.8%, p=0.21), but higher than that in never-smokers with PRISm (PSE, versus 1.2%, p<0.01, CLE, versus 4.7%, p<0.01) and lower than that in smokers with airflow limitation (PSE, versus 71.0%, p<0.01, CLE, versus 79.3%, p<0.01). The presence of CLE but not PSE was independently associated with reduced FVC/TLCCT in smokers with PRISm.

Both PSE and CLE were common, but only CLE was associated with air-trapping in smokers with PRISm, suggesting different physiological roles of these emphysema subtypes.

Association between Chest CT-defined Emphysema and Lung Cancer: A Systematic Review and Meta-Analysis

Background Given the different methods of assessing emphysema, controversy exists as to whether it is associated with lung cancer. Purpose To perform a systematic review and meta-analysis of the association between chest CT-defined emphysema and the presence of lung cancer. Materials and Methods The PubMed, Embase, and Cochrane databases were searched up to July 15, 2021, to identify studies on the association between emphysema assessed visually or quantitatively with CT and lung cancer. Associations were determined by emphysema severity (trace, mild, or moderate to severe, assessed visually and quantitatively) and subtype (centrilobular and paraseptal, assessed visually). Overall and stratified pooled odds ratios (ORs) with their 95% CIs were obtained. Results Of the 3343 screened studies, 21 studies (107 082 patients) with 26 subsets were included. The overall pooled ORs for lung cancer given the presence of emphysema were 2.3 (95% CI: 2.0, 2.6; I² = 35%; 19 subsets) and 1.02 (95% CI: 1.01, 1.02; six subsets) per 1% increase in low attenuation area. Studies with visual (pooled OR, 2.3; 95% CI: 1.9, 2.6; I² = 48%; 12 subsets) and quantitative (pooled OR, 2.2; 95% CI: 1.8, 2.8; I² = 3.7%; eight subsets) assessments yielded comparable results for the dichotomous assessment. Based on six studies (1716 patients), the pooled ORs for lung cancer increased with emphysema severity and were higher for visual assessment (2.5, 3.7, and 4.5 for trace, mild, and moderate to severe, respectively) than for quantitative assessment (1.9, 2.2, and 2.5) based on point estimates. Compared with no emphysema, only centrilobular emphysema (three studies) was associated with lung cancer (pooled OR, 2.2; 95% CI: 1.5, 3.2; P < .001). Conclusion Both visual and quantitative CT assessments of emphysema were associated with a higher odds of lung cancer, which also increased with emphysema severity. Regarding subtype, only centrilobular emphysema was significantly associated with lung cancer. Clinical trial registration no. CRD42021262163 © RSNA, 2022 See also the editorial by Hunsaker in this issue. Online supplemental material is available for this article.

Prognostic value of semi-quantitative CT-based score integrated with cardiovascular risk factors during the first peak of the COVID-19 pandemic: A new score to predict poor outcome

Purpose

Predicting the clinical course of COVID-19 pneumonia is of high clinical importance and may change treatment strategies. This study aimed to compare the semi-quantitative CT score (radiological score), mCHA₂DS₂-VASc score (clinical score), and R-mCHA₂DS₂-VASc score (clinical and radiological score) to predict the risk of ICU admission and mortality in COVID 19 pneumonia.

Methods

This study retrospectively evaluated 901 COVID-19 pneumonia cases with positive PCR results. The mCHA₂DS₂-VASc score was calculated based on clinical risk factors. CT images were evaluated, and the semi-quantitative CT scores were obtained. A new scoring method (R-mCHA₂DS₂-VASc score) was developed by combining these scores. The performance of the mCHA₂DS₂-VASc score, semi-quantitative CT score, and a combination of these scores (R-mCHA₂DS₂-VASc score) was evaluated using ROC analysis.

Results

The ROC curves of the semi-quantitative CT, mCHA₂DS₂-VASc, and R-mCHA₂DS₂-VASc scores were examined. The semi-quantitative CT, mCHA₂DS₂-VASc, and R-mCHA₂DS₂-VASc scores were significant in predicting intensive care unit (ICU) admission and mortality (p < 0.001). The R-mCHA₂DS₂-VASc score performed best in predicting a severe clinical course, and the cut-off value of 8 for the R-mCHA₂DS₂-VASc score had 83.9% sensitivity and 91.6% specificity for mortality.

Conclusions

The R-mCHA₂DS₂-VASc score includes both clinical and radiological parameters. It is a feasible scoring method for predicting a severe clinical course at an early stage with high sensitivity and specificity values. However, prospective studies with larger sample sizes are warranted.

Protective effect of total Saponins from American ginseng against cigarette smoke-induced COPD in mice based on integrated metabolomics and network pharmacology

Chronic obstructive pulmonary disease (COPD) is a prevalent respiratory disease. Aiming at assessing the effect of total saponins from American ginseng on COPD, both the chemical composition and anti-COPD activity of total saponins from wild-simulated American ginseng (TSW) and field-grown American ginseng (TSF) were investigated in this study. Firstly, a HPLC-ELSD chromatographic method was established to simultaneously determine the contents of 22 saponins in TSW and TSF. Secondly, CS-induced COPD mouse model was established to evaluate the activity of TSW and TSF. The results indicated that both TSW and TSF had the protective effect against COPD by alleviating oxidative stress and inflammatory response. TSW showed a stronger effect than TSF. Thirdly, an integrated approach involving metabolomics and network pharmacology was used to construct the "biomarker-reaction-enzyme-target" correlation network aiming at further exploring the observed effects. As the results, 15 biomarkers, 9 targets and 5 pathways were identified to play vital roles in the treatment of TSW and TSF on COPD. Fourthly, based on network pharmacology and the CS-stimulated A549 cell model, ginsenoside Rgl, Rc, oleanolic acid, notoginsenoside R1, Fe, silphioside B were certified to be the material basis for the stronger effect of TSW than TSF. Finally, the molecular docking were performed to visualize the binding modes. Our findings suggested that both TSW and TSF could effectively ameliorate the progression of COPD and might be used for the treatment of COPD.

A Novel CT-Based Radiomics Features Analysis for Identification and Severity Staging of COPD

RATIONALE AND OBJECTIVES

To evaluate the role of radiomics based on Chest Computed Tomography (CT) in the identification and severity staging of chronic obstructive pulmonary disease (COPD).

MATERIALS AND METHODS

This retrospective analysis included 322 participants (249 COPD patients and 73 control subjects). In total, 1395 chest CT-based radiomics features were extracted from each participant's CT images. Three feature selection methods, including variance threshold, Select K Best method, and least absolute shrinkage and selection operator (LASSO), and two classification methods, including support vector machine (SVM) and logistic regression (LR), were used as identification and severity classification of COPD. Performance was compared by AUC, accuracy, sensitivity, specificity, precision, and F1-score.

RESULTS

38 and 10 features were selected to construct radiomics models to detect and stage COPD, respectively. For COPD identification, SVM classifier achieved AUCs of 0.992 and 0.970, while LR classifier achieved AUCs of 0.993 and 0.972 in the training set and test set, respectively. For the severity staging of COPD, the mentioned two machine learning classifiers can better differentiate less severity (GOLD1 + GOLD2) group from greater severity (GOLD3 + GOLD4) group. The AUCs of SVM and LR is 0.907 and 0.903 in the training set, and that of 0.799 and 0.797 in the test set.

CONCLUSION

The present study showed that the novel radiomics approach based on chest CT images that can be used for COPD identification and severity classification, and the constructed radiomics model demonstrated acceptable performance.

Low-dose CT for lung cancer screening: position paper from the Italian college of thoracic radiology

Smoking is the main risk factor for lung cancer (LC), which is the leading cause of cancer-related death worldwide. Independent randomized controlled trials, governmental and inter-governmental task forces, and meta-analyses established that LC screening (LCS) with chest low dose computed tomography (LDCT) decreases the mortality of LC in smokers and former smokers, compared to no-screening, especially in women. Accordingly, several Italian initiatives are offering LCS by LDCT and smoking cessation to about 10,000 high-risk subjects, supported by Private or Public Health Institutions, envisaging a possible population-based screening program. Because LDCT is the backbone of LCS, Italian radiologists with LCS expertise are presenting this position paper that encompasses recommendations for LDCT scan protocol and its reading. Moreover, fundamentals for classification of lung nodules and other findings at LDCT test are detailed along with international guidelines, from the European Society of Thoracic Imaging, the British Thoracic Society, and the American College of Radiology, for their reporting and management in LCS. The Italian College of Thoracic Radiologists produced this document to provide the basics for radiologists who plan to set up or to be involved in LCS, thus fostering homogenous evidence-based approach to the LDCT test over the Italian territory and warrant comparison and analyses throughout National and International practices.

CT-Based Commercial Software Applications: Improving Patient Care Through Accurate COPD Subtyping

Chronic obstructive pulmonary disease (COPD) is heterogenous in its clinical manifestations and disease progression. Patients often have disease courses that are difficult to predict with readily available data, such as lung function testing. The ability to better classify COPD into well-defined groups will allow researchers and clinicians to tailor novel therapies, monitor their effects, and improve patient-centered outcomes. Different modalities of assessing these COPD phenotypes are actively being studied, and an area of great promise includes the use of quantitative computed tomography (QCT) techniques focused on key features such as airway anatomy, lung density, and vascular morphology. Over the last few decades, companies around the world have commercialized automated CT software packages that have proven immensely useful in these endeavors. This article reviews the key features of several commercial platforms, including the technologies they are based on, the metrics they can generate, and their clinical correlations and applications. While such tools are increasingly being used in research and clinical settings, they have yet to be consistently adopted for diagnostic work-up and treatment planning, and their full potential remains to be explored.

Pulmonary vascular volume is associated with DLCO and fibrotic score in idiopathic pulmonary fibrosis: an observational study

BACKGROUND

Idiopathic pulmonary fibrosis (IPF) is a disease that primarily occurs in elderly individuals. However, it is difficult to diagnose and has a complex disease course. High-resolution computed tomography (HRCT) and lung function testing are crucial for its diagnosis and follow-up. However, the correlation of HRCT findings with lung function test results has not been extensively investigated.

METHODS

This study retrospectively analysed the medical records and images of patients with IPF. Patients with evident emphysema and lung cancer were excluded. The diagnosis of all the included cases was confirmed following a discussion among specialists from multiple disciplines. The correlation of HRCT findings, including fibrotic score, HRCT lung volume, pulmonary artery trunk (PA) diameter and pulmonary vascular volume (PVV), with lung function test parameters, such as forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO), was analysed.

RESULTS

A total of 32 patients were included. Higher fibrotic and PVV scores were significantly correlated with lower DLCO (r =  - 0.59, p = 0.01; r =  - 0.43, p = 0.03, respectively) but not with FVC. Higher PVV score significantly correlated with higher fibrotic score (r = 0.59, p < 0.01) and PA diameter (r = 0.47, p = 0.006).

CONCLUSION

Our study demonstrated the structural and functional correlation of IPF. The extent of lung fibrosis (fibrotic score) and PVV score were associated with DLCO but not with FVC. The PA diameter, which reflects the pulmonary artery pressure, was found to be associated with the PVV score.

Associations of pulmonary and extrapulmonary computed tomographic manifestations with impaired physical activity in symptomatic patients with chronic obstructive pulmonary disease

In patients with chronic obstructive pulmonary disease (COPD), emphysema, airway disease, and extrapulmonary comorbidities may cause various symptoms and impair physical activity. To investigate the relative associations of pulmonary and extrapulmonary manifestations with physical activity in symptomatic patients, this study enrolled 193 patients with COPD who underwent chest inspiratory/expiratory CT and completed COPD assessment test (CAT) and the Life-Space Assessment (LSA) questionnaires to evaluate symptom and physical activity. In symptomatic patients (CAT ≥ 10, n = 100), emphysema on inspiratory CT and air-trapping on expiratory CT were more severe and height-adjusted cross-sectional areas of pectoralis muscles (PM index) and adjacent subcutaneous adipose tissue (SAT index) on inspiratory CT were smaller in those with impaired physical activity (LSA < 60) than those without. In contrast, these findings were not observed in less symptomatic patients (CAT < 10). In multivariable analyses of the symptomatic patients, severe air-trapping and lower PM index and SAT index, but not CT-measured thoracic vertebrae bone density and coronary artery calcification, were associated with impaired physical activity. These suggest that increased air-trapping and decreased skeletal muscle and subcutaneous adipose tissue quantity are independently associated with impaired physical activity in symptomatic patients with COPD.

One-year pulmonary impairment after severe COVID-19: a prospective, multicenter follow-up study

BACKGROUND

Long-term pulmonary sequelae following hospitalization for SARS-CoV-2 pneumonia is largely unclear. The aim of this study was to identify and characterise pulmonary sequelae caused by SARS-CoV-2 pneumonia at 12-month from discharge.

METHODS

In this multicentre, prospective, observational study, patients hospitalised for SARS-CoV-2 pneumonia and without prior diagnosis of structural lung diseases were stratified by maximum ventilatory support ("oxygen only", "continuous positive airway pressure (CPAP)" and "invasive mechanical ventilation (IMV)") and followed up at 12 months from discharge. Pulmonary function tests and diffusion capacity for carbon monoxide (DLCO), 6 min walking test, high resolution CT (HRCT) scan, and modified Medical Research Council (mMRC) dyspnea scale were collected.

RESULTS

Out of 287 patients hospitalized with SARS-CoV-2 pneumonia and followed up at 1 year, DLCO impairment, mainly of mild entity and improved with respect to the 6-month follow-up, was observed more frequently in the "oxygen only" and "IMV" group (53% and 49% of patients, respectively), compared to 29% in the "CPAP" group. Abnormalities at chest HRCT were found in 46%, 65% and 80% of cases in the "oxygen only", "CPAP" and "IMV" group, respectively. Non-fibrotic interstitial lung abnormalities, in particular reticulations and ground-glass attenuation, were the main finding, while honeycombing was found only in 1% of cases. Older patients and those requiring IMV were at higher risk of developing radiological pulmonary sequelae. Dyspnea evaluated through mMRC scale was reported by 35% of patients with no differences between groups, compared to 29% at 6-month follow-up.

CONCLUSION

DLCO alteration and non-fibrotic interstitial lung abnormalities are common after 1 year from hospitalization due to SARS-CoV-2 pneumonia, particularly in older patients requiring higher ventilatory support. Studies with longer follow-ups are needed.

Diagnostic efficacy of visual subtypes and low attenuation area based on HRCT in the diagnosis of COPD

Background

Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease. Current gold standard criteria, pulmonary function tests (PFTs) may result in underdiagnosis of potential COPD patients. Therefore, we hypothesize that the combination of high-resolution computed tomography (HRCT) and clinical basic characteristics will enable the identification of more COPD patients.

Methods

A total of 284 patients with respiratory symptoms who were current or former smokers were included in the study, and were further divided into 5 groups of GOLD grade I–IV and non-COPD according to PFTs. All patients underwent inspiratory HRCT scanning and low attenuation area (LAA) was measured. Then they were divided into seven visual subtypes according to the Fleischner Society classification system. Non-parametric tests were used for exploring differences in basic characteristics and PFTs between different groups of enrolled patients and visual subtypes. Binary logistic regression was to find the influencing factors that affected the patients’ outcome (non-COPD vs GOLD I-IV). The area under the receiver operating characteristic curve (AUC-ROC) was to explore the diagnostic efficacy of LAA, visual subtypes, and combined basic characteristics related to COPD for COPD diagnosis. Finally, based on the cut-off values of ROC analysis, exploring HRCT features in patients who do not meet the diagnostic criteria but clinically suspected COPD.

Results

With the worsening severity of COPD, the visual subtypes gradually progressed (p < 0.01). There was a significant difference in LAA between GOLD II–IV and non-COPD (p < 0.0001). The diagnostic efficacy of LAA, visual subtypes, and LAA combined with visual subtypes for COPD were 0.742, 0.682 and 0.730 respectively. The diagnostic efficacy increased to 0.923–0.943 when basic characteristics were added (all p < 0.001). Based on the cut-off value of ROC analysis, LAA greater than 5.6, worsening of visual subtypes, combined with positive basic characteristics can help identify some potential COPD patients.

Conclusion

The heterogeneous phenotype of COPD requires a combination of multiple evaluation methods. The diagnostic efficacy of combining LAA, visual subtypes, and basic characteristics achieves good consistency with current diagnostic criteria.