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Objective: We sought to explore the relationships between multiple chemokines with spirometry, inflammatory mediators and CT findings of emphysema, small airways disease and bronchial wall thickness. Methods: All patients with COPD (n = 65) and healthy control subjects (n = 23) underwent high-resolution CT, with image analysis determining the low attenuation area (LAA), ratio of mean lung attenuation on expiratory and inspiratory scans (E/I MLD) and bronchial wall thickness of inner perimeter of a 10-mm diameter airway (Pi10). At enrollment, subjects underwent pulmonary function studies, chemokines and inflammatory mediators measurements. Results: Multiple chemokines (CCL2, CCL3, CCL5, CX3CL1, CXCL8, CXCL9, CXCL10, CXCL11 and CXCL12) and inflammatory mediators (MMP-9, MMP-12, IL-18 and neutrophil count) were markedly increased in the serum of COPD patients compared with healthy controls. There were associations between small airway disease (E/I MLD) and CCL11, CXCL8, CXCL10, CXCL11, CXCL12 and CX3CL1. Especially CXCL8 and CX3CL1 are strongly associated with E/I MLD (r = 0.74, p < 0.001; r = 0.76, p < 0.001, respectively). CXCL8, CXCL12 and CX3CL1 were moderately positively correlated with emphysema (%LAA) (r = 0.49, p < 0.05; r = 0.51, p < 0.05; r = 0.54, p < 0.01, respectively). Bronchial wall thickness (Pi10）showed no significant differences between the COPD and healthy controls,，but there was an association between Pi10 and FEV1% in COPD patients (r=−0.420, p = 0.048). Our statistical results showed that there were not any associations between airway wall thickness (Pi10) and chemokines. Conclusion: Pulmonary chemokines levels are closely associated with the extent of gas trapping, small airways disease and emphysema identified on high-resolution chest CT scan. Advances in knowledge: This study combines quantitative CT analysis with multiplex chemokines and inflammatory mediators to identify a new role of pathological changes in COPD.

Objective: To investigate the associations between intrapulmonary vascular volume (IPVV) depicted on inspiratory and expiratory CT scans and disease severity in COPD patients, and to determine which CT parameters can be used to predict IPVV.Methods: We retrospectively collected 89 CT examinations acquired on COPD patients from an available database. All subjects underwent both inspiratory and expiratory CT scans. We quantified the IPVV, airway wall thickness (WT), the percentage of the airway wall area (WA%), and the extent of emphysema (LAA%−950) using an available pulmonary image analysis tool. The underlying relationship between IPVV and COPD severity, which was defined as mild COPD (GOLD stage I and II) and severe COPD (GOLD stage III and IV), was analyzed using the Student's t-test (or Mann-Whitney U-test). The correlations of IPVV with pulmonary function tests (PFTs), LAA%−950, and airway parameters for the third to sixth generation bronchus were analyzed using the Pearson or Spearman's rank correlation coefficients and multiple stepwise regression.Results: In the subgroup with only inspiratory examinations, the correlation coefficients between IPVV and PFT measures were −0.215 ~ −0.292 (p &lt; 0.05), the correlation coefficients between IPVV and WT3−6 were 0.233 ~ 0.557 (p &lt; 0.05), and the correlation coefficient between IPVV and LAA%−950 were 0.238 ~ 0.409 (p &lt; 0.05). In the subgroup with only expiratory scan, the correlation coefficients between IPVV and PFT measures were −0.238 ~ −0.360 (p &lt; 0.05), the correlation coefficients between IPVV and WT3−6 were 0.260 ~ 0.566 (p &lt; 0.05), and the correlation coefficient between IPVV and LAA%−950 were 0.241 ~ 0.362 (p &lt; 0.05). The multiple stepwise regression analyses demonstrated that WT were independently associated with IPVV (P &lt; 0.05).Conclusion: The expiratory CT scans can provide a more accurate assessment of COPD than the inspiratory CT scans, and the airway wall thickness maybe an independent predictor of pulmonary vascular alteration in patients with COPD.

<sec> <title>Purpose:</title> This study aimed to identify severe Coronavirus Disease 2019 (COVID-19) cases combining blood test results and imaging parameters based on a machine learning classifier at the initial admission. </sec> <sec> <title>Materials and methods:</title> Ninety-five non-severe and 22 severe laboratory-confirmed COVID-19 cases treated between January 23, 2020 and March 25, 2020 were examined in this retrospective trial. Blood test results and chest computed tomography (CT) images were obtained at the initial admission. The lesions on CT images were segmented using an artificial intelligent (AI) tool. Then, quantitative CT (QCT) parameters, including the volume, percentage, ground glass opacity (GGO) percentage and heterogeneity of the lesions were calculated. Correlations of blood test results and QCT parameters were analyzed by the Pearson test first. Then, discriminative features for detecting severe cases were selected by both the independent samples t test and least absolute shrinkage and selection operator (LASSO) regression. Next, support vector machine (SVM), Gaussian naïve Bayes (GNB), Knearest neighbor (KNN), decision tree (DT), random forest (RF) and multi-layer perceptron-neural net (MLP-NN) algorithms were used as classifiers, and their accuracies were assessed by 10-fold-cross-validation. </sec> <sec> <title>Results:</title> Blood test indexes and CT parameters were moderately to medially correlated. Of all selected features, lesion percentage contributed mostly to the classification of the two groups, followed by lesion volume, patient age, lymphocyte count, neutrophil count, GGO percentage and tumor heterogeneity. RF-assisted identification had the highest accuracy of 91.38%, followed by GNB (87.83%), KNN (87.93%), SVM (86.21%), MLP-NN (85.34%) and DT (84.48%). </sec> <sec> <title>Conclusions:</title> The RF-assisted model combining blood test and QCT parameters is helpful in the identification of severe COVID-19 cases. </sec>

Background Previous studies have demonstrated that there is a certain correlation between emphysema and changes in pulmonary small blood vessels in patients with chronic obstructive pulmonary disease (COPD), but most of them were limited to the investigation of the inspiratory phase. The emphysema indicators need to be further optimized. Based on the parametric response mapping (PRM) method, this study aimed to investigate the effect of emphysema and functional small airway disease on intrapulmonary vascular volume (IPVV). Methods This retrospective study enrolled 63 healthy subjects and 47 COPD patients, who underwent both inspiratory and expiratory CT scans of the chest and pulmonary function tests (PFTs). Inspiratory and expiratory IPVV were measured by using an automatic pulmonary vessels integration segmentation approach, the ratio of emphysema volume (Emph%), functional small airway disease volume (fsAD%), and normal areas volume (Normal%) were quantified by the PRM method for biphasic CT scans. The participants were grouped according to PFTs. Analysis of variance (ANOVA) and Kruskal–Wallis H-test were used to analyze the differences in indicators between different groups. Then, Spearman’s rank correlation coefficients were used to analyze the correlation between Emph%, fsAD%, Normal%, PFTs, and IPVV. Finally, multiple linear regression was applied to analyze the effects of Emph% and fsAD% on IPVV. Results Differences were found in age, body mass index (BMI), smoking index, FEV1%, FEV1/forced vital capacity (FVC), expiratory IPVV, IPVV relative value, IPVV difference value, Emph%, fsAD%, and Normal% between the groups ( P <0.05). A strong correlation was established between the outcomes of PFTs and quantitative CT indexes. Finally, the effect of Emph% was more significant than that of fsAD% on expiratory IPVV, IPVV difference value, and IPVV relative value. Conclusion IPVV may have a potential value in assessing COPD severity and is significantly affected by emphysema.