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Objective:To assess the effectiveness of a model created using clinical features and preoperative chest CT imaging features in predicting the chronic obstructive pulmonary disease (COPD) among patients diagnosed with lung cancer.Methods:A retrospective analysis was conducted on clinical (age, gender, smoking history, smoking index, etc.) and imaging (lesion size, location, density, lobulation sign, etc.) data from 444 lung cancer patients confirmed by pathology at the Second Affiliated Hospital of Naval Medical University between June 2014 and March 2021. These patients were randomly divided into a training set (310 patients) and an internal test set (134 patients) using a 7∶3 ratio through the random function in Python. Based on the results of pulmonary function tests, the patients were further categorized into two groups: lung cancer combined with COPD and lung cancer non-COPD. Initially, univariate analysis was performed to identify statistically significant differences in clinical characteristics between the two groups. The variables showing significance were then included in the logistic regression analysis to determine the independent factors predicting lung cancer combined with COPD, thereby constructing the clinical model. The image features underwent a filtering process using the minimum absolute value convergence and selection operator. The reliability of these features was assessed through leave-P groups-out cross-validation repeated five times. Subsequently, a radiological model was developed. Finally, a combined model was established by combining the radiological signature with the clinical features. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) curves were plotted to evaluate the predictive capability and clinical applicability of the model. The area under the curve (AUC) for each model in predicting lung cancer combined with COPD was compared using the DeLong test.Results:In the training set, there were 182 cases in the lung cancer combined with COPD group and 128 cases in the lung cancer non-COPD group. The combined model demonstrated an AUC of 0.89 for predicting lung cancer combined with COPD, while the clinical model achieved an AUC of 0.82 and the radiological model had an AUC of 0.85. In the test set, there were 78 cases in the lung cancer combined with COPD group and 56 cases in the lung cancer non-COPD group. The combined model yielded an AUC of 0.85 for predicting lung cancer combined with COPD, compared to 0.77 for the clinical model and 0.83 for the radiological model. The difference in AUC between the radiological model and the clinical model was not statistically significant ( Z=1.40, P=0.163). However, there were statistically significant differences in the AUC values between the combined model and the clinical model ( Z=-4.01, P=0.010), as well as between the combined model and the radiological model ( Z=-2.57, P<0.001). DCA showed the maximum net benifit of the combined model. Conclusion:The developed synthetic diagnostic combined model, incorporating both radiological signature and clinical features, demonstrates the ability to predict COPD in patients with lung cancer.
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Objective:To explore the differences of functional small airway and pulmonary vascular parameters in chronic obstructive pulmonary disease (COPD) of different imaging phenotypes.Methods:One hundred and thirty COPD patients underwent biphasic CT scanning in Shanghai Changzheng Hospital from August 2018 to August 2020 were analyzed retrospectively. The patients were classified into three phenotypes based on the presence of emphysema and bronchial wall thickening on CT images. Phenotype A: no emphysema or mild emphysema, with or without bronchial wall thickening; Phenotype E: obvious emphysema without bronchial wall thickening; phenotype M: significant emphysema and bronchial wall thickening were present. Parametric response map (PRM) and pulmonary vascular parameters were quantitatively measured at the whole lung level. PRM parameters included the volume of emphysema (PRMV Emphysema), the volume of functional small airway (PRMV fSAD), the volume of normal pulmonary parenchyma (PRMV Normal) and its volume percentage (%). Pulmonary vascular parameters included the number of vessels (N) and cross-sectional area vessels<5 mm 2 (N -CSA<5) at 6, 9, 12, 15, 18 21, 24 mm distance from the pleura. ANOVA or Kruskal-Wallis H tests were used to compare the differences for PRM and pulmonary vascular parameters among the three phenotypes, and LSD or Bonferroni tests were used for multiple comparisons. Results:There were significant differences among the three phenotypes for PRMV fSAD, PRMV Emphysema, PRMV fSAD%, PRMV Emphysema%, and PRMV Normal% at the whole lung level ( P<0.05). PRMV Emphysema, PRMV Emphysema%, PRMV Fsad, PRMV fSAD% of phenotype A were lower than those of phenotype E and M ( P<0.001), while there was no significant difference for PRMV Emphysema, PRMV Emphysema%, PRMV fSAD, PRMV fSAD% between phenotype E and phenotype M ( P>0.05). There were significant differences in N and N -CSA<5 that 6 mm distance from the pleura among the three groups( P<0.05). Among them, N and N -CSA<5 that 6 mm distance from pleura in phenotype M were significantly lower than those in phenotype A( P<0.001,0.002); No significant differences was found in N between phenotype M and phenotype E( P>0.05), while there was significant differences in N -CSA<5 between phenotype M and phenotype E( P=0.034). Conclusion:Biphasic quantitative CT analysis can reflect the heterogeneity of the functional small airways and pulmonary vascular abnormality in COPD with different phenotypes, and provide objective evidence for individualized diagnosis and treatment.
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Objective:To explore the predictive value of random forest regression model for pulmonary function test.Methods:From August 2018 to December 2019, 615 subjects who underwent screening for three major chest diseases in Shanghai Changzheng Hospital were analyzed retrospectively. According to the ratio of forced expiratory volume in the first second to forced vital capacity (FEV 1/FVC) and the percentage of forced expiratory volume in the first second to the predicted value (FEV 1%), the subjects were divided into normal group, high risk group and chronic obstructive pulmonary disease (COPD) group. The CT quantitative parameter of small airway was parameter response mapping (PRM) parameters, including lung volume, the volume of functional small airways disease (PRMV fSAD), the volume of emphysema (PRMV Emph), the volume of normal lung tissue (PRMV Normal), the volume of uncategorized lung tissue (PRMV Uncategorized) and the percentage of the latter four volumes to the whole lung (%). ANOVA or Kruskal Wallis H was used to test the differences of basic clinical characteristics (age, sex, height, body mass), pulmonary function parameters and small airway CT quantitative parameters among the three groups; Spearman test was used to evaluate the correlation between PRM parameters and pulmonary function parameters. Finally, a random forest regression model based on PRM combined with four basic clinical characteristics was constructed to predict lung function. Results:There were significant differences in the parameters of whole lung PRM among the three groups ( P<0.001). Quantitative CT parameters PRMV Emph, PRMV Emph%, and PRMV Normal% showed a moderate correlation with FEV 1/FVC ( P<0.001). Whole lung volume, PRMV Normal,PRMV Uncategorized and PRMV Uncategorized% were strongly or moderately positively correlated with FVC ( P<0.001), other PRM parameters were weakly or very weakly correlated with pulmonary function parameters. Based on the above parameters, a random forest model for predicting FEV 1/FVC and a random forest model for predicting FEV 1% were established. The random forest model for predicting FEV 1/FVC predicted FEV 1/FVC and actual value was R 2=0.864 in the training set and R 2=0.749 in the validation set. The random forest model for predicting FEV 1% predicted FEV 1% and the actual value in the training set was R 2=0.888, and the validation set was R 2=0.792. The sensitivity, specificity and accuracy of predicting FEV 1% random forest model for the classification of normal group from high-risk group were 0.85(34/40), 0.90(65/72) and 0.88(99/112), respectively; and the sensitivity, specificity and accuracy of predicting FEV 1/FVC random forest model for differentiating non COPD group from COPD group were 0.89(8/9), 1.00 (112/112) and 0.99(120/121), respectively. While the accuracy of two models combination for subclassification of COPD [global initiative for chronic obstructive lung disease (GOLD) Ⅰ, GOLDⅡ and GOLD Ⅲ+Ⅳ] was only 0.44. Conclusions:Small airway CT quantitative parameter PRM can distinguish the normal population, high-risk and COPD population. The comprehensive regression prediction model combined with clinical characteristics based on PRM parameter show good performance differentiating normal group from high risk group, and differentiating non-COPD group from COPD group. Therefore, one-stop CT scan can evaluate the functional small airway and PFT simultaneously.
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BACKGROUND: Oxidative stress and immune imbalance play an important role in the pathogenesis of rheumatoid arthritis (RA). Bilirubin is a powerful antioxidant and also regarded as immunomodulator. Increased evidence shows that bilirubin should be a protective factor for autoimmune disease. However, the relationship between bilirubin and RA remain unclear. METHODS: We analyzed serum bilirubin levels and other laboratory and clinical data in 130 RA patients (35 patients without any complications), 81 osteoarthritis (OA) patients and 96 healthy controls. RESULTS: Binary logistic regression adjusted by age and gender revealed that the levels of serum total, indirect bilirubin were significantly lower in RA patients, when compared with healthy controls (P=.015, OR=0.767, 95% CI=0.619-0.951; P=.010, OR=0.664, 95% CI=0.487-0.906, respectively) or OA patients (P=.000, OR=0.763, 95% CI=0.661-0.882; P=.000, OR=0.656, 95% CI=0.532-0.808, respectively). A reduced trend of levels of bilirubin has been detected along with increased disease activity, despite with no significance (P>.05). Spearman rank test further demonstrated that IgG and ESR were negative associated with total, indirect bilirubin, and albumin, prealbumin, APOA, HDL-C were positively associated with bilirubin. CONCLUSIONS: In conclusion, the levels of serum bilirubins were decreased in RA, and decreased levels could be associated with IgG, albumin and inflammatory marker ESR.
