Objective:To explore the value of difference value and relative value of pulmonary vascular quantitative parameters of biphasic CT in diagnosis of chronic obstructive pulmonary disease (COPD) and high-risk groups.Methods:The study was a cross-sectional study. A retrospective study of 624 patients who underwent biphasic chest CT scanning in the Second Affiliated Hospital of PLA Naval Medical University from August 2018 to December 2020. Subjects were divided into three groups according to pulmonary function test: normal group ( n=321), preserved ratio impaired spirometry (PRISm) ( n=204) and COPD group ( n=99). The pulmonary vascular quantitative parameters were obtained by the workstation, including the number of pulmonary vessels (N total), cross-sectional area less than that of 5 mm 2 (N CSA5), vessel area (VA total) of 9, 12, 15, 18, 21 mm from the pleura, total blood vessels volume (TBV) and total blood vessels volume under 5 mm 2 (BV5) at expiratory and inspiratory phase. The difference value, relative value of biphasic CT were calculated. ANOVA test or Kruskal-Wallis H test was used to compare the differences of pulmonary vascular quantitative parameters among the three groups, and LSD or Bonferroni tests were used for multiple comparisons. Results:The N total, N CSA5, VA total of the whole lung in the inspiratory phase at the level of 9, 12, 15, 18 and 21 mm from the pleura and TBV, BV5 in the normal group, PRISm group and COPD group, showed an overall trend of decreasing and then increasing, and the PRISm group was lower than those of the normal group ( P<0.05). The differences in N total, N CSA5, VA total between the COPD group and the normal group at the level of 9 and 21 mm from the pleura were not statistically significant ( P>0.05), while the N total, N CSA5, V Atotal of biphasic difference value and relative value in COPD group demonstrated a significant difference from those in normal group ( P<0.05). Conclusion:The difference value and relative value of pulmonary vascular quantitative parameters in biphasic CT are more useful than single inspiratory-phase vascular parameters to differentiate the changes in pulmonary vascular remodeling between COPD patients and the normal population, and also to complement the pulmonary vascular characteristics of the PRISm population, which can provide a basis for early warning of COPD and high-risk populations.

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.