An applied study on the assessment of concomitant pulmonary hypertension in patients with chronic obstructive pulmonary disease based on whole-lung CT imaging histologic features

Background: Pulmonary hypertension (PH) is a common complication in the progression of chronic obstructive pulmonary disease (COPD) and plays a pivotal role in the advancement of chronic pulmonary heart disease. This condition is closely associated with acute exacerbations and poor prognosis in COPD patients. Currently, methods used in clinical practice to assess PH each have certain limitations. This study aimed to investigate the diagnostic value of whole-lung computed tomography (CT)-based Radiomics features in identifying PH in patients with COPD. Methods: A total of 171 patients diagnosed with COPD by clinical and pulmonary function testing at the First Affiliated Hospital of Hebei North University between August 2022 and November 2024 were retrospectively enrolled and randomly divided into a training cohort (n=119) and a validation cohort (n=52) at a 7:3 ratio. Clinical data including demographic characteristics, hematological parameters, and coagulation profiles were collected. On axial CT images, the diameters of the main pulmonary artery (MPA) and ascending aortic (AA) were measured, and the MPA/AA ratio was calculated. SPSS.27.0 statistical software was used to perform univariate logistic regression to initially screen potentially significant variables, and multifactorial logistic analysis to screen independent risk factors for concomitant PH in COPD patients. Radiomics features were extracted from the entire lung using the three-dimensional (3D) Slicer Radiomics plug-in, and the feature data were normalized and downscaled, and then the most informative features were screened using the Random Forest (RF) algorithm. Clinical, MPA/AA, Radiomics, and joint models were constructed using R software, and their diagnostic performance was compared using receiver operating characteristic (ROC) curves, calibration plots, and decision curves analysis (DCA). Results: Multivariate logistic regression identified D-dimer [P=0.008; odds ratio (OR) =2.404, 95% confidence interval (CI): 1.264-4.572], and MPA/AA ratio (P=0.044; OR =2.249, 95% CI: 1.021-4.955) as independent risk factors for PH in patients with COPD. Among the 1,168 extracted Radiomics features, five were selected via RF as key predictors. The ROC curves analysis demonstrated that the combined model exhibited significantly superior diagnostic performance compared to the clinical model, the MPA/AA model, and the Radiomics model in both the training cohort area under the ROC curve (AUC =0.884) and the validation cohort (AUC =0.874). Calibration plots, Hosmer-Lemeshow test, and DCA confirmed the superior clinical utility of the combined model. A nomogram was developed to provide intuitive visualization of individual predictor contributions. Conclusions: This combined model integrates CT imaging features, D-dimer levels, and the MPA/AA ratio, enabling noninvasive identification of COPD patients with concomitant PH. It serves as an effective tool for clinical diagnostic support.