ArticleBMC pulmonary medicine2025
CT-Based radiomics nomogram of lung and mediastinal features to identify cardiovascular disease in chronic obstructive pulmonary disease: a multicenter study.
Article in BMC pulmonary medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Multi‑feature Prediction Model for Coronary Heart Disease Comorbidity in Middle‑aged and Older Adults with COPD Based on Machine Learning and SHAP.International journal of chronic obstructive pulmonary disease · 2026Article
- Development and validation of a multimodal clinical-radiomics-deep learning nomogram based on automated chest CT segmentation for classifying COPD severity: a multicenter study.Frontiers in medicine · 2026Article
- The Role of Computed Tomography and Artificial Intelligence in Evaluating the Comorbidities of Chronic Obstructive Pulmonary Disease: A One-Stop CT Scanning for Lung Cancer Screening.International journal of chronic obstructive pulmonary disease · 2025Review
- Nomogram Model for Identifying the Risk of Coronary Heart Disease in Patients with Chronic Obstructive Pulmonary Disease Based on Deep Learning Radiomics and Clinical Data: A Multicenter Study.International journal of chronic obstructive pulmonary disease · 2025Article
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14 authors.
Funding
Abstract
RATIONALE AND
objectivesTo investigate the performance of two diagnostic models based on CT-derived lung and mediastinum radiomics nomograms for identifying cardiovascular disease (CVD) in Chronic Obstructive Pulmonary Disease (COPD) patients. MATERIALS AND
methodsHospitalized participants with COPD were retrospectively recruited between September 2015 and April 2023. Clinical data and visual coronary artery calcium score (CACS) were collected. Radiomics features of lung and mediastinum were extracted. Least absolute shrinkage and selection operator (LASSO) logistic regression was applied for feature selection and radiomic model construction. We constructed 3 radiomics models, based on lung, mediastinum, and combined lung-and-mediastinum. Multivariate logistic regression model was used to establish radiomics nomograms. The performance of radiomics nomograms was evaluated by area under the ROC curve (AUC) and decision curve analysis (DCA).
resultsOf 686 COPD patients, 131 had a history of CVD. Age, neutrophilic granulocyte percentage, hematocrit and GOLD stage were independent clinical factors for CVD. 12 lung, and 6 mediastinum radiomic features were collected to construct the radiomics models. As the lung-and-mediastinum radiomics model included the same 6 features as the mediastinum model, finally 2 radiomics models were studied (lung, mediastinum). The 2 radiomics nomograms showed better discriminatory ability (AUC: 0.79, 95%CI [0.72, 0.86] for lung; 0.86, 95%CI [0.81, 0.92]) for mediastinum) than the clinical factors model (AUC: 0.71, 95%CI [0.64, 0.78]) and visual CACS (AUC: 0.65, 95%CI [0.57, 0.72]). DCA demonstrated the 2 radiomics nomograms outperformed the clinical factors and CACS across the majority of the range of reasonable threshold probabilities.
conclusionWe developed chest CT-based nomograms to identify CVD in COPD patients, in particular based on mediastinum features, had better discriminatory power than clinical factors and visual CACS.
trial registrationThis retrospective study was approved by the institutional review boards at Second Affiliated Hospital of Naval Medical University, Tongji Hospital of Tongji University and Sir Run Run Shaw Hospital (ChiCTR2300069929 March 29, 2023). Retrospectively registered.
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