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
The nomogram model, which integrates clinical, radiomics, and deep learning features, exhibits promising performance in predicting CHD risk among COPD patients. It may offer valuable insights for early intervention and management strategies for CHD.
Authors
- Xiuhua Peng (ORCID: https://orcid.org/0000-0001-8442-2005)
- Hupo Bian (ORCID: https://orcid.org/0009-0002-6857-1611)
- Han‐Zhu Qian (ORCID: https://orcid.org/0000-0003-2785-2628)
- Pengliang Xu
- Shaoqi Zhu (ORCID: https://orcid.org/0009-0009-4775-5471)
- Jianping Jiang (ORCID: https://orcid.org/0000-0002-1051-7797)
- Jingnan Xue
- Mei Li (ORCID: https://orcid.org/0000-0002-9021-5886)
- Luying Qi
- Yifeng Zheng (ORCID: https://orcid.org/0000-0001-7852-6051)
- Hongxing Zhao
Institutions
- Huzhou Normal University (CN)
- Huzhou Central Hospital (CN)
- Third People's Hospital of Huzhou (CN)
Publication Details
- Journal
- International Journal of COPD
- Published
- 2025-09-01
- DOI
- https://doi.org/10.2147/copd.s539307
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Huzhou University