Prediction of plaque progression using different machine learning models of pericoronary adipose tissue radiomics based on coronary computed tomography angiography
At baseline CCTA, the RF-based PCAT radiomics model demonstrated excellent predictive ability for PP. Furthermore, at follow-up CCTA, our results indicated that both increase in FAI and decrease in PCAT volume can independently predict PP, and their combination provided enhanced predictive ability.
Authors
- Steven Shinn‐Forng Peng (ORCID: https://orcid.org/0000-0002-8177-5187)
- Jianping Zhu (ORCID: https://orcid.org/0000-0002-4094-5450)
- Wencai Huang (ORCID: https://orcid.org/0000-0001-7977-8349)
- Qian Wu (ORCID: https://orcid.org/0000-0001-5746-2677)
- Jingjing Pan (ORCID: https://orcid.org/0000-0002-1122-1103)
- Tingting Fu (ORCID: https://orcid.org/0000-0001-8173-8389)
- Qing Huang (ORCID: https://orcid.org/0000-0002-7086-028X)
- Jia-Ni Zou
Institutions
- Wuhan College (CN)
- Central Hospital of Wuhan (CN)
- Wuhan General Hospital of Guangzhou (CN)
- Wuhan University of Science and Technology (CN)
Publication Details
- Journal
- European Journal of Radiology Open
- Published
- 2025-02-15
- DOI
- https://doi.org/10.1016/j.ejro.2025.100638
- Citations
- 5
- Primary Topic
- Cardiovascular Disease and Adiposity
- Type
- article
- Field-Weighted Citation Impact
- 6.94
Funders
- Chinese People’s Liberation Army