Development and validation of a computed tomography myocardial perfusion imaging radiomic model for major adverse cardiovascular events prediction: a multicenter study
The radiomic features from multiparametric CT-MPI maps simultaneously captured perfusion features associated with MACE at both macrovascular and microvascular levels. The combined model exhibited improved MACE prognostic performance compared with conventional models while maintaining high interpretability.
Authors
- guokun wang (ORCID: https://orcid.org/0000-0002-6085-2358)
- Fan Yang (ORCID: https://orcid.org/0000-0001-8005-4997)
- Baoying Chen (ORCID: https://orcid.org/0000-0002-9998-6433)
- Runjianya Ling (ORCID: https://orcid.org/0000-0002-1185-6918)
- Mengmeng Zhu (ORCID: https://orcid.org/0000-0001-7864-2976)
- Zhiqi Zhong
- Dong Li
- Yuehua Li
- Yi Xu
- Yanming Zhao
- Lili Guo
- Shengliang Liu
- Weifang Kong
- Ping Chen
- Yarong Yu
- Zhang Zhang
- Yilin Tian
Institutions
- University of Shanghai for Science and Technology (CN)
- University of Electronic Science and Technology of China (CN)
- Harbin Medical University (CN)
- Xuzhou Medical College (CN)
- Shanghai Jiao Tong University (CN)
- Ruijin Hospital (CN)
- Tianjin Medical University General Hospital (CN)
- Huaian First People’s Hospital (CN)
- Medical Center Hospital (US)
- Second People’s Hospital of Huai’an (CN)
- Second Affiliated Hospital of Harbin Medical University (CN)
- Shanghai Sixth People's Hospital (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- European Heart Journal - Cardiovascular Imaging
- Published
- 2026-02-05
- DOI
- https://doi.org/10.1093/ehjci/jeag044
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00