Development of a machine learning-based radiomics model of perivascular adipose tissue for predicting stroke risk in patients with asymptomatic carotid stenosis: a multicenter study
A combined model based on PVAT imaging features around carotid plaques can effectively predict the short-term stroke risk in ACS patients. This model may be expected to provide an important auxiliary tool for clinical prognosis assessment and treatment decisions, with potential clinical application value.
Authors
- W. S. Chen
- Ting Zhao (ORCID: https://orcid.org/0000-0003-3502-6043)
- Weiwei Hu (ORCID: https://orcid.org/0000-0002-3485-6304)
- Jinhong Sun
- Cheng Ma (ORCID: https://orcid.org/0000-0002-8148-374X)
- Jianhua Wu (ORCID: https://orcid.org/0000-0001-6093-599X)
- Guihan Lin
- Jie Zhang (ORCID: https://orcid.org/0000-0002-1704-1795)
- Zhihao Shi
- Hua Yang
- Lei Xu
- Suhong Ye
- Yongjun Chen
- Xiongying Yi
Institutions
- Wenzhou Medical University (CN)
- First Affiliated Hospital of Wenzhou Medical University (CN)
- Second Affiliated Hospital & Yuying Children's Hospital of Wenzhou Medical University (CN)
- Lishui Central Hospital (CN)
- Lishui City People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Radiology
- Published
- 2026-01-21
- DOI
- https://doi.org/10.3389/fradi.2025.1738298
- Primary Topic
- Cardiovascular Disease and Adiposity
- Type
- article
- Field-Weighted Citation Impact
- 0.00