An explainable CT-based machine learning model integrating carotid plaque and perivascular adipose tissue for predicting symptomatic plaques
This explainable radiomics-based model, combining CP-PVAT features with clinical risk factors, may serve as a promising tool for identifying symptomatic carotid plaques and supporting individualized cerebrovascular risk assessment.
Authors
- Aiyun Sun
- Lijing Shao (ORCID: https://orcid.org/0000-0002-1334-8853)
- Jingkai Xu (ORCID: https://orcid.org/0000-0002-3640-1602)
- Chengeng Ye
- Shulan Chen
- Haibo Wang (ORCID: https://orcid.org/0000-0003-4809-4897)
- Yimin Chen (ORCID: https://orcid.org/0000-0002-1602-3253)
- Xiangwu Zheng (ORCID: https://orcid.org/0000-0001-7512-6036)
- Guoquan Cao (ORCID: https://orcid.org/0000-0001-9450-2690)
- Zhe Xiao
- Kun Tang (ORCID: https://orcid.org/0000-0003-0996-0481)
Institutions
- Wenzhou Medical University (CN)
- First Affiliated Hospital of Wenzhou Medical University (CN)
- United Imaging Healthcare (China) (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-09-24
- DOI
- https://doi.org/10.3389/fneur.2025.1679861
- Primary Topic
- Cardiovascular Disease and Adiposity
- Type
- article
- Field-Weighted Citation Impact
- 0.00