Catalyst Awardee

Project Description

A novel plaque stability prediction model research based on the radiomics and single-cell transcriptomics of pericarotid artery adipose tissue

Liu Bao, MD |Peking Unon Medical College Hosipital;  Shao Jiang, MS; Lai Zhichao, MD; Li Kang, MD; Xu Leyin, MD; Chen Junye; Du Fenghe; Wang Jiaxian; Zhu Zhan; Shu Keqiang
Competition Sponsor: Chinese Academy of Medical Sciences
Awardee Year: 2022

Carotid artery stenosis is an important cause of stroke. Unstable carotid plaque shedding is the main cause of stroke, which has a significant impact on the average life expectancy of the population. Therefore, there is an urgent need for early diagnosis of plaque stability. A large number of recent studies published in Lancet and other journals have confirmed that perivascular adipose tissue (PVAT) is an important factor leading to the aggravation of atherosclerosis. Our group and international studies have confirmed that PVAT inflammation occurs earlier than vascular endothelium. However, at present, the interaction mechanism between PVAT and plaque stability is still unclear, and there is a lack of effective means to determine the stability of carotid plaque before operation. We have carried out plaque stability prediction research based on plaque CT radiomics and plaque 68Ga-FAPI PET/MRI. We has developed the preparation technology of PVAT single cell suspension which could meet the requirements of single cell RNA sequencing and formulated the original carotid artery PVAT image segmentation scheme for radiomics. In this study, we will further establish a cohort of patients with carotid artery stenosis to integrate the pathological section staining results of carotid plaque, PVAT radiomics and PVAT single cell RNA sequencing, which could determine the corresponding relationship between PVAT imaging characteristics and PVAT cellular components and differentially expressed genes. We will construct a novel prediction model of plaque stability based on PVAT imaging features through machine learning and to push the diagnostic boundary of carotid atherosclerotic disease forward.

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