Construction of a machine learning model for identifying clinical high-risk carotid plaques based on radiomics
- VernacularTitle:基于影像组学构建识别临床高风险颈动脉斑块的机器学习模型
- Author:
Xiaohui WANG
1
;
Xiaoshuo LÜ
1
;
Zhan LIU
1
;
Yanan ZHEN
2
;
Fan LIN
2
;
Xia ZHENG
2
;
Xiaopeng LIU
2
;
Guang SUN
2
;
Jianyan WEN
1
;
Zhidong YE
1
;
Peng LIU
1
Author Information
1. 1. Peking University China-Japan Friendship School of Clinical Medicine, Beijing, 100029, P. R. China 2. Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, 100029, P. R. China
2. Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Beijing, 100029, P. R. China
- Publication Type:Journal Article
- Keywords:
Carotid plaque;
radiomics;
machine learning
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2024;31(01):24-34
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a radiomics model for identifying clinical high-risk carotid plaques. Methods A retrospective analysis was conducted on patients with carotid artery stenosis in China-Japan Friendship Hospital from December 2016 to June 2022. The patients were classified as a clinical high-risk carotid plaque group and a clinical low-risk carotid plaque group according to the occurrence of stroke, transient ischemic attack and other cerebrovascular clinical symptoms within six months. Six machine learning models including eXtreme Gradient Boosting, support vector machine, Gaussian Naive Bayesian, logical regression, K-nearest neighbors and artificial neural network were established. We also constructed a joint predictive model combined with logistic regression analysis of clinical risk factors. Results Finally 652 patients were collected, including 427 males and 225 females, with an average age of 68.2 years. The results showed that the prediction ability of eXtreme Gradient Boosting was the best among the six machine learning models, and the area under the curve (AUC) in validation dataset was 0.751. At the same time, the AUC of eXtreme Gradient Boosting joint prediction model established by clinical data and carotid artery imaging data validation dataset was 0.823. Conclusion Radiomics features combined with clinical feature model can effectively identify clinical high-risk carotid plaques.
