Machine learning-based radiomics model for risk stratification of severe asymptomatic carotid stenosis
- VernacularTitle:基于机器学习的影像组学模型在重度无症状性颈动脉狭窄危险分层中的应用
- Author:
Zhan LIU
1
;
Xiaopeng LIU
1
;
Min LIU
2
;
Yanan ZHEN
1
;
Xia ZHENG
1
;
Jianyan WEN
1
;
Zhidong YE
1
;
Peng LIU
1
Author Information
1. Department of Cardiovascular Surgery, China-Japan Friendship Hospital, Peking University China-Japan Friendship School of Medicine, Beijing, 100029, P. R. China
2. Department of Radiology, China-Japan Friendship Hospital, Beijing, 100029, P. R. China
- Publication Type:Journal Article
- Keywords:
Severe asymptomatic carotid stenosis;
machine learning;
radiomics;
prediction model;
artificial intelligence
- From:
Chinese Journal of Clinical Thoracic and Cardiovascular Surgery
2022;29(10):1270-1276
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore the utility of machine learning-based radiomics models for risk stratification of severe asymptomatic carotid stenosis (ACS). Methods The clinical data and head and neck CT angiography images of 188 patients with severe carotid artery stenosis at the Department of Cardiovascular Surgery, China-Japan Friendship Hospital from 2017 to 2021 were retrospectively collected. The patients were randomly divided into a training set (n=131, including 107 males and 24 females aged 68±8 years), and a validation set (n=57, including 50 males and 7 females aged 67±8 years). The volume of interest was manually outlined layer by layer along the edge of the carotid plaque on cross-section. Radiomics features were extracted using the Pyradiomics package of Python software. Intraclass and interclass correlation coefficient analysis, redundancy analysis, and least absolute shrinkage and selection operator regression analysis were used for feature selection. The selected radiomics features were constructed into a predictive model using 6 different supervised machine learning algorithms: logistic regression, decision tree, random forest, support vector machine, naive Bayes, and K nearest neighbor. The diagnostic efficacy of each prediction model was compared using the receiver operating characteristic (ROC) curve and the area under the curve (AUC), which were validated in the validation set. Calibration and clinical usefulness of the prediction model were evaluated using calibration curve and decision curve analysis (DCA). Results Four radiomics features were finally selected based on the training set for the construction of a predictive model. Among the 6 machine learning models, the logistic regression model exhibited higher and more stable diagnostic efficacy, with an AUC of 0.872, a sensitivity of 100.0%, and a specificity of 66.2% in the training set; the AUC, sensitivity and specificity in the validation set were 0.867, 83.3% and 78.8%, respectively. The calibration curve and DCA showed that the logistic regression model had good calibration and clinical usefulness. Conclusion The machine learning-based radiomics model shows application value in the risk stratification of patients with severe ACS.
