Objective To explore the predictive value of a combined model based on clinical imaging features and radiomics for the occurrence of vulnerable coronary artery plaques,and visualize the model through Shapley algorithm for further analysis.Methods A retrospective study was conducted on 383 patients diagnosed with coronary heart disease and who underwent two CCTA examinations at Shunde Hospital of Southern Medical University from 2016 to 2020.Radiomics features were extracted from the corresponding regions of interest.A multi-step combined method was used to select the best features from each region for joint modeling.Logistic regression was employed to select important clinical imaging features,and an interpretable XGBoost clinical imaging model was constructed.The Shapley algorithm was utilized to visualize the model and interpret the feature contributions.Results Compared with single-region radiomics models,multi-region radiomics models demonstrated higher predictive performance(AUC=0.701).Combining important clinical imaging features with the joint model improved the performance even further(AUC=0.885).By analyzing the feature importance using the Shapley analysis algorithm,it was found that the first six radiomics features contributed significantly to the model's predictive results.The Shapley heatmap algorithm visualized the prediction process of vulnerable plaque occurrence.Conclusion The clinical radiomics combined model shows high accuracy and generalizability in predicting vulnerable coronary artery plaques.The visualization of interpretable machine learning algorithms ensures the practicality of the model,providing a non-invasive tool for the development of targeted treatment plans in clinical practice.
关键词
冠状动脉疾病/易损斑块/影像组学/机器学习/无创评估模型
Key words
coronary artery disease/vulnerable plaques/radiomics/machine learning/non-invasive evaluation model
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