Development of a clinical prediction model for acute myocarditis using coronary computed tomography angiography-based radiomics

Abstract

Background: Both acute myocarditis patients and normal cohort usually present with normal coronary computed tomography angiography (CCTA) performance, and the performance of CCTA radiomics on the prediction for myocarditis is still unclear. This study aims to build a clinical prediction model for acute myocarditis using CCTA-based radiomics.Methods: A total of 215 consecutive patients from the Affiliated Jinhua Hospital, Zhejiang University School of Medicine (Center 1) and Sir Run Run Shaw Hospital, Zhejiang University School of Medicine (Center 2) who underwent CCTA and were diagnosed as normal or acute myocarditis were enrolled. All CCTA images of myocardium were automatically segmented to extract radiomics features. Pearson correlation analysis was used to identify features that were highly correlated with others. The application of the 5-fold cross-validation test reduced reliance on a single training set and provided more robust performance estimation. The best radiomics prediction model was chosen and combined with the clinical labels to construct a clinical-radiomics model for the classification of patients as with or without myocarditis. Results: Pearson’s correlation and least absolute shrinkage and selection operator (LASSO) regression analyses identified 10 radiomics features and 7 clinical features which demonstrated the best correlation. The receiver operating characteristic curves of the three models that used the support vector machine (SVM) demonstrated the best performance. The area under the curves (AUCs) of Model 1 (Rad-score model) using training and test datasets were 0.970 (0.949–0.991) and 0.912 (0.832–0.992), respectively. The AUCs of Model 2 (clinical factors model) for the training and test datasets were 0.992 (0.983–1.000) and 0.943 (0.875–1.000), respectively. Model 3 (clinical factors and Rad-score model) demonstrated the best results, with AUCs of 1.000 (0.999–1.000) and 0.951 (0.880–1.000) in the training and test datasets, respectively. Conclusions: The CCTA-based radiomics model constructed using machine learning demonstrated good performance for predicting myocarditis.