Predicting major adverse cardiovascular events in suspected myocarditis patients using cmr late gadolinium enhancement radiomics: a time-to-event prognostication modeling

Abstract

Abstract Background Myocarditis, characterized by inflammation of the heart tissue, exhibits a wide range of clinical manifestations; however, stratifying its risk is difficult. Objective We aimed to prognosticate Major Adverse Cardiovascular Events (MACE, i.e., death, sustained ventricular arrhythmia, heart failure hospitalization, and recurrent myocarditis) in patients with suspected myocarditis using cardiac magnetic resonate (CMR) late gadolinium enhancement (LGE) radiomic features. Method We enrolled consecutive 1099 patients (age: 47.68±16.56, 39.63% female) undergoing CMR with the initial suspicion of myocarditis based on the judgment of the referring physician, recruited from two major registries. We used fully automated deep learning tools to determine the segmental extent of LGE in the left ventricle. Furthermore, we pre-processed the dataset through bias-field correction and intensity normalization to ensure the model consistency across various image acquisitions. Different radiomic features, including shape, intensity, and texture, were extracted from CMR-LGE. We employed several feature selection methods and time-to-event survival models based on radiomics features. The development process, covering pre-processing, feature selection, and hyperparameter optimization, utilized 70% of the dataset (10-fold cross-validation). The evaluation was performed on the remaining 30% of the test set. Model performance was measured using the c-index and cumulative AUC (C-AUC) on the untouched 30% test set. Results The top-performing model, employing a 90% variance threshold with the survival support gradient boosting model, achieved a C-Index of 0.76 and a cumulative C-AUC of 0.80. The Random Forest Survival model was a close second, with a C-Index of 0.75 and a C-AUC of 0.79. The conventional multivariate Cox-PH model achieved a C-Index of 0.67 and a C-AUC of 0.70. Conclusion Utilizing radiomics features from the CMR-LGE, combined with machine learning algorithms, allows accurate prediction of MACE in suspected myocarditis patients. The performance of the Radiomics model could be improved by including other modality information.