Predicton of major adverse cardiovascular events in patients with hypertrophic cardiomyopathy by deep learning and radiomics

Introduction Hypertrophic cardiomyopathy (HCM) patients may be at risk for major adverse cardiovascular events (MACE), making risk stratification essential for implementing interventions in high-risk individuals. Deep transfer learning (DTL) and radiomics have made significant advances in the medical field; however, to date, no studies have combined echocardiography in HCM patients with DTL and radiomics to develop predictive models for identifying individuals at risk for MACE. Methods This study is a retrospective analysis that included 210 HCM patients, with a mean follow-up time of 29.44 ± 16.21 months. Among the patients, 59 experienced MACE and 151 non-MACE. The patients were randomly divided into training and validation sets in an 8:2 ratio. We collected chest parasternal left ventricular long-axis and short-axis images, with the left ventricular myocardial region defined as the region of interest (ROI). Radiomics features were extracted using the Pyradiomics software package, and DTL features were obtained through the pre-trained Resnet50 model. These radiomics and DTL features were then combined, and feature selection was conducted using the Least Absolute Shrinkage and Selection Operator (LASSO). The selected features were used to construct the DTL-RAD predictive model with machine learning algorithms. The model's diagnostic performance was evaluated using the Receiver Operating Characteristic (ROC) curve and Decision Curve Analysis (DCA). Finally, we compared the prediction performance of the DTL-RAD model with those of models built using only radiomics features or only DTL features. Results The diagnostic performance of the DTL-RAD model in both the training and validation sets was excellent, with AUC values of 0.936 and 0.918, specificity values of 0.852 and 0.767, and sensitivity values of 0.892 and 0.929, respectively. It significantly outperformed models that used only radiomics or DTL features. Furthermore, the DCA demonstrated that the DTL-RAD model exhibited superior clinical applicability and effectiveness, surpassing the performance of other models. Conclusion The DTL-RAD model demonstrated exceptional performance in identifying HCM patients at risk of MACE, accurately detecting high-risk individuals among HCM patients at an early stage. This provides a basis for precise clinical intervention, effectively reducing the incidence of MACE in HCM patients.