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Objective: Bleeding from gastroesophageal varices (GEV) is a medicalemergency associated with high mortality. We aim to construct an artificialintelligence-based model of two-dimensional shear wave elastography (2D-SWE) ofthe liver and spleen to precisely assess the risk of GEV and high-riskgastroesophageal varices (HRV).  Design: A prospective multicenter study was conducted in patients withcompensated advanced chronic liver disease. 305 patients were enrolled from 12hospitals, and finally 265 patients were included, with 1136 liver stiffnessmeasurement (LSM) images and 1042 spleen stiffness measurement (SSM) imagesgenerated by 2D-SWE. We leveraged deep learning methods to uncover associationsbetween image features and patient risk, and thus conducted models to predictGEV and HRV.  Results: A multi-modality Deep Learning Risk Prediction model (DLRP) wasconstructed to assess GEV and HRV, based on LSM and SSM images, and clinicalinformation. Validation analysis revealed that the AUCs of DLRP were 0.91 forGEV (95% CI 0.90 to 0.93, p < 0.05) and 0.88 for HRV (95% CI 0.86 to 0.89, p <0.01), which were significantly and robustly better than canonical riskindicators, including the value of LSM and SSM. Moreover, DLPR was better thanthe model using individual parameters, including LSM and SSM images. In HRVprediction, the 2D-SWE images of SSM outperform LSM (p < 0.01).  Conclusion: DLRP shows excellent performance in predicting GEV and HRV overcanonical risk indicators LSM and SSM. Additionally, the 2D-SWE images of SSMprovided more information for better accuracy in predicting HRV than the LSM.

Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease