首页|Deep learning radiomics for assessment of gastroesophageal varices in
people with compensated advanced chronic liver disease
Deep learning radiomics for assessment of gastroesophageal varices in
people with compensated advanced chronic liver disease
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Arxiv
Objective: Bleeding from gastroesophageal varices (GEV) is a medical
emergency associated with high mortality. We aim to construct an artificial
intelligence-based model of two-dimensional shear wave elastography (2D-SWE) of
the liver and spleen to precisely assess the risk of GEV and high-risk
gastroesophageal varices (HRV).
Design: A prospective multicenter study was conducted in patients with
compensated advanced chronic liver disease. 305 patients were enrolled from 12
hospitals, and finally 265 patients were included, with 1136 liver stiffness
measurement (LSM) images and 1042 spleen stiffness measurement (SSM) images
generated by 2D-SWE. We leveraged deep learning methods to uncover associations
between image features and patient risk, and thus conducted models to predict
GEV and HRV.
Results: A multi-modality Deep Learning Risk Prediction model (DLRP) was
constructed to assess GEV and HRV, based on LSM and SSM images, and clinical
information. Validation analysis revealed that the AUCs of DLRP were 0.91 for
GEV (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 risk
indicators, including the value of LSM and SSM. Moreover, DLPR was better than
the model using individual parameters, including LSM and SSM images. In HRV
prediction, the 2D-SWE images of SSM outperform LSM (p < 0.01).
Conclusion: DLRP shows excellent performance in predicting GEV and HRV over
canonical risk indicators LSM and SSM. Additionally, the 2D-SWE images of SSM
provided more information for better accuracy in predicting HRV than the LSM.
Arxiv
