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

Lan Wang; Ruiling He; Lili Zhao; Jia Wang; Zhengzi Geng; Tao Ren; Guo Zhang; Peng Zhang; Kaiqiang Tang; Chaofei Gao; Fei Chen; Liting Zhang; Yonghe Zhou; Xin Li; Fanbin He; Hui Huan; Wenjuan Wang; Yunxiao Liang; Juan Tang; Fang Ai; Tingyu Wang; Liyun Zheng; Zhongwei Zhao; Jiansong Ji; Wei Liu; Jiaojiao Xu; Bo Liu; Xuemei Wang; Yao Zhang; Qiong Yan; Muhan Lv; Xiaomei Chen; Shuhua Zhang; Yihua Wang; Yang Liu; Li Yin; Yanni Liu; Yanqing Huang; Yunfang Liu; Kun Wang; Meiqin Su; Li Bian; Ping An; Xin Zhang; Linxue Qian; Shao Li; Xiaolong Qi

arXiv · 2306.07505

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

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Abstract

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.

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Lan Wang, Ruiling He, Lili Zhao, Jia Wang, Zhengzi Geng, Tao Ren, Guo Zhang, Peng Zhang, Kaiqiang Tang, Chaofei Gao, Fei Chen, Liting Zhang, Yonghe Zhou, Xin Li, Fanbin He, Hui Huan, Wenjuan Wang, Yunxiao Liang, Juan Tang, Fang Ai, Tingyu Wang, Liyun Zheng, Zhongwei Zhao, Jiansong Ji, Wei Liu, Jiaojiao Xu, Bo Liu, Xuemei Wang, Yao Zhang, Qiong Yan, Muhan Lv, Xiaomei Chen, Shuhua Zhang, Yihua Wang, Yang Liu, Li Yin, Yanni Liu, Yanqing Huang, Yunfang Liu, Kun Wang, Meiqin Su, Li Bian, Ping An, Xin Zhang, Linxue Qian, Shao Li, Xiaolong Qi. 2023-06-13. Deep learning radiomics for assessment of gastroesophageal varices in people with compensated advanced chronic liver disease. https://arxiv.org/abs/2306.07505

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