Deep learning model for prediction of hepatocellular carcinoma in patients with HBV-related cirrhosis on antiviral therapy
Joon Yeul Nam, Dong Hyun Sinn, Jun Ho Bae, Eun Sun Jang, Jin‐Wook Kim, Sook‐Hyang Jeong
Abstract
BACKGROUND & AIMS: Personalised risk prediction of the development of hepatocellular carcinoma (HCC) among patients with liver cirrhosis on potent antiviral therapy is important for targeted screening and individualised intervention. This study aimed to develop and validate a new model for risk prediction of HCC development based on deep learning, and to compare it with previously reported risk models. METHODS: )-index. RESULTS: <0.001). The misclassification rate of this model was 23.7% (model accuracy: 76.3%) in the validation group. CONCLUSIONS: The deep-learning-based model had better performance than the previous models for predicting the HCC risk in patients with HBV-related cirrhosis on potent antivirals. LAY SUMMARY: For early detection of hepatocellular carcinoma, it is important to maintain regular surveillance. However, there is currently no standard prediction model for risk stratification that can be used to establish a personalised surveillance strategy. We develop and validate a deep-learning-based model that showed better performance than previous models.