ORIGINAL RESEARCH article
Front. Oncol.
Sec. Gastrointestinal Cancers: Hepato Pancreatic Biliary Cancers
Construction of a Bayesian network-based risk prediction model for hepatocellular carcinoma in cirrhotic patients
Provisionally accepted
- 1Xinjiang Medical University, Ürümqi, China
- 2People's Hospital of Xinjiang Uygur Autonomous Region, Urumqi, China
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Objective:To investigate clinical data of hospitalised cirrhosis patients, identify risk factors for cirrhosis progression to hepatocellular carcinoma, establish a risk prediction model, and provide scientific basis for early identification of high-risk patients. Results: This study enrolled 1,204 individuals, including 1,128 cirrhosis patients, of whom 76 progressed to liver malignancy. Multivariate logistic regression analysis indicated that female gender was a protective factor against cirrhosis progression to liver malignancy(OR=0.532, OR95% CI=0.297-0.952); while hepatitis B, elevated total cholesterol, and reduced antithrombin III activity were risk factors for progression to hepatocellular carcinoma (OR=4.080, OR95%CI=2.443-6.814;OR=2.308, OR95%CI=1.132-4.707, OR=2.982, OR 95% CI=1.389-6.402) (P<0.05); The LASSO regression model ultimately identified 14 variables most significantly associated with the transformation of cirrhosis into malignant liver tumours.The results of the Bayesian network model indicate that DOI, AT3, SCC, CRP, and TC have a direct connection with the occurrence of hepatic malignancy. The performance of the Bayesian network model (AUC = 0.857, Brier Score = 0.052, Accuracy = 0.940, C-index = 0.833 (95% CI: 0.784–0.882), Sensitivity = 0.836, Specificity=0.744) was superior to that of the logistic regression model. Ten-fold cross-validation showed an average accuracy of 0.93. After balancing sensitivity and specificity, the optimal threshold was determined by maximizing the Youden index (0.052), with a predicted probability >0.052 indicating progression from liver cirrhosis to hepatic malignancy. Based on the ROC curve, threshold 1 was set at 0.2 and threshold 2 at 0.8, establishing risk stratification as follows: low risk, intermediate risk, and high risk. This resulted in 318 patients classified as low risk, 42 as intermediate risk, and 1 as high risk. Conclusions: Gender, hepatitis B, TC, and AT3 constitute risk factors for hepatocellular carcinoma in cirrhotic patients; Gender, hepatitis type, DOI, FT4, AT3, SCC, CRP, MAO, and Ca are associated with the progression of liver cirrhosis to malignant liver tumours either directly or indirectly. The risk prediction model constructed by combining LASSO regression with Bayesian networks demonstrates good predictive value.
Keywords: Bayesian network mode4, cirrhosis1, hepatocellular carcinoma2, Influencing factors3, Risk Prediction5
Received: 30 Oct 2025; Accepted: 19 Dec 2025.
Copyright: © 2025 Ma, Song and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yu Qing Yang
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