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. 2025 Jun;8(6):e70253.
doi: 10.1002/cnr2.70253.

Prediction Model for Familial Aggregated HBV-Associated Hepatocellular Carcinoma Based on Serum Biomarkers

Linmei Zhong  1 , Guole Nie  2 , Qiaoping Wu  3 , Honglong Zhang  4 , Haiping Wang  4   5   6 , Jun Yan  4   5   6
Affiliations

Prediction Model for Familial Aggregated HBV-Associated Hepatocellular Carcinoma Based on Serum Biomarkers

Linmei Zhong et al. Cancer Rep (Hoboken). 2025 Jun.

Abstract

Background: Accurate assessment of the risk of familial aggregated hepatitis B virus (HBV)-associated hepatocellular carcinoma (HCC) and regular surveillance for these patients at high risk may be valuable to reduce the occurrence and improve the prognosis of HCC.

Aim: This study aimed to develop a simple and reliable prediction model for the risk of HCC in these patients.

Methods and results: This study analyzed clinical laboratory results from a database of 1285 patients with familial aggregated HBV who attended the First Hospital of Lanzhou University from January 2010 to December 2019. Univariate and multivariate logistic regression (LR) analysis showed that hemoglobin (Hb), neutrophil percentage (NP), total protein (TP), glutamyl transpeptidase (GGT), alglucosidase alfa (AFU), aspartate aminotransferase (AST) to Alanine aminotransferase (ALT) ratio (AAR), and alpha-fetoprotein (AFP) were identified to be independent risk factors for HBV-associated HCC. Prediction models were developed using a multivariate LR model, classification and regression tree, Native Bayes, Bagged tree, AdaBoost, and random forest. We used a multivariate LR model as a benchmark for performance assessment (AUC = 0.737). The results showed that the Native Bayes model had an AUC of 0.749, which was better than that of the other models.

Conclusion: Finally, the Native Bayes model demonstrated better predictive performance for HCC, which helped in the clinical decision-making and identification of HCC high-risk groups.

Keywords: familial aggregated HBV; hepatocellular carcinoma; machine learning; risk prediction.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

FIGURE 1
FIGURE 1
Flow chart of this study and the analytical flow of predictive model construction. ML, machine learning; CART, classification and regression trees; RF, random forest.
FIGURE 2
FIGURE 2
ROCs of different characteristics. The ROCs of each of the seven independent variables for the HCC prediction. The optimal cut‐off values for each were selected based on their respective ROCs. HB, hemoglobin; NP, neutrophil percentage; TP, total protein; GGT, glutamyl transpeptidase; AFU, alglucosidase alfa; AAR, aspartate aminotransferase (AST) to alanine aminotransferase (ALT) ratio; AFP, alpha‐fetoprotein.
FIGURE 3
FIGURE 3
The ROCs of different prediction models. Predictive models were constructed using the seven independent variables screened, and the results of ROCs for six predictive models. CART, Classification and regression trees.
See this image and copyright information in PMC

References

    1. Sung H., Ferlay J., Siegel R. L., et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA: A Cancer Journal for Clinicians 71, no. 3 (2021): 209–249. - PubMed
    1. Hanazaki K., Kajikawa S., Shimozawa N., et al., “Survival and Recurrence After Hepatic Resection of 386 Consecutive Patients With Hepatocellular Carcinoma,” Journal of the American College of Surgeons 191, no. 4 (2000): 381–388. - PubMed
    1. Nguyen A., Nguyen H., Yee K. C., Palmer A. J., Blizzard C. L., and de Graaff B., “A Systematic Review and Narrative Synthesis of Health Economic Evaluations of Hepatocellular Carcinoma Screening Strategies,” Value in Health 24, no. 5 (2021): 733–743. - PubMed
    1. EASL‐EORTC , “Clinical Practice Guidelines: Management of Hepatocellular Carcinoma,” Journal of Hepatology 56, no. 4 (2012): 908–943. - PubMed
    1. Marrero J. A., Kulik L. M., Sirlin C. B., et al., “Diagnosis, Staging, and Management of Hepatocellular Carcinoma: 2018 Practice Guidance by the American Association for the Study of Liver Diseases,” Hepatology 68, no. 2 (2018): 723–750. - PubMed

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