A Novel Nomogram Model for Predicting the Risk of Hepatocellular Carcinoma in Patients with Chronic Hepatitis B Infection - Wu Y; Wang M; Zhang Z; Chen G; Zhang B (Journal of Hepatocellular Carcinoma)
Yanfang Wu,1,* Meixia Wang,2,* Zhenzhen Zhang,1,* Guobin Chen,1 Boheng Zhang1 1Department of Hepatic Oncology, Zhongshan Hospital, Fudan University (Xiamen Branch), Xiamen Clinical Research Center for Cancer Therapy, Clinical Research Center for Precision medicine of abdominal tumor of Fujian Province, Xiamen, 361015, People’s Republic of China; 2Department of Hospital Infection Management, Zhongshan Hospital, Fudan University (Xiamen Branch), Xiamen, Fujian, 361015, People’s Republic of China*These authors contributed equally to this workCorrespondence: Boheng Zhang, Department of Hepatic Oncology, Zhongshan Hospital, Fudan University (Xiamen Branch), Xiamen Clinical Research Center for Cancer Therapy, Clinical Research Center for Precision medicine of abdominal tumor of Fujian Province, Xiamen, 361015, People’s Republic of China, Tel/Fax +86-592-3569583, Email zhang_boheng@126.comPurpose: Hepatitis B virus (HBV) infection is a major cause of hepatocellular carcinoma (HCC). This study aimed to construct a novel nomogram model for predicting the risk of HCC in patients with HBV infection.Patients and Methods: This retrospective study analyzed clinical data from healthcare databases in Xiamen, encompassing 5161 adults with HBV infection without HCC and 2819 adults with HBV-related HCC between January 2016 and December 2020. Subsequently, the patients were randomly divided into a training set (n=5586) and testing set (n=2394). The training set was used to identify the risk factors for HCC development and to construct an HCC risk prediction nomogram model. The predictive accuracy of the model was assessed using the receiver operating characteristic (ROC) curve analysis and decision curve analysis (DCA) in both sets. Furthermore, the performance of the nomogram model was compared with that of the existing models.Results: Multivariate analysis revealed that age, sex, liver cirrhosis, neutrophil/platelet count ratio (NLR), serum bilirubin (TBIL), aspartate aminotransferase (AST), serum albumin (ALB), serum alpha-fetoprotein (AFP), and HBV DNA were independently associated with HCC. A nomogram model was developed by incorporating these risk factors. The the receiver operating characteristic curve (AUC) of the nomogram model were 0.897 and 0.902 for the training and testing sets, respectively. Analysis of the AUC demonstrated that the nomogram model exhibited significantly enhanced predictive performance for HCC compared to the alternative risk scores in both sets. Furthermore, DCA indicated that the nomogram model provided a broad range of threshold probabilities related to the net clinical benefits. A web-based calculator was developed(https://nomogram-model-hcc.shinyapps.io/DynNomapp/).Conclusion: The novel nomogram model, which includes age, sex, liver cirrhosis, NLR, TBIL, AST, ALB, AFP, and HBV DNA as factors, precisely predicts the risk of HCC in patients with chronic hepatitis B(CHB) and outperforms the existing models.Keywords: HBV infection, HCC, neutrophil/platelet count ratio, prediction