Construction and validation of a nomogram for predicting the incidence of hepatocellular carcinoma based on serum abnormal prothrombin and alpha-fetoprotein
10.3760/cma.j.cn113884-20240530-00165
- VernacularTitle:基于血清异常凝血酶原、甲胎蛋白构建预测肝细胞癌发病的列线图模型及模型评估
- Author:
Long YU
1
;
Xiangkun WANG
1
;
Xudong ZHANG
1
;
Zhongyuan LIU
1
;
Yuxiang GUO
1
;
Maosen WANG
1
;
Qingfang HAN
1
;
Renfeng LI
1
Author Information
1. 郑州大学第一附属医院肝胆胰外科，郑州 450052
- Publication Type:Journal Article
- Keywords:
Carcinoma, hepatocellular;
Alpha-fetoproteins;
Nomograms;
Abnormal prothrombin
- From:
Chinese Journal of Hepatobiliary Surgery
2025;31(1):1-5
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To construct a nomogram model for predicting the incidence of hepatocellular carcinoma based on serum abnormal prothrombin and alpha-fetoprotein and evaluate the predictive effect.Methods:Retrospective analysis of data from 351 patients with liver disease who received treatment at the First Affiliated Hospital of Zhengzhou University from January 2021 to December 2023, including 285 males and 66 females, aged (52.9±11.9) years. Among the 351 patients, there were 229 cases (65.2%) of hepatocellular carcinoma, 87 cases (24.8%) of liver cirrhosis, and 35 cases (10.0%) of chronic hepatitis B. All patients were randomly divided into a training set ( n=245) and a testing set ( n=106) in a 7∶3 ratio without replacement sampling. The training set was used to construct the model, and the testing set was used to evaluate the model. At the same time, gender, age, disease type, and other indicators were compared between the two sets. The risk factors of hepatocellular carcinoma were analyzed by univariate and multivariate logistic regression based on the training set, and a nomogram was constructed to predict the incidence of hepatocellular carcinoma based on the multivariate results. Receiver operating characteristic (ROC) curve and calibration curve were used to evaluate the predictive performance of nomogram, and decision curve analysis was used to evaluate the clinical applicability of the model. Results:There was no statistically significant difference in age, gender, disease type, etc. between the training and testing sets of patients (all P>0.05). Univariate logistic regression analysis showed that age, abnormal prothrombin logarithm (LnPIVKA-Ⅱ), alpha-fetoprotein logarithm (LnAFP), and diabetes were associated with hepatocellular carcinoma (all P<0.05). Multivariate logistic regression analysis showed that older age ( OR=1.07, 95% CI: 1.03-1.12), higher LnPIVKA-Ⅱ ( OR=2.97, 95% CI: 1.97-4.46), higher LnAFP ( OR=1.43, 95% CI: 1.11-1.84) and diabetes ( OR=5.17, 95% CI: 1.02-26.17) were risk factors for hepatocellular carcinoma (all P<0.05). Based on the above variables, a nomogram model for predicting the incidence of hepatocellular carcinoma was constructed. The area under the ROC curve analysis of the nomogram for predicting the incidence of hepatocellular carcinoma was 0.920 (95% CI: 0.886-0.953) in the training set and 0.934 (95% CI: 0.891-0.977) in the testing set. The calibration curve fit well with the standard curve, and the prediction was basically consistent with the actual situation. The decision curve analysis showed that the net benefit of the model was greater than 0 under most thresholds (0.1-1.0). Conclusion:The nomogram constructed based on age, LnPIVKA-Ⅱ, LnAFP and diabetes can effectively predict the incidence of hepatocellular carcinoma and has clinical applicability.
