Survival prediction for gallbladder carcinoma after curative resection: Comparison of nomogram and Bayesian network models

Background: In this study, we developed a nomogram and a Bayesian network (BN) model for prediction of survival in gallbladder carcinoma (GBC) patients following surgery and compared the performance of the two models. Methods: Survival prediction models were established and validated using data from 698 patients with GBC who underwent curative-intent resection between 2008 and 2017 at one of six Chinese tertiary hospitals. Model construction and internal validation were performed using data from 381 patients at one hepatobiliary center, and external validation was then performed using data from 317 patients at the other five centers. A BN model and a nomogram model were constructed based on the independent prognostic variables. Performance of the BN and nomogram models was compared based on area under receiver operating characteristic curves (AUC), model accuracy, and a confusion matrix. Results: Independent prognostic variables included age, pathological grade, liver infiltration, T stage, N stage, and margin. In internal validation, AUC was 84.14% and 78.22% for the BN and nomogram, respectively, and model accuracy was 75.65% and 72.17%, respectively. In external validation, AUC was 76.46% and 70.19% for the BN and nomogram, respectively, with model accuracy of 66.88% and 60.25%, respectively. Based on the confusion matrix, the nomogram had a higher true positive rate but a substantially lower true negative rate compared to the BN. Conclusion: A BN model was more accurate than a Cox regression-based nomogram for prediction of survival in GBC patients undergoing curative-intent resection.