Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.
Department of Hepatobiliary Surgery, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710061, Shaanxi, China.
Eur J Surg Oncol. 2020 Nov;46(11):2106-2113. doi: 10.1016/j.ejso.2020.07.009. Epub 2020 Jul 31.
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.
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.
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.
A BN model was more accurate than a Cox regression-based nomogram for prediction of survival in GBC patients undergoing curative-intent resection.
本研究建立了预测胆囊癌（GBC）患者术后生存的列线图和贝叶斯网络（BN）模型，并比较了两种模型的性能。
使用 2008 年至 2017 年期间在六家中国三级医院接受根治性切除术的 698 例 GBC 患者的数据，建立并验证了生存预测模型。在一个肝胆中心对 381 例患者的数据进行模型构建和内部验证，然后在其他五个中心的 317 例患者的数据上进行外部验证。基于独立预后变量构建了 BN 模型和列线图模型。基于接收者操作特征曲线下面积（AUC）、模型准确性和混淆矩阵比较 BN 和列线图模型的性能。
独立的预后变量包括年龄、病理分级、肝浸润、T 分期、N 分期和切缘。内部验证中，BN 和列线图的 AUC 分别为 84.14%和 78.22%，模型准确性分别为 75.65%和 72.17%。外部验证中，BN 和列线图的 AUC 分别为 76.46%和 70.19%，模型准确性分别为 66.88%和 60.25%。基于混淆矩阵，列线图的真阳性率较高，但真阴性率显著较低。
对于接受根治性切除术的 GBC 患者，BN 模型比基于 Cox 回归的列线图更准确地预测生存。
