Analysis of prognostic factors for survival after surgery for gallbladder cancer based on a Bayesian network.
Analysis of prognostic factors for survival after surgery for gallbladder cancer based on a Bayesian network.
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基于贝叶斯网络的胆囊癌术后生存预后因素分析
DOI：
10.1038/s41598-017-00491-3
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发表时间：
2017-03-22
影响因子：
4.6
通讯作者：
Cong LL
中科院分区：
文献类型：
--
作者：
Cai ZQ;Guo P;Si SB;Geng ZM;Chen C;Cong LL
胆囊癌（GBC）的预后因素尚不清楚。本研究结合贝叶斯网络（BN）与重要性措施，以确定影响GBC患者生存时间的关键因素。采用BayesiaLab软件对366例GBC手术患者的数据集进行BN模型的建立和检验。树增强朴素贝叶斯方法也被用来挖掘因素之间的关系。采用复合重要性测度对影响生存时间的因素进行排序。BN模型的准确率为81.15%。对于生存期长的患者（>6个月），模型的真阳性率为77.78%，假阳性率为15.25%。根据所建立的BN模型，性别、年龄和病理类型是影响GBC患者生存的独立因素。N分期、肝浸润、T分期、M分期和手术方式是预测生存时间的因变量。根据重要性指标分析结果，手术类型和TNM分期是影响GBC预后的最重要因素。
The factors underlying prognosis for gallbladder cancer (GBC) remain unclear. This study combines the Bayesian network (BN) with importance measures to identify the key factors that influence GBC patient survival time. A dataset of 366 patients who underwent surgical treatment for GBC was employed to establish and test a BN model using BayesiaLab software. A tree-augmented naïve Bayes method was also used to mine relationships between factors. Composite importance measures were applied to rank the influence of factors on survival time. The accuracy of BN model was 81.15%. For patients with long survival time (>6 months), the true-positive rate of the model was 77.78% and the false-positive rate was 15.25%. According to the built BN model, the sex, age, and pathological type were independent factors for survival of GBC patients. The N stage, liver infiltration, T stage, M stage, and surgical type were dependent variables for survival time prediction. Surgical type and TNM stages were identified as the most significant factors for the prognosis of GBC based on the analysis results of importance measures.