基于贝叶斯网络的胆囊癌术后生存预后因素分析。
Analysis of prognostic factors for survival after surgery for gallbladder cancer based on a Bayesian network.
机构信息
Department of Industrial Engineering, School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an 710072, Shaanxi, China.
Department of Hepatobiliary Surgery, First Affiliated Hospital, Xi'an Jiaotong University, Xi'an 710061, Shaanxi, China.
出版信息
Sci Rep. 2017 Mar 22;7(1):293. doi: 10.1038/s41598-017-00491-3.
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.
胆囊癌（GBC）预后的相关因素仍不清楚。本研究结合贝叶斯网络（BN）和重要性度量，确定影响 GBC 患者生存时间的关键因素。使用 BayesiaLab 软件，采用 366 例接受 GBC 手术治疗患者的数据集来建立和检验 BN 模型。采用树增强朴素贝叶斯方法挖掘因素之间的关系。应用综合重要性度量来对生存时间的影响因素进行排序。BN 模型的准确率为 81.15%。对于生存时间较长（>6 个月）的患者，模型的真阳性率为 77.78%，假阳性率为 15.25%。根据建立的 BN 模型，性别、年龄和病理类型是 GBC 患者生存的独立因素。N 分期、肝浸润、T 分期、M 分期和手术类型是生存时间预测的因变量。根据重要性度量分析结果，手术类型和 TNM 分期被确定为 GBC 预后的最重要因素。
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