| 曹莹,姚成,陈冬,俞森权,郑健,朱红叶,高文仓.基于贝叶斯网络构建晚期非小细胞肺癌生存预后模型[J].浙江中西医结合杂志,2021,31(10): |
| 基于贝叶斯网络构建晚期非小细胞肺癌生存预后模型 |
| The survival prediction model of advanced non-small cell lung cancer based on Bayesian network |
| 投稿时间：2021-04-16 修订日期：2021-07-07 |
| DOI： |
| 中文关键词: 晚期非小细胞肺癌 生存预后模型 贝叶斯网络 |
| 英文关键词:Advanced non-small cell lung cancer Survival prediction model Bayesian network |
| 基金项目:浙江省医学会临床科研(2017ZYC-A21) |
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| 中文摘要: |
| 目的 探讨基于贝叶斯网络构建晚期非小细胞肺癌生存预后模型的临床价值。方法 回顾性分析自2015年1月1日至2020年10月31日本院收治的初治的晚期非小细胞肺癌患者临床资料和初诊时血液学指标,包括血常规、生化、凝血功能、肿瘤指标等,采用Cox模型筛选预后因素,然后利用贝叶斯网络方法构建生存预测模型。结果 最终纳入模型的预后变量共5个,包括肝脏转移、治疗线数、鳞状上皮细胞癌抗原、年龄和中性粒细胞-淋巴细胞比值。贝叶斯网络所建模型对晚期非小细胞肺癌患者生存预后预测准确率达69.44%。结论 该生存预测模型,基于贝叶斯网络的方法,同时兼顾临床特点和血液学指标,可操作性强、预测性能佳,能够更好地为临床治疗的选择和预后的判断提供决策支持。 |
| 英文摘要: |
| Objective To investigate the clinical value of Bayesian network in predicting survival of patients with advanced non-small cell lung cancer (NSCLC). Methods Patients with advanced NSCLC diagnosed in our hospital from January 2015 to October 2020 were analyzed retrospectively. The clinical manifestations and blood indicators at diagnoses including blood routine, blood biochemistry, coagulation function and tumor markers were included. The Cox regression model was performed to determine prognostic factors. Then, the new prediction model was metastases, lines of treatment, squamous cell carcinoma antigen, age and neutrophil-lymphocyte ratio were included in the survival prediction model, leading to a 69.44% accuracy. Conclusion The survival prediction model based on Bayesian network balancing clinical manifestations and blood indicators at the same time, has a high operability and accuracy, which could be used to guide the decision making and to predict the survival of patients with advanced NSCLC. |
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