在头癌中使用贝叶斯网络和马尔科夫毯子进行临床解释的生存风险分层
Keyur D Shah1, Ibrahim Chamseddine2, Xiaohan Yuan3
1Department of Radiation Oncology, Winship Cancer Institute, Emory University, Atlanta, Georgia.
International journal of radiation oncology, biology, physics
|October 13, 2025
概括
这项研究使用贝叶斯网络确定了头癌存活率的关键因素. 开发的模型准确预测患者的风险,帮助个性化治疗决策.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 头癌 (HNC) 的生存预测是复杂的,需要确定关键的预后因素.
- 传统模型可能无法完全捕捉HNC中的临床,解剖和治疗变量之间的复杂依赖关系.
研究的目的:
- 开发一个临床上可解释的贝叶斯网络 (BN) 模型用于HNC生存.
- 通过2年生存率 (SVy2) 的马尔科夫毯 (MB) 来识别一种节的生存相关特征集.
- 评估衍生BN模型的预后和因果效用.
主要方法:
- 使用了RADCURE数据集 (3346名HNC患者) 接受了最终的 (化疗) 辐射治疗.
- 构建了一个概率BN来建模变量依赖性;提取了SVy2.2的MB.
- 使用MB特征训练了后勤回归模型,在时间分割数据集 (2174列车/820测试) 上验证,使用包括AUC,C指数和Kaplan-Meier分析在内的性能指标.
主要成果:
- 在SVy2的MB中确定了6个关键特征:ECOG性能状态,T阶段,HPV状态,疾病部位,初级GTV和治疗方式.
- 在试验数据上,BN模型实现了0.65的AUC和0.78的C指数,根据风险显著分层患者 (日志级P<.01).
- 在子组中观察到强的表现:HPV阴性,T4阶段和大型GTV队列,具有显著的生存分层.
结论:
- 一个紧的,来自MB的BN模型有效地分层了头癌的生存风险.
- 该模型的可解释结构支持可解释的预后,并有助于个性化治疗决策.
- 因果分析表明,ECOG 0,HPV阳性状态和化疗辐射对生存的影响是积极的.
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