乳腺癌的预测模型:基于物流回归和混合贝叶斯网络
Fan Su1, Jianqian Chao2,3, Pei Liu1
1Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, No. 87 Ding Jia Qiao, Central Gate Street, Gulou District, Nanjing, Jiangsu, China.
BMC medical informatics and decision making
|July 13, 2023
概括
与物流回归 (LR) 模型相比,混合贝叶斯网络 (HBN) 模型在预测乳腺癌存活率方面表现出卓越的准确性和稳定性. 这种改善的表现特别明显在先进的人体表皮生长因子受体-2-阳性 (HER2+) 患者亚组中.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 准确的预后模型对于指导乳腺癌治疗决策至关重要.
- 现有的模型可能在预测生存方面存在局限性,特别是对于特定的子组.
- 将混合贝叶斯网络 (HBN) 等先进的建模技术与物流回归 (LR) 等传统方法进行比较至关重要.
研究的目的:
- 开发和比较乳腺癌患者生存期的两个预后模型.
- 评估整体患者队列和特定高级HER2+亚组中的模型性能.
- 为了确定HBN模型是否比LR模型提供更好的预测能力.
主要方法:
- 利用SEER数据库用于乳腺癌患者数据 (2018年培训/测试,2019年外部验证).
- 构建混合贝叶斯网络 (HBN) 和物流回归 (LR) 模型.
- 使用准确性,校准和净益度指标的验证模型,包括高级HER2+患者的亚组分析.
主要成果:
- HBN模型确定了与生存相关的17个变量,在内部和外部验证中表现优于LR模型 (AUC:HBN 0.900/0.871与LR 0.831/0.786).
- 对于先进的HER2+亚组,HBN的外部验证AUC (0.813) 显著超过了LR的 (0.601).
- 通过HBN模型确定了17个预后因素,包括年龄,瘤特征,治疗和受体状态.
结论:
- 与乳腺癌存活率预测的LR模型相比,HBN模型显示出更高的准确性,校准和临床实用性.
- HBN模型表现出更大的稳定性和稳定性,特别是在预测先进的HER2+患者亚组的结果方面.
- 这些发现支持采用HBN模型进行更精确的乳腺癌预后.
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