使用SEER-Medicare数据集进行IV期乳腺癌长期幸存者的预测建模
Nabil Adam1,2, Robert Wieder3,4
1Phalcon, LLC, Manhasset, NY 11030, USA.
Cancers
|December 17, 2024
概括
这项研究开发了深度学习模型,以预测IV期乳腺癌患者的存活率. 这些模型旨在优化治疗决策,延长寿命,同时尽量减少不良事件.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 人工智能的人工智能
背景情况:
- 第四期乳腺癌 (BC) 的存活率有限,需要个性化治疗策略.
- 目前的模型提供了最小的生存延长,突出了对先进预测工具的需求.
研究的目的:
- 开发高可信度深度学习 (DL) 模型,用于预测IV期BC患者的个人存活率.
- 通过考虑患者特定的情况和潜在的治疗结果来指导治疗.
主要方法:
- 使用了与SEER-Medicare相关的数据集 (1991-2016) 与14,312名IV期BC患者.
- 使用的DL模型 (DeepSurv,DeepHit,Nnet-survival,Cox-Time) 包含时间固定的和时间变化的共变量.
- 验证模型使用随机抽样进行培训,验证和测试,通过亚马逊SageMaker进行微调.
主要成果:
- 通过整合时间固定的和时间变化的协变量,实现了低于10%的预测误差.
- 证明了模型能够生成准确的个体患者生存曲线的能力.
- 证实了在晚期乳腺癌中基于DL的生存预测的原理证明.
结论:
- 开发了DL模型,能够对个人IV期BC患者的高可靠性生存预测.
- 这些模型可以通过模拟"如果"情景来协助治疗决策.
- 目标是优化治疗以延长存活时间,并尽量减少转移性乳腺癌的不良事件.
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