对于患有乳腺癌的老年患者,深度学习引导的辅助化疗选择
Enzhao Zhu1, Linmei Zhang2, Jiayi Wang1
1School of Medicine, Tongji University, Shanghai, China.
Breast cancer research and treatment
|January 31, 2024
概括
深度学习模型可以为老年乳腺癌患者个性化辅助化疗,改善生存结果. 最好的模型,BITES,确定了影响治疗决策的关键患者因素.
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:
- 在老年乳腺癌患者中辅助化疗的疗效仍然存在争议.
- 需要个性化治疗建议,以优化这一群体的治疗结果.
研究的目的:
- 开发和验证深度学习 (DL) 模型,为老年乳腺癌患者提供个性化辅助化疗建议.
- 以DL模型为指导,评估治疗对生存的益处.
主要方法:
- 训练了六种因果推理模型,以提供个性化化疗建议.
- 对接受DL推治疗的患者与没有接受DL治疗的患者的结局进行比较.
- 使用逆概率治疗权重 (IPTW) 来减少偏差,并使用线性回归,风险差异 (RD) 和SurvSHAP (t) 分析结果.
主要成果:
- 均衡的个人治疗效应生存数据 (BITES) 模型显示出最佳性能.
- 遵守BITES建议与显著的生存益处有关 (多变量HR:0.78,IPTW调整HR:0.74).
- 影响化疗决定的关键患者因素包括癌症亚型,瘤大小,淋巴结状况,TNM阶段,等级和手术类型.
结论:
- 深度学习模型在指导老年乳腺癌患者的辅助化疗决策方面显示出显著的潜力.
- 个性化推可以在这个人群中改善生存结果.
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