针对使用深度学习的三阴性乳腺癌患者进行个性化化疗选择
Xinyi Yang1, Reshetov Iogr Vladmirovich1, Poltavskaya Maria Georgievna1
1I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia.
Frontiers in medicine
|July 5, 2024
概括
深度学习模型,特别是SNB模型,可以识别受益于化疗的三阴性乳腺癌 (TNBC) 患者. 这种方法提供了个性化的治疗建议,并改善了生存结果.
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:
- 治疗三阴性乳腺癌 (TNBC) 的辅助化疗具有潜在的不确定性和过度治疗的挑战.
- 个性化治疗选择对于优化TNBC患者的治疗结果至关重要.
研究的目的:
- 评估深度学习 (DL) 模型在指导TNBC个性化化疗选择中的有效性.
- 量化患者基线特征对化疗治疗疗效的影响.
主要方法:
- 分析了10,070名女性TNBC患者的队列.
- 自正常化平衡 (SNB) 深度学习模型是为个人治疗效果估计而开发的.
- 逆概率治疗权重 (IPTW) 用于减轻对比治疗结果的偏差.
- 混合效应多变量线性回归可视化了基线特征对化疗选择的影响.
主要成果:
- 以SNB模型为指导的治疗在TNBC患者中显示出显著的生存益处 (IPTW调整HR:0.53).
- 国家银行模型的建议超过了其他模型和当前的临床指南.
- 对于模型不推化疗的患者,没有观察到生存益处.
- 美国国家药物局确定了患有较大的瘤和更积极的淋巴结的老年患者是化疗的最佳候选人.
结论:
- SNB深度学习模型在识别可以从化疗中受益的TNBC患者方面表现有前途.
- 这种方法提供了基于个体生存的洞察力和个性化的治疗建议.
- 需要对这些模型进行进一步的临床验证.
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