通过深度学习确定乳腺癌患者新辅助系统治疗的个体适应性
Enzhao Zhu1, Linmei Zhang2, Yixian Liu3
1School of Medicine, Tongji University, Shanghai, China.
概括
一个新的深度学习模型,BITES,个性化乳腺癌的新辅助系统疗法 (NST),显著改善生存率和降低死亡率. BITES确定了特定的患者群体,最有可能从NST中受益.
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:
- 由于患者异质性,乳腺癌的新辅助全身疗法 (NST) 存活益处在争论中.
- 需要个性化治疗策略来优化结果.
研究的目的:
- 为个性化乳腺癌治疗建议开发深度学习 (DL) 模型.
- 确定从NST中受益最多的患者子组.
主要方法:
- 为个性化治疗建议开发了六种深度学习模型.
- 对结果的比较分析,基于对模型建议的遵守.
- 多变量后勤和Poisson回归用于特征影响可视化.
主要成果:
- 均衡个人治疗对生存效应 (BITES) 模型表现出卓越的性能.
- BITES显示了显著的生存优势 (HR:0.51,RD:21.46,RMST差:21.51).
- 患有晚期TNM阶段,三阴亚型和较大的瘤的患者从NST中受益最多.
结论:
- 比特斯模型显示出有助于临床决策和提供定量治疗见解的潜力.
- 建议在临床环境中进一步验证并包括更多的患者特征.
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