深度学习用于预测三阴性乳腺癌的存活率:在现实世界的队列中开发和验证
Yiyue Xu1, Butuo Li1, Bing Zou1
1Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, No.440, Ji Yan Road, Jinan, 250117, Shandong, P.R. China.
Scientific reports
|August 18, 2025
概括
一个新的深度学习生存模型改善了三阴性乳腺癌 (TNBC) 患者的预后. 这种工具增强了患者分层和治疗指南对这种侵袭性疾病.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 三阴性乳腺癌 (TNBC) 是具有侵略性和异质性的,需要改善患者分层才能有效治疗.
- 目前的预后方法可能无法完全捕捉TNBC的复杂性,导致治疗决策不足最佳.
研究的目的:
- 开发和验证一种基于深度学习的生存模型,用于TNBC预后.
- 创建个性化的预后系统,以增强患者分层和指导临床治疗决策.
主要方法:
- 利用来自SEER数据库的37818名TNBC患者的大数据集,分为培训,验证和测试集.
- 开发了一个使用 pysurvival 算法,一种深度学习方法的生存模型.
- 使用C指数验证了模型的性能,并将其与传统的Cox比例危险 (CPH) 和随机生存 (RSH) 模型进行了比较.
- 通过使用曲线下的面积 (AUC) 对AJCC-TNM分期系统进行了个性化预后系统的评估.
主要成果:
- 深度学习生存模型表现出强的表现,C指数为0.824 (验证) 和0.816 (测试),表现优于CPH和RSH模型.
- 在现实世界队列上的外部验证证实了该模型的稳定性,C指数为0.758.
- 与AJCC-TNM分期 (AUC 0.771) 相比,个性化的预后系统实现了更高的预测准确性 (AUC 0.821).
结论:
- 开发的深度学习生存模型和个性化的预后系统在评估TNBC预后时提供了更高的准确性.
- 这些工具促进了更好的患者分层,帮助临床医生制定更知情和个性化的治疗建议.
- 这些发现表明,利用人工智能在三阴性乳腺癌的精密瘤学方面取得了重大进展.
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