一个深度学习生存模型来评估乳头甲状腺癌的生存预后:基于人口的队列研究
Guibin Zheng1,2, Peng Wei3, Danxia Li1
1Department of Thyroid Surgery, The Affiliated Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong, China.
Annals of surgical oncology
|April 20, 2025
概括
一个深度学习模型准确地预测了皮毛甲状腺癌 (PTC) 患者的生存率. 该工具将患者分为低风险和高风险组,帮助制定个性化治疗策略.
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:
- 深度学习模型在预测复杂数据集的生存结果方面表现有前途.
- 有限的研究存在于深度学习生存分析,专门针对乳头甲状腺癌 (PTC).
- 这项研究解决了在PTC患者管理中需要先进的预后工具的需求.
研究的目的:
- 开发和验证一种深度学习模型,用于预测乳头甲状腺癌 (PTC) 患者的生存率.
- 利用临床风险因素来提高预后准确度.
- 为了个性化治疗规划,将患者分为不同的风险组.
主要方法:
- 使用来自美国SEER计划 (2000-2020) 的PTC患者数据开发了Cox比例危险深度神经网络 (DeepSurv) 模型.
- 模型性能使用MD安德森癌症中心 (MDACC) 和癌症基因组图谱 (TCGA) 的独立数据集进行了验证.
- 根据10年整体生存 (OS) 风险得分,患者被分为低风险和高风险组.
主要成果:
- 该DeepSurv模型实现了高预测准确度,一致性指数为0.798 (SEER),0.893 (MDACC) 和0.848 (TCGA).
- 该模型有效地区分了低风险和高风险的PTC患者组,基于10年的OS.
- 与低风险患者相比,高风险患者在所有验证数据集中表现出明显较差的生存状况 (P < 0.001).
结论:
- 经过验证的DeepSurv模型准确地将乳头甲状腺癌 (PTC) 患者分为低风险和高风险预后组.
- 这种深度学习方法为定制PTC个性化治疗策略提供了有价值的预后信息.
- 这些发现支持将深度学习工具整合到PTC管理的临床决策中.
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