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基于深度学习模型预测上皮卵巢癌患者的预后

Zihan Li¹, Jiao Wang², Yixin Zhang¹

¹Department of Epidemiology and Health Statistics, Dalian Medical University, Dalian, China.

Frontiers in oncology

|August 11, 2025

概括

这项研究开发了预后模型,以预测卵巢上皮癌患者的整体存活率. DeepSurv机器学习模型展示了最好的预测性能,有助于临床决策.

科学领域:

在瘤学瘤学.
生物统计学生物统计学
医疗保健中的机器学习

背景情况:

表皮卵巢癌 (EOC) 的死亡率和发病率很高.
准确的预后模型对于改善EOC患者的生存结果至关重要.

研究的目的:

开发和验证预后模型,用于预测表皮卵巢癌 (EOC) 患者的整体存活率 (OS).
将机器学习 (ML) 模型的性能与传统回归方法进行比较.

主要方法:

利用SEER数据库 (N=10902) 和基于医院的队列 (N=116) 进行模型开发和验证.
采用COX回归用于预后因素识别,并开发了三种ML模型 (包括DeepSurv) 和一个名ogram.
使用C指数,ROC曲线,校准曲线和决策曲线分析 (DCA) 评估的模型.

主要成果:

在EOC患者中确定了OS的12个独立预后因素.
DeepSurv模型表现出卓越的性能,其C指数为0.715 (内部) 和0.672 (外部验证).
在预测3年和5年生存率方面取得了很高的准确性,ROC曲线从0.731到0.766.

结论:

成功开发和验证了多种预测模型,包括名录和基于机器学习的方法.
DeepSurv模型显示出作为EOC患者管理的临床决策支持工具的巨大潜力.
这些模型为预测整体存活率和指导卵巢上皮癌治疗策略提供了有价值的工具.

关键词:

深度学习是一种深度学习.上皮卵巢癌卵巢癌卵巢癌机器学习是机器学习.预后预后预后幸存率幸存率生存率

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