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用机器学习对患有恶性卵巢瘤的患者进行个性化生存预测和风险分层:以外部验证为基础的基于人口的研究

Shangcheng Yan¹, Zhen Cao¹, Qiyao Zhang¹

¹Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.

|July 17, 2025

概括

这项研究开发了一种机器学习模型,用于预测罕见的卵巢癌恶性卵巢瘤 (MSO) 的存活率. 该模型准确预测患者的结果,并有助于风险分层.

科学领域:

妇科瘤学妇科瘤学
内分泌手术内分泌手术
机器学习在医学中的应用

背景情况:

恶性卵巢瘤 (MSO) 是一种罕见的甲状腺癌,发生在卵巢瘤中.
现有的MSO的预后和分期系统并不完善.
对于MSO患者管理,需要准确的预后预测和风险分层.

研究的目的:

开发和验证一种机器学习 (ML) 模型,用于预测MSO患者的整体存活率 (OS).
根据ML预测,为MSO创建一个风险分层系统.
为改善患者咨询和治疗决策提供一个工具.

主要方法:

利用SEER计划 (1975-2021) 数据进行培训,并使用文献审查队列进行测试.
训练并评估了多种ML模型,包括随机生存森林 (RSF),梯度增强机 (GBM) 和Cox模型.
使用c指数,时间依赖的AUC,Brier分数和分层能力评估模型性能.
通过SHapley添加式扩展 (SHAP) 解释了表现最好的模型.

主要成果:

在外部测试队列中,RSF模型表现出优异的性能 (c指数:0.841,AUC:0.852).
由SHAP确定的更糟糕的OS的关键预测因素包括老年,子宫切除术,较大的瘤大小和先进的AJCC阶段.
开发了一个交互式Web应用程序,用于可视化预测和功能贡献.

结论:

这项研究提出了MSO的第一个外部验证的时间到事件预后模型.
基于ML的预测和分层提供精确的个体患者洞察力.
开发的工具可以协助临床决策对MSO治疗和监测.

关键词:

恶性卵巢瘤 (MSO) 是一种恶性卵巢瘤.机器学习 (ML) 是指机器学习.预测模型预测模型风险分层的分层化生存分析,生存分析.

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