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预测5年癌存活率的机器学习研究:一个多中心研究

Mingchao Wang¹, Yiming Ding¹, Zhenwei Zhou¹

¹Department of Urology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China.

Translational andrology and urology

|December 10, 2025

概括

这项研究开发了一种机器学习模型,用于预测细胞癌 (RCC) 患者的5年生存率. 梯度增强机器模型显示出高精度,有助于对RCC管理的临床决策.

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:

细胞癌 (RCC) 呈现出显著的生物学异质性,在准确的预后和有效的治疗策略方面提出了挑战.
了解预后因素对于改善患者的治疗结果和指导RCC管理中的临床决策至关重要.

研究的目的:

确定细胞癌 (RCC) 患者5年生存期的关键预后因素.
开发和验证机器学习模型,用于预测RCC的5年生存率.
在RCC管理中支持基于证据的临床决策.

主要方法:

从监测,流行病学和最终结果 (SEER) 数据库 (2010-2021) 中分析临床数据.
使用最小绝对收缩和选择操作员 (LASSO) 回归和Boruta的预测因素选择.
开发和评估七个机器学习算法用于5年生存预测,包括渐变增强机 (GBM).
使用沙普利增量解释 (SHAP) 分析解释模型预测.

主要成果:

梯度增强机 (GBM) 模型在预测RCC患者5年生存率方面表现出卓越的表现,AUC达到0.841.
GBM 模型的Brier 评分 (0.127) 和估计校准指数 (ECI) (0.005) 是最低的,这表明它的准确性和可靠性很高.
SHAP分析确定了影响RCC患者存活率的显著临床因素,证实了它们的预后重要性.

结论:

开发了一个强大的机器学习模型,特别是GBM,用于准确预测RCC患者的5年生存期.
预测模型和确定的预后因素可以帮助临床医生在对RCC患者护理方面做出更明智的决定.
这项研究强调了机器学习在提高预后准确性和指导细胞癌的治疗策略方面的潜力.

关键词:

五年生存率五年生存率机器学习机器学习监测,流行病学和最终结果计划 (SEER)细胞癌是脏细胞癌.

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