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一个在线工具,用于预测肺外小细胞癌的存活率,随机森林预测

¹Cancer Center, West China Hospital, Sichuan University, Chengdu, China.

Frontiers in oncology

|July 17, 2023

概括

一个新的机器学习工具预测了罕见的肺外小细胞癌 (EPSCC) 的存活率. 该工具使用大规模数据,提供了一种可靠的方法来预测EPSCC患者的结果.

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:

肺外小细胞癌 (EPSCC) 是一种罕见的恶性瘤.
目前的知识主要依赖于小细胞肺癌的数据.
对于EPSCC,有很大需要可靠的生存预测工具.

研究的目的:

为EPSCC开发和验证基于机器学习的生存预测模型.
为EPSCC生存预测创建一个公开可访问的在线工具.
为了确定EPSCC的关键预后因素.

主要方法:

使用监测流行病学和最终结果 (SEER) 数据库对大型队列 (n=3,921) 进行培训和内部验证.
对比了各种机器学习算法的性能,用于生存预测.
在外部队列 (n=68) 上验证所选模型,以评估概括性.
部署了表现最好的模型作为免费的在线预测工具.

主要成果:

随机森林模型在内部验证上表现最好,曲线下的面积 (AUC) 为0.736-0.800.
随机森林模型在决策曲线分析中表现优于TNM分类.
该模型在外部验证队列中实现了0.739-0.811的AUC.
现在可以公开使用EPSCC的在线生存预测工具.

结论:

一个强大的在线生存预测工具EPSCC已经开发使用机器学习和广泛的数据.
对于EPSCC生存的关键预测因素包括年龄,TNM阶段和手术干预.
这种工具增强了EPSCC的临床决策和患者管理.

关键词:

在 SEER 数据库中,我们可以看到肺外小细胞癌的肺外小细胞癌机器学习是机器学习.在线工具在线工具.幸存率幸存率生存率

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