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机器学习用于头部和部状细胞癌的生存结果:一个多中心验证研究

Rasheed Omobolaji Alabi^1,2, Orlando Guntinas-Lichius³, Mohammed Elmusrati^4,5

¹Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland. rasheed.alabi@helsinki.fi.

Scientific reports

|November 29, 2025

概括

这项研究开发了一种机器学习模型,利用临床病理学和治疗数据预测头部和部状细胞癌 (HNSCC) 患者的整体存活率. 外部验证证实了它的通用性,突出了个性化治疗的关键预后因素.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
机器学习机器学习

背景情况:

头部和部状细胞癌 (HNSCC) 通常在晚期出现,导致预后不佳.
机器学习 (ML) 模型为个性化治疗规划提供了潜力,但需要强大的外部验证.

研究的目的:

开发和外部验证用于预测HNSCC患者整体存活率 (OS) 的机器学习模型.
整合临床病理学和与治疗相关的因素,以提高预后准确度.
通过 permutation feature importance (PFI) 来识别关键的预后参数. 通过 permutation feature importance (PFI) 来确定关键的预后参数.

主要方法:

使用来自美国SEER计划 (n=40,164) 的数据开发了一个投票组合ML模型.
该模型使用来自德国 (n=3950) 和瑞典 (n=323) 的多中心数据进行了外部验证.
转换特征重要性 (PFI) 用于评估输入变量的预后意义.

主要成果:

在SEER队列中,ML模型实现了0.76的AUC和70.0%的准确性.
外部验证的AUC值为0.68 (德国) 和0.76 (瑞典),证明了性能变化的概括性.
PFI确定了诊断时的年龄,T阶段,瘤部位,婚姻状况和手术治疗作为OS的关键预测因素.

结论:

开发的ML模型对预测HNSCC患者的存活率充满希望,可以帮助做出基于风险的治疗决策.
外部地理验证对于评估模型可重现性和通用性至关重要,即使性能指标有所不同.
虽然独立验证是理想的,但数据隐私问题可能会在将其整合到ML开发管道中带来挑战.

关键词:

外部验证的验证方法头部和部状细胞癌 (HNSCC)机器学习是机器学习.总体生存率总体生存率验证研究的验证研究.

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