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机器学习的应用用于预测患有细胞癌的患者的无进展和整体存活率

Caroline W Grant¹, Jerry Li², Swan Lin³

¹Department of Molecular Pharmacology and Experimental Therapeutics, Mayo Clinic, Rochester, Minnesota, USA.

Clinical and translational science

|October 23, 2025

概括

与传统模型相比,机器学习模型,特别是基于树的方法,显著改善了晚期细胞癌 (RCC) 患者的生存预测. 这些先进的模型整合了瘤生长抑制指标,以更准确地预测无进展和整体生存.

科学领域:

在瘤学瘤学.
生物统计学生物统计学
机器学习机器学习

背景情况:

晚期细胞癌 (RCC) 的患者结局不佳,生存率有限.
准确预测治疗结果对于推进精准医学和RCC药物开发至关重要.
结合瘤生长抑制 (TGI) 指标的机器学习 (ML) 模型对生存预测有希望,但尚未在RCC中探索.

研究的目的:

评估参数 (PM),半参数 (SPM) 和ML模型在预测RCC患者无进展生存 (PFS) 和总生存 (OS) 的性能.
将基于树的ML模型的预测准确度与使用TGI指标和基线数据的传统生存模型进行比较.
确定最有效的建模方法,用于预测高级RCC的生存结果.

主要方法:

利用了来自四项临床试验的1839名RCC患者的数据.
采用参数 (PM),半参数 (SPM) 和各种ML模型,包括随机生存森林 (RSF) 和XGBoost.
将数据分为训练 (70%) 和测试 (30%) 集,应用特征选择,并使用引导重新抽样 (n=100) 进行验证.
使用C指数和综合障碍评分来评估模型性能.

主要成果:

基于树的ML模型 (RSF,XGBoost) 在训练和测试数据集中在预测PFS和OS方面显著优于PM和SPM模型 (p <0.05).
与其他方法 (9-35) 相比,RSF对PFS和OS的预测准确度更高,需要较少的共变量 (3-5).
莎普利的添加式扩展揭示了预测因子之间的复杂,非线性关系,突出了基于树的模型捕捉复杂的预测相互作用的能力.

结论:

基于树的ML模型,特别是RSF,与传统的生存模型相比,在晚期RCC患者中预测PFS和OS的性能优于传统的生存模型.
这些先进的模型有效地整合了TGI指标和基线数据,提供了更准确的预后见解.
需要进一步验证,以确认这些模型在不同疗法和不同瘤严重程度的患者群体中的通用性.

关键词:

在 RCC RCC 里面,有很多东西可以做.在XGBoost中使用.机器学习是机器学习.总体存活率总体存活率预测预测预测预测没有进展的生存率.随机的森林随机的森林随机生存森林随机生存森林细胞癌瘤是细胞癌.抑制瘤生长抑制瘤生长

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