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在使用机器学习进行激进囊切除术的患者的生存分析中评估炎症标志物

Naci Burak Çınar¹, Hasan Yılmaz², Efe Yılmaz Taşyürek³

¹Department of Urology, Kutahya City Hospital, Kutahya, Turkey.

World journal of urology

|October 21, 2025

概括

机器学习模型预测了激进囊切除术后的生存率. 结合SIRI,NLR和PLR等炎症标志物,提高了预测准确度,T阶段和白蛋白是关键因素.

科学领域:

泌尿器科泌尿器科泌尿器科泌尿器科
在瘤学瘤学.
医疗信息学医疗信息学

背景情况:

激进囊切除术 (RC) 是肌肉侵入性膀癌的标准治疗方法.
预测RC后的整体存活率 (OS) 对患者管理至关重要.
机器学习 (ML) 提供了利用各种患者数据改善生存预测的潜力.

研究的目的:

利用人口统计,临床和病理数据开发一个ML模型来预测RC患者的OS.
评估炎症标志物的附加值,以提高ML模型的预测准确度.

主要方法:

追溯分析了241名RC患者的数据.
使用原始特征 (数据集-1) 和结合炎症标志物 (数据集-2和数据集-3) 的ML模型的开发.
基于预测能力的炎症标志物 (SIRI,NLR,PLR) 的系统整合和评估,使用SHAP进行特征重要性分析.

主要成果:

没有炎症标志物的ML模型达到0.78.8的最大F1得分.
包括所有炎症标志物并没有显著改善F1得分 (0.73-0.78).
将SIRI,NLR和PLR连续添加到数据集-3中的人口统计数据中,随机森林模型的最高F1得分为0.80. SHAP分析发现T阶段,手术前白蛋白和LVI是最强的预测因素.

结论:

使用人口统计和临床数据的ML模型可以在RC患者中以很好的准确性预测OS (最大F1=0.78).
结合特定的炎症标志物 (SIRI,NLR,PLR) 进一步提高预测性能 (最大F1=0.80).
瘤阶段和手术前白蛋白被确定为这些ML模型中最重要的预测因素.

关键词:

人工智能的人工智能是人工智能.炎症标志物是一种炎症标志物.机器学习机器学习进行激进的囊切除术.沙普利的添加剂解释 (SHAP)幸存的生存方式

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