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基于机器学习的割抵抗性前列腺癌的生存预测:使用全面临床数据集的多模型分析

Jeong Hyun Lee¹, Jaeyun Jeong², Young Jin Ahn¹

¹Department of Urology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul 06273, Republic of Korea.

Journal of personalized medicine

|September 26, 2025

概括

机器学习模型,包括随机生存森林 (RSF) 和XGBoost,与传统方法相比,显著改善了割抵抗性前列腺癌 (CRPC) 患者的生存预测. 这些先进的模型为个性化治疗规划提供了准确,可解释的预后工具.

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:

准确的生存预测对于治疗割抵抗性前列腺癌 (CRPC) 的治疗计划至关重要.
传统的统计模型在处理复杂的数据相互作用和CRPC预后的变量包含方面存在局限性.
机器学习 (ML) 在CRPC中提供了更强大的生存预测的潜力.

研究的目的:

开发和评估ML模型来预测CRPC患者的癌症特异性死亡率 (CSM),整体死亡率 (OM) 和短期生存率.
将ML模型的性能与CRPC生存预测的传统统计方法进行比较.
通过使用可解释的ML技术,确定CRPC中生存结果的关键预测因素.

主要方法:

从801名CRPC患者回顾收集了46个变量.
开发多种ML模型 (RSF,XGBoost,LightGBM,物流回归) 用于生存预测.
性能评估使用C指数,AUC,准确度,精度,回忆,F1得分和SHAP进行解释性.

主要成果:

在RSF模型中,CSM (0.772) 和OM (0.771) 的C指数最高.
RSF和XGBoost模型在预测2年和3年生存率方面表现出卓越的表现.
SHAP分析确定了第一线CRPC治疗的时间,血红蛋白和性酸酶作为关键的预后因素.

结论:

机器学习模型,特别是RSF和XGBoost,在预测CRPC存活率方面表现优于传统方法.
这些ML模型为个性化CRPC治疗提供了准确和可解释的预后工具.
建议对新疗法进行外部验证和纳入临床实施.

关键词:

抗割的抗割药物机器学习是机器学习.预测算法预测算法前列腺新生体前列腺瘤.幸存率幸存率生存率

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