机器学习方法用于预测激素敏感前列腺癌患者的进展
Bingyu Zhu1,2, Haiyang Jiang2, Chongjian Zhang2
1Department of Urology, The Affiliated Chengdu 363 Hospital of Southwest Medical University, Chengdu, Sichuan, China.
Frontiers in oncology
|March 2, 2026
概括
机器学习模型可以预测荷尔蒙敏感前列腺癌 (HSPC) 进展为抵抗割的前列腺癌 (CRPC). 像Random Forest这样的组合方法显示出强大的预测性能,有助于患者早期风险分层.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 机器学习 机器学习
背景情况:
- 荷尔蒙敏感前列腺癌 (HSPC) 经常在安卓基因剥夺疗法 (ADT) 后进展为割抵抗性前列腺癌 (CRPC).
- 预测这种进展对于及时的临床干预和治疗规划至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 模型,用于预测HSPC向CRPC的进展.
- 通过统计分析,识别与此过渡相关的显著临床特征和标记.
主要方法:
- 从410名HSPC患者的临床数据分析.
- 应用ML模型,包括决策树,随机森林,XGBoost,ANN和SVM.
- 使用遗传算法 (GA) 选择特征,并通过AUC,校准图和学习曲线对模型进行评估.
主要成果:
- 集合学习方法,特别是随机森林 (RF) 和XGBoost,表现出卓越的表现.
- 在测试组中,RF的AUC为0.873,而XGBoost的AUC为0.866.6.
- 模型表现出良好的校准和没有显著的过拟合,表明可靠的预测能力.
结论:
- 整体ML方法,特别是随机森林,在预测HSPC进展方面是有效的.
- 基线临床数据有可能导致HSPC患者的风险分层.
- 建议在更大的,多中心前性研究中进一步验证临床整合.
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