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Xingyue Huo¹, Manish Kohli¹, Joseph Finkelstein¹

¹University of Utah, Salt Lake City, UT, USA.

Studies in health technology and informatics

|July 1, 2025

概括

机器学习模型可以预测转移性割抵抗性前列腺癌 (mCRPC) 患者的24个月死亡率. 随机森林模型,使用前列腺特异性抗原 (PSA) 等临床因素,显示出最佳性能.

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:

准确的预后生物标志物对于癌症患者生存风险评估至关重要.
目前的前列腺特异性抗原 (PSA) 和临床因素等生物标志物对前列腺癌预后的预测准确性有局限性.
转移性割抵抗性前列腺癌 (mCRPC) 需要改进的工具来预测患者的结果.

研究的目的:

开发和评估用于预测mCRPC患者24个月死亡率的机器学习模型.
识别导致mCRPC死亡率预测的关键临床和人口特征.
为了比较不同的机器学习算法在mCRPC中的预后预测的性能.

主要方法:

分析了703名mCRPC患者的队列.
评估了41个临床和人口特征.
实现和比较了包括XGBoost,支持矢量机 (SVM) 和随机森林在内的机器学习模型.

主要成果:

随机森林模型实现了最高的性能,准确度为0.67和曲线下的面积 (AUC) 为0.68.
该模型有效地区分了生存时间小于或大于24个月的患者.
发现的关键预测因素包括前列腺特异性抗原 (PSA),专蛋白和乳酸脱酶 (LDH).

结论:

机器学习模型可以有效利用临床因素来预测癌症患者的死亡率.
随机森林模型在mCRPC中显示出预后预测的前景.
已识别的PSA,白蛋白和LDH等预测因子可以为临床决策和患者管理提供信息.

关键词:

生物标志物生物标志物机器学习是机器学习.转移性割抵抗性前列腺癌 (mCRPC) 是一种

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