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Hua Zhang¹, Bingtian Dong², Jialin Han³

¹Department of Ultrasound, Affiliated Hangzhou First People's Hospital, School of Medicine, Westlake University, Hangzhou, China. 1028755029@qq.com.

Scientific reports

|July 6, 2025

概括

机器学习模型,特别是XGBoost,显著改善前列腺癌骨转移 (PCBM) 患者的生存预测. 关键因素如T阶段,PSA和格里森得分为个性化治疗计划提供信息.

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:

前列腺癌骨转移 (PCBM) 是一个重大的生存挑战.
目前用于PCBM生存预测的临床模型缺乏精度.
准确的预后工具对于管理PCBM患者至关重要.

研究的目的:

开发和验证用于增强PCBM生存预测的机器学习模型.
确定影响PCBM患者生存的关键预后因素.
评估开发的预测模型的临床适用性.

主要方法:

使用SEER数据库 (2010-2019) 获取PCBM患者数据.
采用单变量和多变量考克斯回归来进行特征识别.
开发并验证了XGBoost模型与五倍交叉验证.
通过AUC和准确度评估模型性能;通过SHAP值评估特征重要性.
进行了决策曲线和卡普兰-梅尔分析.

主要成果:

XGBoost模型表现出强大的性能,AUC为0.76 (1年),0.83 (3年) 和0.91 (5年).
重要的预后因素包括T阶段,年级,年龄,PSA和格里森得分.
化疗和放射治疗与改善的生存率有关;在多变量分析中,手术没有显著意义.
单身婚姻状况和较低的收入与较高的死亡风险相关.

结论:

XGBoost模型为PCBM生存预测提供了高准确性和可解释性.
确定预后因素和治疗影响可以指导个性化治疗策略.
为预测模型的临床集成开发了一个基于网络的工具.

关键词:

骨转移的发生.前列腺癌是什么意思前列腺癌是什么意思塞尔·塞尔 (Seer) 是一个看得见的人.预测生存的预测.在XGBoost中使用.

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