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利用机器学习模型探索前列腺癌诊断,治疗和生存的差异

Reuben Adatorwovor¹, Rasaq Oladapo², Parisa Ghasemi³

¹Department of Biostatistics, College of Public Health, University of Kentucky, Lexington, KY, USA.

Cancer control : journal of the Moffitt Cancer Center

|October 8, 2025

概括

机器学习模型将年龄,治疗和吸烟确定为前列腺癌生存率的关键预测因素. 年轻的患者和接受手术的患者有更好的结果,突出了服务不足地区的差异.

科学领域:

在瘤学瘤学.
数据科学数据科学数据科学
公共卫生公共卫生

背景情况:

前列腺癌 (CaP) 是全球癌症死亡的主要原因,在美国存在很大的差异.
在医疗服务不足的地区,如阿巴拉契亚,由于进入障碍,CaP死亡率更高.
机器学习 (ML) 可以在CaP中识别复杂的生存预测因素.

研究的目的:

应用ML模型来确定CAP存活的关键预测因素.
评估社会人口统计学和临床因素对CAP结果的影响.
用肯塔基州癌症登记数据分析CaP存活率.

主要方法:

来自肯塔基州癌症登记处的37893例CaP病例 (2010-2022) 的回顾性分析.
利用Kaplan-Meier,随机生存森林 (RSF) 和弹性网回归来估计生存和变量重要性.
采用多重归算缺少的数据和离开一个的交叉验证.

主要成果:

诊断时的年龄,治疗方式和吸烟状态是最重要的生存预测因素.
在RSF模型中,年龄的影响力比治疗类型大约2.5倍.
较年轻的患者 (<60岁) 的结果更好;保险状况,瘤等级和淋巴结参与也显示出差异.

结论:

ML模型有效地确定了年龄,吸烟和治疗作为关键的CaP生存预测因素.
调查结果强调了早期查和公平治疗的必要性.
干预措施应解决肯塔基州和阿巴拉契亚州等高风险地区的差异.

关键词:

肯塔基州的癌症注册中心.早期诊断早期诊断早期诊断弹性网弹性网是一种弹性网.机器学习是机器学习.前列腺癌是前列腺癌.随机生存森林随机生存森林吸烟是为了吸烟.存在生存差异的差异.治疗不平等的差异.

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