使用机器学习模型在前列腺癌患者中比较主动监测和警等待与激进治疗
Siqi Hu1,2, Chun-Pin Chang1,2, John Snyder3
1Huntsman Cancer Institute, Salt Lake City, UT.
JCO clinical cancer informatics
|November 21, 2023
概括
机器学习准确地区分了前列腺癌保守护理中的主动监视 (AS) 和警等待 (WW). 这项研究强调了在人口研究中需要分别分析AS和WW的必要性.
科学领域:
- 在瘤学瘤学.
- 机器学习 机器学习
- 医疗保健服务研究 医疗服务研究
背景情况:
- 积极监测 (AS) 和警等待 (WW) 是低风险前列腺癌的保守管理策略.
- 在2021年,59.6%的低风险前列腺癌患者选择了AS,但AS和WW经常被集体研究.
- 区分AS和WW对于准确的治疗结果分析至关重要.
研究的目的:
- 开发和验证一种机器学习模型,以在前列腺癌的保守治疗中区分AS和WW.
- 分析从2004年到2017年初始前列腺癌管理的趋势.
- 调查治疗方式与慢性疾病风险之间的关联.
主要方法:
- 分析了18134名在2004年至2017年期间被诊断为前列腺腺癌的前列腺腺癌患者.
- 使用机器学习算法,包括后勤回归,并进行10倍交叉验证.
- 考克斯的比例危险模型评估了与不同治疗方法相关的慢性疾病风险.
主要成果:
- 机器学习模型表现出良好的性能,接收器操作曲线下的面积为0.73和F-score为0.79.
- 在2004年至2017年期间,在低风险和中等风险的前列腺癌患者中观察到AS利用率的显著增加.
- 激进治疗与降低前列腺癌死亡率相关,但与AS相比,阿尔茨海默病和高血压等疾病的风险增加.
结论:
- 机器学习模型在保守的前列腺癌管理中有效地区分AS和WW.
- 这些发现强调了在人口水平研究中区分AS和WW的重要性.
- 为了准确了解治疗结果和风险,需要对AS和WW进行单独的分析.
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