基于机器学习的癌预后和转移模型
Yuxiang Zhang1, Na Hong2, Sida Huang3
1National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China.
Cancer innovation
|December 13, 2023
概括
物流回归模型准确地预测癌存活率和转移,帮助高风险患者进行早期干预. 这种机器学习方法可以提高癌患者的预后准确度.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 癌占泌尿系统瘤的20%,30%的病例在诊断时呈现为转移性.
- 虽然局部癌通常可以通过手术治愈,但转移性疾病经常导致复发和死亡.
- 准确的生存预测和高风险转移患者的鉴定对于有效的干预和改善结果至关重要.
研究的目的:
- 开发和比较机器学习模型,用于预测癌患者的3年生存率和转移.
- 确定用于预后和癌风险分层的最有效模型.
主要方法:
- 利用来自监测,流行病学和最终结果 (SEER) 数据库的12,394名癌患者的数据.
- 开发和评估了八种机器学习模型:支持矢量机器,物流回归,决策树,随机森林,XGBoost,AdaBoost,K-最近邻居和多层感知器.
- 使用准确度,精度,灵敏度,特异性,F1分数和接收器操作特征下的区域 (AUROC) 评估模型性能.
主要成果:
- 后勤回归证明了生存率 (0.741) 和转移率 (0.804) 预测的最高AUROC.
- 后勤回归模型实现了0.684的准确性为3年生存预测和0.800的转移预测.
- 逻辑回归的具体性能指标包括两种预测任务的灵敏度,特异性和F1分数.
结论:
- 机器学习模型,特别是逻辑回归,可以有效地预测癌存活率和转移.
- 开发的模型为癌管理中的早期干预策略提供了有价值的决策支持.
- 优化模型可以帮助临床医生识别高风险患者,从而改善整体预后.
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