结直肠癌风险预测工具的开发和验证:模型的比较
Duco T Mülder1, Rosita van den Puttelaar1, Reinier G S Meester2
1Department of Public Health, Erasmus Medical Center, Rotterdam, Netherlands.
International journal of medical informatics
|August 26, 2023
概括
后勤回归模型有效预测查程序中的结直肠癌风险. 虽然随机森林模型没有改善预测,但物流回归仍然是风险分层的可靠工具.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 预防医学 预防医学
背景情况:
- 风险分层增强了癌症查的有效性,通过允许量身定制的查间隔.
- 一个利用性别,年龄和先前便血红蛋白度的先前预后模型预测了结直肠癌 (CRC) 风险,先进瘤 (AN) 的AUC为0.78.
研究的目的:
- 验证现有的逻辑回归 (LR) 模型,用于预测结直肠癌 (CRC) 查中的晚期瘤 (AN).
- 为了评估机器学习方法,特别是随机森林 (RF),是否可以提高风险预测准确性,而不是建立的LR模型.
主要方法:
- 在荷兰CRC查计划中的219257名参与者的数据上训练了LR和RF模型.
- 验证模型使用来自1,137,599名参与者 (2018年后) 和192,793名参与者 (2020年以后) 的样本外数据.
- 评估模型性能使用接收器运行特征曲线 (AUC) 下的面积和AN和CRC的相对风险.
主要成果:
- 在第一个验证集 (2018年后) 中,AN预测的AUC为LR和RF的0.77.
- 在第二个验证组 (2020年以后) 中,AN预测的AUC为LR和RF的0.73.
- 预测风险最高的5%的风险组显示了7倍的AN风险增加;最低的80%的风险低于平均水平.
结论:
- LR模型是基于便的CRC查计划的验证和有效的风险预测工具.
- 虽然预测性能稍微下降在后一轮,LR模型保持其预测能力.
- 随机森林的表现并没有超过LR,这可能是由于有限的解释变量,由于其可解释性而加强了LR的实用性.
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