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ColoLDB:基于机器学习的结直肠癌预测模型,使用常规实验室参数进行预测

Xing Zhang¹, Xuedong Tong¹, Jiangtao Mou¹

¹Department of Laboratory Medicine, The Third Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Journal of gastrointestinal oncology

|March 12, 2026

概括

这项研究开发了一种新的结直肠癌 (CRC) 查工具,使用八种实验室标记物. 与现有方法相比,用随机森林构建的ColoLDB模型在检测CRC方面显示出更高的准确性.

科学领域:

在瘤学瘤学.
生物标志物生物标志物
医疗保健中的机器学习

背景情况:

结肠直肠癌 (CRC) 是一个普遍存在的全球健康问题.
目前的查方法,如结肠镜检查是侵入性的,可以错过早期瘤.
需要更简单,更容易获得的CRC早期检测方法.

研究的目的:

为早期检测结直肠癌 (CRC) 开发一种非侵入性查方法.
确定可表明CRC风险的关键实验室参数.
为帮助临床医生在CRC诊断中创建一个预测模型.

主要方法:

使用住院号码进行数据识别,并排除无效记录.
收集了各种实验室测试数据,包括瘤标志物和生化参数.
应用机器学习模型 (LightGBM,LR,RF,XGBoost) 和SHAP用于解释.

主要成果:

确定了八个关键的实验室参数:SG,CA19-9,CEA,年龄,ALB,CYFRA21-1,HDL-C和CA72-4.4
随机森林 (RF) 模型实现了0.863的AUC,证明了高诊断性能.
开发的ColoLDB模型表现优于仅使用CEA和CA19-9的诊断模型.

结论:

八个实验室指标与CRC风险有关.
基于射频的ColoLDB模型是预测CRC发生的有效工具.
这种新的方法提高了诊断效率,并显示了CRC查的前景.

关键词:

大肠直肠癌 (CRC) 是一种癌症.结肠直肠实验室数字生物标记模型 (ColoLDB模型)实验室参数实验室参数机器学习是机器学习.

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