非转移性细胞癌的基于CTCs的复发模型:整合机器学习和SHAP解释
Geng Tian1, Fabrice Kayitare1, Xiaoyong Chen1
1Department of Urology, Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Biomarkers in medicine
|November 23, 2025
概括
预测细胞癌 (RCC) 复发是非常重要的. 这项研究发现,介质细胞和混合循环瘤细胞 (CTC) 的变化,以及瘤的阶段和大小,可以预测手术后的复发.
科学领域:
- 在瘤学瘤学.
- 生物标志物 生物标志物
- 机器学习 机器学习
背景情况:
- 手术后复发是细胞癌 (RCC) 的一个主要挑战.
- 循环瘤细胞 (CTC) 显示出预后潜力,但复发预测的亚型动态需要进一步研究.
研究的目的:
- 在RCC患者中分析CTC特征和亚型.
- 开发一种基于机器学习的预后模型,用于预测术后复发.
主要方法:
- 经过切除的124名RCC患者通过连续取血样进行监测.
- 使用标准化协议量化了CTC亚型 (上皮,混合,介质).
- 机器学习算法,包括随机森林,使用54个变量进行训练 (45个CTC,9个临床/病理).
- 用SHAP框架进行模型解释.
主要成果:
- 随机森林模型实现了0.84 (培训) 和0.77 (验证) 的AUC.
- 通过SHAP分析确定的关键预测因素包括pT阶段,瘤大小以及中和混合CTC计数的动态变化.
- 在41个月的中位数随访期间,在24名患者中观察到复发.
结论:
- 结合CTC亚型动态的机器学习模型可以有效预测RCC复发.
- 中和混合CTC的动态变化是复发的重要预测因素.
- 这种方法可能有助于对RCC患者进行个性化的术后管理.
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