基于5年临床随访数据构建结直肠癌的预后预测模型
Boao Xiao1,2, Min Yang1,2, Yao Meng1,2
1School of Public Health, Xuzhou Medical University, Xuzhou, 221004, Jiangsu, China.
Scientific reports
|January 21, 2025
概括
一个新的机器学习模型使用临床数据准确预测结直肠癌 (CRC) 患者的预后. 该工具可以识别高风险个体,帮助制定个性化治疗策略,以获得更好的结果.
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:
- 结肠直肠癌 (CRC) 是一个重大的公共卫生挑战.
- 准确的预后预测对于有效的患者管理和治疗计划至关重要.
研究的目的:
- 开发和验证用于预测结直肠癌患者预后的机器学习模型.
- 确定与CRC患者预后不佳相关的关键临床变量.
主要方法:
- 利用了411名结肠直肠癌患者的数据集,以及五年的随访数据.
- 采用递归特征消除 (RFE) 来选择决策树,随机森林,支持矢量机和极端梯度增强 (XGBoost) 模型的特征.
- 使用网格搜索优化超参数,并通过多个指标评估模型性能,包括精度,F1得分和ROC曲线.
- 使用夏普利添加式扩展 (SHAP) 解释了最佳模型.
主要成果:
- 该RFE-XGBoost模型实现了高性能:0.83准确度,0.81F1得分和0.89AUC.
- 该模型展示了优越的校准和临床实用性.
- SHAP分析确定了术后输血,AJCC阶段,CEA,CA19-9,年龄和CA125作为死亡风险的重要预测因素.
结论:
- 该RFE-XGBoost模型有效预测结直肠癌患者的预后.
- SHAP分析提高了预测模型的可解释性和临床可信度.
- 这种方法为识别高风险CRC患者提供了有价值的工具,有可能改善临床决策.
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