可解释的机器学习模型用于预测使用手术前实验室测试的第三阶段结肠癌患者的5年术后复发风险:一项两中心研究
Hangping Wei1,2, Xihao Fu1, Yuanyuan Cheng2
1Department of Medical Oncology, The First Affiliated Hospital of Soochow University, 899 Pinghai Road, Suzhou, Jiangsu, 215006, P.R. China.
BMC gastroenterology
|January 29, 2026
概括
使用手术前数据的机器学习模型可以预测第三阶段结肠癌 (CC) 患者的5年复发风险. 随机森林模型显示了最高的准确性,有助于个性化治疗策略.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 医疗信息学 医疗信息学
背景情况:
- 大肠直肠癌 (CRC) 是一种普遍存在的恶性瘤,具有显著的异质性.
- 第三期结肠癌 (CC) 患者面临术后复发的风险.
- 预测复发对于有效的治疗策略至关重要.
研究的目的:
- 应用机器学习 (ML) 算法来预测III期CC患者5年复发风险.
- 评估ML模型中手术前实验室测试和临床特征的有用性.
- 为了确定CC复发的关键预测因素.
主要方法:
- 使用了两个队列 (ZCC培训,DYC测试),共计415名III期CC患者.
- 单变量分析从48个实验室测试和15个临床/病理特征中确定了显著的预测因子 (p < 0.05).
- 六个ML模型 (逻辑回归,随机森林,XGBoost,SVM,BP NET,KNN) 被训练并使用测试组上的AUC进行验证.
- 用SHAP分析进行特征重要性排名.
主要成果:
- 为ML模型选择了十项实验室测试和六项临床/病理特征.
- 随机森林模型在外部测试队列中实现了最高的预测性能 (AUC = 0.845).
- 后勤回归也显示出强的表现 (AUC = 0.823),与随机森林相比.
- 经过SHAP分析,周围神经侵袭,FIB和PT被确定为复发的最重要的预测因素.
结论:
- 整合手术前数据的ML模型可以有效预测III期CC患者的5年复发风险.
- 开发的模型为个性化治疗规划提供了宝贵的见解.
- 这种方法支持临床决策,以优化患者的治疗结果.
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