使用系统性炎症营养指数 (SINTI) 进行局部晚期直肠癌早期术后复发的可解释AI驱动的预测:一项多中心研究和临床网络工具
Zhen Pan1, Tengyi Peng1, Ye Wang1
1Department of Colorectal Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China.
International journal of colorectal disease
|February 5, 2026
概括
使用系统性炎症-营养瘤生物标志物指数 (SINTI) 的可解释AI模型准确地预测局部晚期直肠癌 (LARC) 患者的早期复发后的新辅助化学放射治疗 (NCRT). 这种工具有助于临床决策,以获得更好的患者结果.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 生物标志物发现发现
背景情况:
- 接受新辅助化学放射治疗 (NCRT) 的局部晚期直肠癌 (LARC) 患者如果在两年内出现早期复发 (ER),预后不佳.
- 准确预测ER对于改善LARC患者的治疗结果至关重要.
研究的目的:
- 开发和验证一种可解释的人工智能 (AI) 模型,用于预测LARC患者的ER.
- 该模型基于系统性炎症-营养瘤生物标志物指数 (SINTI),该指数来自常规血液检测.
主要方法:
- 对两个队列 (n=715和n=224) 进行了多中心的回顾性分析.
- 使用LASSO调节的特征选择确定了SINTI的关键生物标志物.
- 评估了10个机器学习架构,通过随机搜索和交叉验证进行超参数优化.
- 使用SHAP价值分析实现了模型的可解释性.
主要成果:
- 六个关键生物标志物形成了SINTI模型,该模型与临床阶段和瘤大小一起,独立地预测了ER.
- 在交叉验证过程中,XGBoost算法实现了0.860的平均AUC,外部验证的AUC为0.840.
- SINTI对该模型的预测权重贡献了36.1%,而SHAP值显示了与复发风险的单调关系.
结论:
- 使用SINTI的可解释AI框架显示了预测LARC患者NCRT后复发风险的前景.
- 该模型为临床决策支持提供了一个透明且可能部署的解决方案.
- 在广泛临床实施之前,建议进一步进行前性验证.
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