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机器学习的应用用于预后建模,用于用化学放射治疗治疗的不可切除的胰腺癌

Wei Xiao¹, Binbin Yang², Shanbao Ke³

¹Department of Gastroenterology, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Zhengzhou, Henan, China.

Frontiers in oncology

|September 17, 2025

概括

使用XGBoost的人工智能 (AI) 模型准确地预测了接受化疗放射治疗 (CRT) 的不可切除胰腺癌患者的存活率. 这种人工智能工具有助于个性化风险评估和治疗规划,以改善患者的治疗结果.

科学领域:

在瘤学瘤学.
人工智能在医学中的应用
医疗信息学医疗信息学

背景情况:

不切除的胰腺癌患者尽管接受化学辐射疗法 (CRT),但结果不佳.
对于胰腺癌存活率的传统预后工具缺乏准确性.
在胰腺癌中需要改进生存预测方法.

研究的目的:

开发一种基于人工智能 (AI) 的模型,用于预测胰腺癌患者的整体存活率.
为了比较不同的机器学习模型的性能,用于生存预测.
确定这一患者队列中生存的关键预测因素.

主要方法:

对214名用CRT治疗的无法切除的胰腺癌患者的回顾性分析.
开发和比较五种生存预测模型:Cox,LASSO,RSF,SVM和XGBoost.
模型性能评估使用C指数,时间依赖ROC,校准和决策曲线分析;用SHAP评估特征重要性.

主要成果:

该XGBoost模型表现出卓越的性能,其C指数为0.949.
XGBoost在6个月和12个月 (0.751和0.732) 实现了更高的AUC,并显示出更好的校准和临床效用.
通过SHAP分析确定的关键预测因素包括CA199,瘤大小,血小板数和年龄,将患者分为不同的风险组.

结论:

一个基于AI的XGBoost模型准确地预测了接受CRT的不可切割的胰腺癌患者的存活率.
这种人工智能模型为个性化风险分层和治疗规划提供了有价值的工具.
改善生存预测可以为胰腺癌患者提供更为定制的治疗策略.

关键词:

人工智能的人工智能是人工智能.化学疗法或放射疗法模型模型模型模型模型模型胰腺癌是一种癌症.幸存率幸存率生存率

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