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用可解释机器学习对胰腺癌患者进行个性化三年生存预测和预后预测:基于人口的研究和外部验证

Buwei Teng¹, Xiaofeng Zhang¹, Mingshu Ge¹

¹Department of Hepatobiliary Surgery, The Affiliated Lianyungang Hospital of Xuzhou Medical University/The First People's Hospital of Lianyungang, Lianyungang, China.

Frontiers in oncology

|November 5, 2024

概括

机器学习模型准确地预测胰腺癌患者的三年生存期和预后. 这些模型提供了个性化的预测,改善了患者的护理和结果.

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:

胰腺癌的整体生存率非常低.
准确预测生存和预后对于患者管理至关重要.

研究的目的:

开发和验证机器学习 (ML) 模型,用于预测胰腺癌患者的三年生存期和预后.
通过使用ML解释性技术,识别影响患者生存的关键因素.

主要方法:

从监测,流行病学和最终结果 (SEER) 数据库 (2000-2021) 中对20 064名胰腺癌患者的分析.
使用六个ML算法的递归特征消除 (RFE) 进行特征选择.
评估13个ML算法用于预测性能,使用AUC,准确性和灵敏度等指标.
使用101ML算法组合的预测模型开发,通过C-index进行评估.

主要成果:

CatBoost模型实现了三年生存期的高预测准确性 (AUC在训练中为0.932,在内部测试中为0.899,在外部测试中为0.826).
手术类型被确定为影响三年生存率的最重要因素,通过SHapley添加式扩展 (SHAP).
"RSF+GBM"算法在预后预测方面表现最好 (在训练中C指数为0.774).

结论:

开发的ML模型在预测胰腺癌患者的结果方面表现出卓越的准确性和可靠性.
这些模型为更精确,个性化的预后预测提供了基础.
这些发现可以帮助临床决策和胰腺癌患者咨询.

关键词:

塞尔·塞尔 (Seer) 是一个看得见的人.机器学习是机器学习.胰腺癌是一种癌症.预后预测预测预测三年生存率三年生存率

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