一个基于机器学习的模型,用于临床预测冠状腺癌的远端转移:一个多中心的,回顾性研究
Jihu Wei1, Shijin Lu2, Wencai Liu3
1Faculty of Postgraduate, Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
PeerJ
|December 22, 2023
概括
这项研究确定了冠状腺癌 (CS) 远程转移的关键因素,并开发了一种机器学习模型来进行风险分层. XGBoost模型准确预测转移风险,有助于临床决策和患者监测.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 远程转移 (DM) 显著限制了冠状动脉瘤 (CS) 患者的整体存活期 (OS).
- 早期诊断和治疗CS仍然具有挑战性.
- 识别转移性因素对于改善患者的治疗结果至关重要.
研究的目的:
- 调查与CS中DM相关的因素.
- 开发一个风险分层模型来预测DM.
- 帮助临床医生为CS患者做出决策.
主要方法:
- 评估了六种机器学习 (ML) 算法:逻辑回归 (LR),天真贝叶斯分类器 (NBC),决策树 (DT),随机森林 (RF),梯度增强机 (GBM) 和极端梯度增强 (XGBoost).
- 进行了10倍的交叉验证,并使用多中心数据进行外部验证.
- 选择了表现最好的模型 (曲线下的最高面积 - AUC) 来构建在线风险计算器.
主要成果:
- 总共有1,385名患者被纳入,其中5.9%患有转移性CS.
- 多变量分析确定了较高的病理等级,T阶段,N阶段,非左侧初级病变,以及缺乏手术/化疗作为DM的显著风险因素.
- ML算法实现了从0.911-0.985的AUC,XGBoost证明了最高的预测准确性.
结论:
- CS,T阶段,N阶段,横向性和手术/化疗的分级是DM的独立风险因素.
- 基于随时可用的临床因素的XGBoost模型有效预测DM风险.
- 在线风险计算器有助于患者评估,支持精准医学,并指导长期癌症监测.
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