一个基于机器学习的模型,用于预测直筒形肌癌患者的存活率
Yifei Wang1, Bingbing Chen1, Jinhai Yu1
1Department of Gastric and Colorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, China.
PloS one
|March 25, 2025
概括
研究人员开发了一种XGBoost预测模型,用于直交结癌 (RSC) 的预后. 该模型确定了关键的风险因素,并帮助临床决策改善患者的生存率.
科学领域:
- 在瘤学瘤学.
- 手术瘤学手术瘤学
- 机器学习在医学中的应用
背景情况:
- 直腰结节具有独特的解剖学和血管特性,影响其功能和手术方法.
- 有限的研究存在于直交结癌 (RSC) 预后,需要有效的临床预测模型.
研究的目的:
- 为了确定直交结癌 (RSC) 存活率的独立风险因素.
- 开发和评估基于机器学习的预测模型,用于RSC预后.
- 确定RSC管理中临床决策支持的最佳模型.
主要方法:
- 对524名RSC患者 (2017-2019) 的回顾性分析.
- 单变量和多变量考克斯回归用于识别风险因素.
- 构建和评估六个机器学习模型,包括XGBoost.
- 使用AUC和Brier分数评估模型歧视,校准和临床效用.
主要成果:
- 确定了RSC存活的七个独立的风险因素:年龄,性别,糖尿病,瘤分化,N期,远程转移和道泄漏.
- 基于XGBoost的预测模型表现出卓越的性能,AUC为0.7856 (1年),0.8484 (3年) 和0.796 (5年).
- 与其他模型相比,XGBoost模型表现出最低的Brier分数和优越的临床决策益处.
结论:
- 开发了一种基于XGBoost的最佳预测模型,用于对直交结癌 (RSC) 的预后.
- 这个模型可以帮助RSC患者的临床决策.
- 该模型有可能改善被诊断患有RSC的个体的生存结果.
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