在术后胃癌患者中,XGBoost和后勤回归用于预测肉症的比较研究
Yajing Gu1, Shu Su1, Xianping Wang2
1Department of Urology, The Affiliated Jiangning Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
Scientific reports
|April 14, 2025
概括
机器学习,特别是XGBoost,在术后胃癌患者中有效预测了肉症. 关键预测因素包括糖尿病,营养评分和血清白蛋白,有助于早期干预和改善结果.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 老年病的医生 老年病的医生
背景情况:
- 肉,肌肉质量和力量的损失,显著恶化了术后胃癌患者的结果.
- Sarcopenia 的早期检测和干预对于减少术后并发症,发病率和死亡率至关重要.
研究的目的:
- 开发和验证一个基于机器学习的风险预测模型,用于胃切除术的胃癌患者的肉病.
- 为了比较XGBoost的预测性能和术后肉症的后勤回归模型.
主要方法:
- 对231名经过胃切除术的胃癌患者进行了回顾性分析.
- 开发XGBoost和后勤回归模型,利用临床数据预测肉症.
- 使用曲线下的面积 (AUC),灵敏度和特异性评估模型性能.
- 使用SHAP (夏普利添加式扩展) 来实现模型的可解释性.
主要成果:
- 在55.4%的被纳入患者中发生了萨科佩尼亚.
- 与物流回归 (AUC=0.918) 相比,XGBoost模型显示出优异的预测性能 (AUC=0.987).
- 确定的主要预测因素是糖尿病,营养评分,血清白蛋白,ECOG性能状态和操作风格,糖尿病和营养评分是最具影响力的.
结论:
- 基于XGBoost的预测模型提供了一个有价值的工具,用于在术后胃癌患者中早期临床查和介入肉症.
- 机器学习模型提供了更高的准确性和可解释性,用于预测复杂的术后并发症,如肉症.
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