开发和验证基于机器学习的诺米克图,用于预测患有恶性多发性溢液肺癌患者的预后
Xin Hu1, Shiqiao Zhao2, Yanlun Li3
1Department of Respiratory and Critical Care Medicine, Affiliated Hospital of North Sichuan Medical College, Nanchong, Sichuan, China.
Scientific reports
|March 21, 2025
概括
一种后勤回归 (LR) 模型有效预测肺癌患者患有恶性多叶流血 (MPE) 的预后. 这种模型,集成到一个名图中,有助于风险分层,以便及时干预和改善患者的治疗结果.
科学领域:
- 在瘤学瘤学.
- 肺部病理学 肺部病理学
- 医疗信息学 医疗信息学
背景情况:
- 恶性流 (MPE) 是晚期肺癌患者常见的,限制生命的并发症.
- 准确的预后评估工具对于及时干预和改善患者护理至关重要.
- 现有的预测模型可能无法充分利用MPE的先进机器学习技术.
研究的目的:
- 评估和比较9个机器学习模型的性能,用于预测MPE肺癌患者的预后.
- 为了确定MPE预后的关键预测因素.
- 为患者风险分层和临床决策制定一个强大的名图.
主要方法:
- 来自多个医院队列 (培训,内部测试和三个外部测试队列) 的患者数据的回顾性分析.
- 单变量和多变量逻辑回归 (LR) 来识别独立的预测因子.
- 开发和验证九个机器学习模型 (包括LR,DT,RF,XGBoost,Enet,rSVM,MLP,LightGBM,KNN) 和基于表现最好的模型的名ogram.
主要成果:
- 治疗方案,心周溢血的存在和肺溢血总量被确定为重要的预测因素.
- 在所有队列中,LR模型表现出优异的性能,曲线下的面积 (AUC) 值在0.885到0.962.
- 开发的名图表显示出出色的校准和临床实用性,有效地将患者分为不同的风险组,具有显著的生存差异.
结论:
- 基于LR的模型及其衍生名录为MPE肺癌患者的预后评估提供了可靠和准确的工具.
- 诺米图有助于个性化的风险量化,支持早期干预策略.
- 这项研究强调了机器学习,特别是LR在提高MPE患者的预后预测方面的有效性.
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