PRESCO:一种在线工具,用于预测癌症手术后严重的肺部并发症和生存率
Frontiers in oncology
|January 23, 2026
概括
机器学习模型可以准确预测癌症手术患者的严重肺部并发症 (SPC) 和生存率. 在线工具PRESCO帮助临床医生进行风险分层和术后决策,以改善瘤治疗.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 肺部病理学 肺部病理学
背景情况:
- 严重的肺部并发症 (SPCs) 在癌症手术患者的发病率和死亡率显著增加.
- 目前用于预测SPC发生和生存结果的工具缺乏准确性和实用性.
- 解决这个差距对于改善患者的治疗结果和管理医疗保健负担至关重要.
研究的目的:
- 开发和验证用于预测癌症手术后SPC发生的机器学习模型.
- 创建模型来预测28天和90天的生存结果在癌症患者的SPCs.
- 将这些预测模型部署为可访问的在线工具,用于临床使用.
主要方法:
- 进行了一项回顾性队列研究,涉及434名癌症手术患者.
- 通过使用网格搜索和交叉验证开发和优化了六个监督机器学习分类器.
- 模型性能使用AUC,布里尔得分,精度,回忆和F1得分进行评估,并使用SHAP进行解释.
主要成果:
- 极端随机树 (EXT) 模型显示了SPC发生率 (AUC=0.813) 和28天死亡率 (AUC=0.921) 的最佳表现.
- 随机森林 (RDF) 模型在预测90天死亡率方面表现出色 (AUC=0.899).
- 关键预测因素包括手术前高血压,ECOG评分,手术期间流血,SOFA,APACHE II评分和BUN.
结论:
- 经过验证的机器学习模型准确地预测癌症后手术患者的SPC发生和存活率.
- 普雷斯科在线工具可以实时预测,促进个性化的风险分层.
- 这些工具的临床实施可以指导瘤学的术后决策,从而有可能改善患者的护理.
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