预测子宫内膜癌风险的模型:系统审查
Bea Harris Forder1, Anastasia Ardasheva1, Karyna Atha1
1Medical Sciences Division, University of Oxford, Oxford, UK.
Diagnostic and prognostic research
|February 3, 2025
概括
本综述总结了子宫内膜癌 (EC) 风险预测模型. 虽然许多模型显示中等到高的预测能力,但大多数具有高偏差风险,需要进一步验证.
科学领域:
- 妇科瘤学 妇科瘤学
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 子宫内膜癌 (EC) 是英国最常见的妇科癌症,发病率越来越高.
- 现有的EC风险预测模型在设置和预测时间框架方面有所不同.
- 本系统性审查评估了EC风险预测模型的特性和性能.
研究的目的:
- 系统地审查和总结现有的子宫内膜癌 (EC) 风险预测模型.
- 评估这些模型的特性,开发方法和预测性能.
- 评估已识别的EC风险预测模型的质量和适用性.
主要方法:
- 系统地搜索MEDLINE和Embase (OVID) 数据库,以进行EC风险预测和验证研究.
- 包括专注于预测未来EC诊断风险的论文.
- 提取研究特征,模型变量,开发方法和性能指标.
- 使用预测模型风险偏差评估工具评估模型质量.
主要成果:
- 在20项研究中确定了19个模型,按人口 (一般,高风险) 和更年期状态分类.
- 后勤回归是主要的开发方法.
- 一般人群中的三种模型表明偏差风险低,适用性高.
- 大多数模型表现出中等到高的预测能力 (AUC0.56-0.92).
- 希普斯利-科克斯和库普兰QCancer模型显示出高预测能力和良好的校准,偏差风险低.
结论:
- 几种EC风险预测模型具有中等到高的预测能力.
- 研究质量存在显著差异,大多数模型都表现出高偏差风险.
- 在大型,多样化的队列中对表现良好模型的外部验证至关重要,以确认它们的临床实用性.
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