对乳腺癌风险的预测模型进行系统审查
Federica Re1,2, Natnicha Manaboriboon3, Iwan G A Raza2
1Clinical Trial Service Unit & Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Old Road Campus, Oxford, OX3 7LF, UK.
BMC cancer
|October 28, 2025
概括
这次审查分析了107种乳腺癌 (BC) 风险预测模型. 结合人口,遗传或成像数据的模型在BC风险评估中表现出比单独的人口数据更好的表现.
科学领域:
- 在瘤学瘤学.
- 流行病学 流行病学
- 生物统计学 生物统计学
背景情况:
- 准确的乳腺癌 (BC) 风险预测对于早期诊断和预防至关重要.
- 目前的BC风险模型通常使用人口统计,遗传或成像数据.
- 需要进行系统审查,以评估现有的BC风险预测模型的性能.
研究的目的:
- 系统地审查和总结已开发的乳腺癌 (BC) 风险预测模型.
- 评估这些BC风险模型的区分能力和校准.
- 确定影响BC风险预测模型性能的因素.
主要方法:
- 在MEDLINE和Embase中搜索开发或验证BC风险模型的研究.
- 选了9511个标题和摘要,审查了360篇全文文章.
- 专注于研究开发新的BC风险预测模型,用于一般和高风险人群.
主要成果:
- 包括107项研究开发新的BC风险预测模型.
- 曲线下的面积 (AUC) 值在0.51到0.96.9之间.
- 校准 (观察/预期比) 从0.84到1.10不等,在18项研究中进行了外部验证.
结论:
- 大多数BC风险模型是在高加索人群中开发的,具有可变的预测性能.
- 结合人口和遗传数据或成像/活检数据的模型优于仅使用人口数据的模型.
- 添加多个数据类型并没有显著改善模型性能,除了将人口统计数据与遗传数据或成像数据相结合之外.
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