在多因素乳腺癌风险预测中的缺失数据的个体级不确定性建模
Bethan L White1, Lorenzo Ficorella1, Xin Yang1
1Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom.
JCO precision oncology
|February 6, 2026
概括
缺少乳腺癌风险数据造成了不确定性. 收集更多的信息,比如遗传数据,可以显著提高风险预测准确度,以便做出更好的临床决策.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 遗传学 遗传学 是一个
背景情况:
- 多因素乳腺癌 (BC) 风险模型对于个性化风险评估至关重要.
- 不完整的风险因素数据给BC风险预测带来了显著的不确定性.
- 准确量化这种不确定性对于有效的风险沟通和临床决策至关重要.
研究的目的:
- 量化10年BC风险估计中的不确定性,用于缺少风险因素数据的个人.
- 开发和应用一个框架来估计风险不确定性分布和重新分类概率.
- 确定缺少数据对BC风险分层的影响.
主要方法:
- 使用BOADICEA模型的蒙特卡洛模拟方法来估计BC风险分布.
- 采用链式方程的多变量归算,使用大型参考数据集来处理缺失的共变量.
- 开发了一个框架来计算不确定性间隔 (UI) 和对具有不完整数据的个人重新分类的概率.
主要成果:
- 不完整的风险因素数据导致BC风险估计存在相当大的不确定性,其中95%的UI涵盖了所有风险类别.
- 中等风险的妇女,特别是那些有家族病史或致病变体的妇女,表现出高的重新分类概率 (高达57.5%).
- 随着额外的数据,特别是遗传信息和乳房扫描密度,风险确定性得到了大幅改善.
结论:
- 缺少的数据可能导致风险重新分类的很大概率,影响临床决策.
- 提出的方法有效地识别了额外数据收集最有利的情况.
- 通过数据收集改进的风险分层支持在乳腺癌风险评估中做出更明智的临床决策.
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