种族和民族对高级乳腺癌风险预测模型性能预测模型的影响
Karla Kerlikowske1,2, Shuai Chen3, Brian L Sprague4
1Departments of Medicine and Epidemiology and Biostatistics, University of California, San Francisco, CA, USA. Karla.Kerlikowske@ucsf.edu.
NPJ digital medicine
|December 14, 2025
概括
从乳腺癌风险模型中删除种族可能会导致偏见. 这种方法高估了亚洲女性的风险,低估了黑人女性的风险,这可能会影响查策略和结果.
科学领域:
- 医疗信息学 医疗信息学
- 在瘤学瘤学.
- 健康差异 在健康上的差异
背景情况:
- 医疗保健中的算法偏见越来越令人担忧,特别是在风险预测模型中.
- 种族和族裔经常被纳入模型,但建议将它们排除在外,以减轻偏见.
- 乳腺癌监测联盟 (BCSC) 风险模型用于查决策.
研究的目的:
- 评估消除种族和种族对BCSC高级乳腺癌风险模型的影响.
- 评估由种族天真预测模型引入的潜在算法偏差.
主要方法:
- 分析了BCSC6年的累积先进乳腺癌风险模型.
- 模型的性能与不包括种族和种族变量进行了比较.
- 预期/观察到的风险比率和分类变化被计算为不同的种族群体.
主要成果:
- 排除种族和种族导致亚洲女性的风险高估 (E/O=1.28) 和黑人女性的风险低估 (E/O=0.61).
- 被归类为中等/高风险的亚洲女性比例从6.1%增加到16.7%.
- 被归类为中等/高风险的黑人女性比例从75.3%下降到47.5%,表明校准较差.
结论:
- 种族天真预测模型可以引入或加剧偏见,导致不准确的风险评估.
- 从这个模型中排除种族和种族,会对黑人女性的校准产生负面影响.
- 这可能导致低于最佳的查策略,可能会增加晚期乳腺癌诊断和死亡率.
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