Washington University School of Medicine, St Louis, MO, USA.
J Natl Cancer Inst. 2021 Nov 29;113(12):1620-1624. doi: 10.1093/jnci/djab074.
Cancer risk prediction models have the potential to revolutionize the science and practice of cancer prevention and control by identifying the likelihood that a patient will develop cancer at some point in the future, likely experience more benefit than harm from a given intervention, and survive their cancer for a certain number of years. The ability of risk prediction models to produce estimates that are valid and reliable for people from diverse socio-demographic backgrounds-and consequently their utility for broadening the reach of precision medicine to marginalized populations-depends on ensuring that the risk factors included in the model are represented as thoroughly and as accurately as possible. However, cancer risk prediction models created in the United States have a critical limitation, the origins of which stem from the country's earliest days: they either erroneously treat the social construct of race as an immutable biological factor (ie, they "essentialize" race), or they exclude from the model those socio-contextual factors that are associated with both race and health outcomes. Models that essentialize race and/or exclude socio-contextual factors sometimes incorporate "race corrections" that adjust a patient's risk estimate up or down based on their race. This commentary discusses the origins of race corrections, potential flaws with such corrections, and strategies for developing cohorts for developing risk prediction models that do not essentialize race or exclude key socio-contextual factors. Such models will help move the science of cancer prevention and control towards its goal of eliminating cancer disparities and achieving health equity.
癌症风险预测模型通过识别患者在未来某个时间点患癌症的可能性、确定他们从特定干预措施中获得的益处是否超过危害，以及在一定年限内生存的可能性，有可能彻底改变癌症预防和控制的科学和实践。风险预测模型能够为来自不同社会人口背景的人群提供有效且可靠的估计，从而为将精准医学的应用范围扩大到边缘化人群提供实用价值，这取决于确保模型中包含的风险因素尽可能全面和准确地体现出来。然而，美国创建的癌症风险预测模型存在一个关键的局限性，其根源可以追溯到这个国家的早期：这些模型要么错误地将种族这一社会建构视为不可改变的生物因素（即“本质主义”种族），要么将与种族和健康结果相关的社会背景因素排除在模型之外。将种族本质化和/或排除社会背景因素的模型有时会采用“种族修正”，根据患者的种族来调整其风险估计值的高低。本评论讨论了种族修正的起源、这些修正存在的潜在缺陷，以及开发不将种族本质化或排除关键社会背景因素的风险预测模型队列的策略。这些模型将有助于推动癌症预防和控制科学朝着消除癌症差异和实现健康公平的目标迈进。
