目前，结直肠癌筛查建议采用以年龄为主要标准进行单一适用的方法。而将除年龄外的因素纳入风险分层的精准筛查能够提高目前策略的水平，并最优化利用可用资源，为患者、医疗服务提供者和保健系统带来好处。预测模型可以识别高风险人群并为他们提供更加深入的筛查，低风险人群则可建议采用非侵入式筛查方式进行较少次数的筛查。除了年龄，预测模型还包括确定的风险因素，例如遗传因素（例如家族结直肠癌史，生殖系和多基因风险评分）、生活方式（例如吸烟、酗酒、饮食不当、体力不足）、性别、种族和族裔等。虽然已经验证了多种风险预测模型，但很少有经过系统研究以进行风险适应性的群体结直肠癌筛查。为了在未来构想精准筛查的临床实施，需要开发适用于整个人群的可靠且准确的预测模型，对群体进行前瞻性的风险适应性CRC筛查研究，赢得患者和医疗服务提供者的接受，并评估这些新范式的可行性、资源、成本和成本效益。本文评估了风险预测模型的当前状态，并提供未来实施精准筛查的路线图。 版权所有 © 2023 AGA Institute. 由Elsevier Inc.发表。保留所有权利。

Current colorectal cancer (CRC) screening recommendations take a one-size-fits-all approach using age as the major criterion to initiate screening. Precision screening that incorporates factors beyond age to risk stratify individuals could improve upon current approaches and optimally utilize available resources with benefits for patients, providers, and healthcare systems. Prediction models could identify high-risk groups who would benefit from more intensive screening, while low-risk groups could be recommended less intensive screening incorporating non-invasive screening modalities. In addition to age, prediction models incorporate well-established risk factors such as genetics (e.g., family CRC history, germline, and polygenic risk scores), lifestyle (e.g., smoking, alcohol, diet, physical inactivity), sex, race and ethnicity among others. While several risk prediction models have been validated, few have been systematically studied for risk-adapted population CRC screening. In order to envisage clinical implementation of precision screening in the future, it will be critical to develop reliable and accurate prediction models that apply to all individuals in a population, prospectively study risk-adapted CRC screening on the population level, garner acceptance from patients and providers, and assess feasibility, resources, cost, and cost-effectiveness of these new paradigms. This review evaluates the current state of risk prediction modeling and provides a roadmap for future implementation of precision CRC screening.Copyright © 2023 AGA Institute. Published by Elsevier Inc. All rights reserved.