Comparative Modeling of Effective Policies for Colorectal Cancer Control

结直肠癌控制有效政策的比较模型

• 批准号: 10471354
• 负责人: KAREN M KUNTZ
• 金额: $ 113.31万
• 依托单位: SLOAN-KETTERING INST CAN RESEARCH
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-08 至 2025-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10471354
• 关键词: Address; Age; American Cancer Society; Area; Calibration; Cancer Control; Cancer Etiology; Cessation of life; Clinical; Collaborations; Colonoscopy; Colorectal Cancer; Country; Data; Diagnosis; Effectiveness; Goals; Health Policy; High Performance Computing; Incidence; Income; Intervention; Knowledge; Literature; Methods; Modality; Modeling; Natural History; New Hampshire; Outcome; Pathway interactions; Pattern; Persons; Policies; Population; Population Research; Positioning Attribute; Regimen; Registries; Research; Resources; Role; Seminal; United States; Update; Work; base; carcinogenesis; colorectal cancer risk; comparative; computer aided detection; deep learning; experience; improved; insight; learning strategy; novel; personalized medicine; personalized screening; population based; prevent; screening; screening program

ABSTRACT Colorectal cancer (CRC) is the second leading cause of cancer death in the United States. The long-term goal of our proposed project is to reduce the population burden of CRC by providing the information needed to address key policy questions across the CRC control continuum in an accessible and transparent manner. To accomplish this goal we will use our population-based microsimulation models to: 1) Evaluate the impact of screening as practiced in the US; 2) Inform the debate about the increase in CRC incidence before age 50; 3) Consider the effectiveness of precision of screening and surveillance; 4) Address other emerging issues and opportunities in CRC control; and 5) Use novel methods to improve model accessibility and transparency. Our team will fill critical gaps in knowledge, enabling decision makers to act. New evidence that we will incorporate in our models to better inform CRC control opportunities will be 1) updated information on screening patterns in the US (in collaboration with the Population-based Research Optimizing Screening through Personalized Regimen, or PROSPR), 2) data on the increased risk of CRC in persons under age 50 (in collaboration with Rebecca Siegal of the American Cancer Society, who did the seminal work in this area), and 3) state-of-the art colonoscopy screening data to incorporate alternative carcinogenesis pathways in the natural history models (in collaboration with the New Hampshire Colonoscopy Registry). We will synthesize and incorporate the growing body of evidence in the literature to assess the clinical utility of personalized screening and treatment, as well as the potential role for novel computer-aided detection and diagnosis modalities. We will expand our models to project clinical and resource-based outcomes for middle-income countries that are considering the implementation of a screening program. Lastly, there is a critical need to make our models assessible and transparent. To this end we will use high performance computing approaches to develop and apply deep- learning methods for model calibration and model emulation, which will aid in model sharing. The three participating modeling groups are well positioned to carry out this work, bringing a wealth of experience, expertise, and insight to issues related to microsimulation modeling of CRC, and have a proven track record of collaboration and disseminating our work to health policy decision makers.

摘要 结直肠癌（CRC）是美国癌症死亡的第二大原因。远景目标 我们建议的项目的目的是通过提供所需的信息来减轻CRC的人口负担， 以可访问和透明的方式解决CRC控制连续体中的关键政策问题。到 为了实现这一目标，我们将使用我们基于人口的微观模拟模型：1）评估 美国实践中的筛查; 2）告知有关50岁之前CRC发病率增加的争论; 3） 考虑筛查和监测精确性的有效性; 4）解决其他新出现的问题， CRC控制中的机会;以及5）使用新颖的方法来提高模型的可访问性和透明度。我们 团队将填补知识方面的关键空白，使决策者能够采取行动。我们将纳入的新证据 在我们的模型中，为了更好地告知CRC控制机会，将1）更新有关筛查模式的信息， 美国（与基于人口的研究合作，通过个性化 方案，或PROSPR），2）关于50岁以下人群CRC风险增加的数据（与 美国癌症协会的丽贝卡·西格尔在这一领域做了开创性的工作），以及3）最先进的技术 结肠镜筛查数据，以将替代致癌途径纳入自然史模型 (in与新罕布什尔州结肠镜登记处合作）。我们将综合并纳入 文献中越来越多的证据评估了个性化筛查和治疗的临床效用， 以及新型计算机辅助检测和诊断模式的潜在作用。我们将扩大我们 模型，以预测中等收入国家的临床和基于资源的结果， 实施筛查计划。最后，迫切需要使我们的模型可评估， 透明.为此，我们将使用高性能计算方法来开发和应用深度- 学习模型校准和模型仿真的方法，这将有助于模型共享。三 参赛的模特儿团体都有很好的条件来进行这项工作，带来丰富的经验， 专业知识和对CRC微观仿真建模相关问题的洞察力，并拥有以下方面的良好记录： 合作并向卫生政策决策者宣传我们的工作。