Modeling Breast Carcinoma In Situ: Implications for Prevention and Control

描述(申请人提供)：包括原位疾病在内的早期乳腺癌的发病率在过去十年中迅速增加，这主要是由于筛查乳房X光检查的使用越来越多。存在对早期乳腺癌过度诊断的可能性的担忧。我们对原位疾病发展为浸润性乳腺癌的自然历史和进展的了解是不完整的。很少有基于人群的模型来探索关于原位疾病自然病史的假说。这项研究提出了一系列分析，旨在加深我们在这方面的理解，并为预防和筛查策略提供信息，以控制乳腺癌。我们的第一个目标是扩展现有的美国乳腺癌基于人群的离散事件模拟模型，以解释关于原位疾病的更详细的信息。我们将用观察数据来评估关于乳腺癌的自然病史和进展的假设的一致性。然后，该模型将被用来研究与不同预防和控制战略相关的个人和人群层面的健康权衡。具体来说，对于这笔赠款，我们建议估计与增加筛查相关的早期乳腺癌过度诊断和随后过度治疗的可能性。这项工作的结果将是一个量化建模平台，有助于在两个层面上为乳腺癌预防和控制决策提供信息：个人面临筛查和治疗决策，以及社会制定政策和指导资源分配。这项拟议的研究为乳腺癌的临床和政策问题带来了决策分析方法的优势。我们的方法和分析也适用于其他类型的癌症。

DESCRIPTION (provided by applicant): The incidence of early stage breast cancer including in situ disease has increased rapidly over the past decade primarily due to increasing use of screening mammography. Concern exists regarding the potential for over-diagnosis of early stage breast cancer. Our understanding of the natural history and progression of in situ disease to invasive breast cancer is incomplete. Few population-based models exist to explore hypotheses about the natural history of in situ disease. This research proposes a series of analyses designed to further our understanding in this regard and to inform decision making about prevention and screening strategies to control breast cancer. Our first aim is to extend an existing U.S. population-based discrete-event simulation model of breast cancer to account for more detailed information about in situ disease. We will evaluate the consistency of hypotheses about the natural history and progression of breast carcinoma in situ with observed data. This model will then be used to examine health trade-offs at both an individual and population level associated with different prevention and control strategies. For this grant specifically, we propose to estimate the potential for over-diagnosis and subsequent over-treatment of early stage breast cancer that is associated with increased screening. The result of this work will be a quantitative modeling platform useful in informing decision-making about breast cancer prevention and control at two levels: an individual facing screening and treatment decisions, and society setting policy and guiding resource allocation. The proposed research brings the strengths of a decision-analytic approach to clinical and policy questions in breast cancer. Our methodologies and analyses have relevance for other types of cancer.