The comparative effectiveness of incorporating novel DClS prognostic markers into

几乎20%的乳腺癌诊断和近30%的筛查发现的乳腺癌是DCIS。自.以来 我们对DCIS自然病史的了解的局限性阻碍了对哪些DCIS肿瘤的识别 进展为浸润性癌症，DCIS的治疗需要类似于侵袭性癌症的治疗 乳腺癌，尽管DCIS后的相对存活率接近100%。研究人员正在积极寻找 有关通过识别预后标记物来识别DCIS以优化筛查过程的方法 可能是恶性的。我们的目标是(1)比较当前的筛查流程与全面的、个性化的 考虑DCIS预后标志物的乳腺癌筛查过程，例如正在研究的那些 项目1和2。我们的进一步目标是(2)进行分组分析，以确定如何使用新的DCI 预后标记物影响筛查具有不同DCIS比率的妇女的益处和危害(例如，通过 年龄和种族)，以及(3)评估数字乳房X光检查和MRI使用的增加对DCIS的影响 新的DCIS预后标记物的发生率、过度治疗和比较有效性。致信地址 这些目标，我们将使用威斯康星大学乳腺癌模拟(UWBCS)模型来检验 在人口层面上的相对有效性。UWBCS模型，作为癌症的一部分开发 入侵/监视建模网络(CISNET)是一种离散事件、随机模拟模型 旨在复制美国人口中的乳腺癌发病率和死亡率。数据来自 佛蒙特州乳腺癌监测系统和其他来源，包括威斯康星州原位队列，将 为该项目的UWBCS模型提供必要的新投入。好处的多个衡量标准和 将评估与乳腺癌筛查相关的危害。仿真建模非常适合于 比较有效性因为可以同时考虑多个筛选过程变量， 可以组合数据来源以弥补差距，并可以及时评估长期成果。 我们的比较有效性分析将提供一个框架，通过这个框架，新的预后指标可以 评估其对筛查的益处和危害的潜在影响，重点是那些乳房 预后良好的癌症诊断，主要是通过筛查才能发现的。这个项目将 解决评估新的个性化治疗决策方法是否与 新出现的筛查测试可以通过避免在所有人群中过度治疗来最大限度地提高生活质量。

almost 20% of breast cancer diagnoses, and neariy 30% of screen-detected breast cancers, are DCIS. Since limitations in our understanding ofthe natural history of DCIS prevent identification ofwhich DCIS tumors will progress into invasive cancers, the management of DCIS requires treatment similar to therapies for Invasive breast cancer even though relative survival after DCIS approaches 100%. Researchers are actively searching for methods to optimize the screening process by identifying prognostic markers to identify DCIS with malignant potential. We aim to (1) compare current screening processes with a comprehensive, personalized breast cancer screening process that considers DCIS prognostic markers such as those under investigation in Projects 1 and 2. We further aim to (2) perform subgroup analyses to determine how the use of new DCIS prognostic markers affects the benefits and harms of screening for women with varying rates of DCIS (e.g., by age and race), and to (3) evaluate the impact of increasing digital mammography and MRI use on DCIS incidence, overtreatment, and the comparative effectiveness of new DCIS prognostic markers. To address these aims, we will use the University of Wisconsin Breast Cancer Simulation (UWBCS) model to examine comparative effecfiveness at the population level. The UWBCS model, developed as part of the Cancer Inten/ention and Surveillance Modeling Network (CISNET), Is a discrete-event, stochastic simulation model designed to replicate breast cancer incidence and mortality rates in the U.S. population. Data from the Vermont Breast Cancer Surveillance System and other sources, including the Wisconsin In Situ Cohort, will provide essential new inputs to the UWBCS model for this project. Multiple measures of the benefits and harms associated with breast cancer screening will be evaluated. Simulation modeling is ideally suited for comparative effectiveness since numerous screening process variables can be considered simultaneously, data sources can be combined to address gaps, and long term outcomes can be evaluated in a timely manner. Our comparative effectiveness analysis will provide a framework by which new prognostic markers can be evaluated for their potential impacts on the benefits and harms of screening, with a focus on those breast cancer diagnoses with excellent prognosis that are primarily only found through screening. This project will address a critical need to assess whether novel new personalized treatment decision-making approaches tied to emerging screening tests can maximize quality of life by avoiding overtreatment in all populations.