The comparative effectiveness of incorporating novel DClS prognostic markers into

项目摘要 在广泛的筛查性乳腺X线检查之前，导管原位癌（DCIS）是一种罕见的诊断。现在，近20%的乳腺癌诊断和近30%的筛查乳腺癌是DCIS。由于我们对DCIS自然史的理解有限，无法确定哪些DCIS肿瘤将进展为浸润性癌症，因此DCIS的管理需要与浸润性乳腺癌相似的治疗，即使DCIS后的相对生存率接近100%。研究人员正在积极寻找通过识别预后标志物来优化筛选过程的方法，以识别具有恶性潜能的DCIS。我们的目标是（1）比较目前的筛查过程与全面的，个性化的乳腺癌筛查过程，考虑DCIS预后标志物，如项目1和2中正在研究的标志物。我们进一步的目标是（2）进行亚组分析，以确定新的DCIS预后标志物的使用如何影响DCIS发生率不同的女性筛查的益处和危害（例如，（3）评估数字乳腺X线摄影和MRI使用的增加对DCIS发病率、过度治疗以及新DCIS预后标志物的比较有效性的影响。为了实现这些目标，我们将使用威斯康星州大学的乳腺癌模拟（UWBCS）模型来检查在人群水平上的比较有效性。UWBCS模型是作为癌症干预和监测建模网络（CISNET）的一部分开发的，是一种离散事件随机模拟模型，旨在复制美国人群中的乳腺癌发病率和死亡率。来自佛蒙特州乳腺癌监测系统和其他来源的数据，包括威斯康星州原位队列，将为本项目的UWBCS模型提供重要的新输入。将评估与乳腺癌筛查相关的益处和危害的多项措施。模拟建模非常适合比较有效性，因为可以同时考虑许多筛选过程变量，可以组合数据源以解决差距，并且可以及时评估长期结果。 我们的比较有效性分析将提供一个框架，通过该框架可以评估新的预后标志物对筛查的益处和危害的潜在影响，重点关注那些主要通过筛查发现的预后良好的乳腺癌诊断。该项目将解决一个关键需求，即评估与新兴筛查测试相关的新型个性化治疗决策方法是否可以通过避免所有人群的过度治疗来最大限度地提高生活质量。

PROJECT SUMMARY Prior to widespread screening mammography, ductal carcinoma in situ (DCIS) was a rare diagnosis. Now almost 20% of breast cancer diagnoses, and nearly 30% of screen-detected breast cancers, are DCIS. Since limitations in our understanding of the natural history of DCIS prevent identification of which 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 effectiveness at the population level. The UWBCS model, developed as part of the Cancer Intervention 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.