The comparative effectiveness of incorporating novel DClS prognostic markers into the breast cancer screening process

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%的筛查发现的乳腺癌是DCIS。以来 我们对DCIS自然史的理解存在局限性， 由于DCIS进展为侵袭性癌症，DCIS的管理需要与侵袭性癌症的治疗相似的治疗。 即使DCIS后的相对生存率接近100%。研究人员正在积极寻找 对于通过鉴定预后标志物来优化筛选过程的方法， 恶性潜能我们的目标是（1）将当前的筛选过程与全面的，个性化的 乳腺癌筛查过程，考虑DCIS预后标志物，如正在研究中的 项目1和2。我们进一步的目标是（2）进行亚组分析，以确定新DCIS的使用如何 预后标志物影响对具有不同DCIS率的妇女进行筛查的益处和危害（例如，通过 年龄和种族），以及（3）评价数字乳腺X线摄影和MRI使用增加对DCIS的影响 发生率、过度治疗和新的DCIS预后标志物的比较有效性。解决 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）模型来检验 在人口水平上的相对有效性。UWBCS模型，作为癌症的一部分开发， 国际情报与监视建模网络（CISNET）是一个离散事件随机仿真模型 旨在复制美国人群中的乳腺癌发病率和死亡率。的数据 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模型提供必要的新输入。多种措施的好处和 将评估与乳腺癌筛查相关的危害。仿真建模非常适合于 比较有效性由于可以同时考虑许多筛选过程变量， 可以合并数据来源以弥补差距，并及时评估长期成果。 我们的比较有效性分析将提供一个框架，通过这个框架，可以 评估其对筛查的益处和危害的潜在影响，重点关注那些乳腺癌患者， 癌症诊断具有良好的预后，主要是通过筛查发现。该项目将 满足评估新型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所有人群的过度治疗来最大限度地提高生活质量。

Prior to widespread screening mammography, ductal carcinoma in situ (DCIS) was a rare diagnosis. Now 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.