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

将新型 DClS 预后标志物纳入乳腺癌筛查过程的比较有效性

• 批准号: 8715711
• 负责人: AMY TRENTHAM-DIETZ
• 金额: $ 22.69万
• 依托单位: UNIVERSITY OF VERMONT & ST AGRIC COLLEGE
• 依托单位国家: 美国
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-06-01 至 2016-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8715711
• 关键词: Address; Affect; African American; Age; Biopsy; Breast Cancer Detection; Breast Cancer Epidemiology; Cancer Intervention and Surveillance Modeling Network; Cause of Death; Characteristics; Communities; Consensus; Data; Data Sources; Decision Making; Detection; Diagnosis; Diagnostic; Digital Mammography; Disease; Event; Face; In Situ; In Situ Lesion; Incidence; Indolent; Institute of Medicine (U.S.); Investigation; Lead; Left; Life Expectancy; Link; Magnetic Resonance Imaging; Malignant - descriptor; Malignant Neoplasms; Mammography; Measures; Methods; Natural History; Noninfiltrating Intraductal Carcinoma; Outcome; Pattern; Population; Procedures; Process; Prognostic Marker; Quality of life; Quality-Adjusted Life Years; Race; Relative (related person); Reporting; Research; Research Personnel; Source; Subgroup; Surveillance Modeling; System; Technology; Testing; Treatment Cost; Universities; Vermont; Wisconsin; Woman; Women' s Group; aged; aggressive therapy; base; breast cancer diagnosis; cohort; comparative; comparative effectiveness; design; effectiveness research; interest; malignant breast neoplasm; model design; models and simulation; mortality; novel; older women; outcome forecast; prevent; screening; simulation; trend; tumor

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.

在广泛筛查乳房X线摄影之前，原位导管癌（DCIS）是罕见的诊断。现在 DCIS几乎有20％的乳腺癌诊断，几乎有30％的筛查乳腺癌。自从 我们对DCIS自然历史的理解的局限性阻止了DCIS肿瘤的识别 发展成为侵入性癌症，DCI的管理需要类似于侵入性疗法的治疗 即使DCIS接近100％的相对生存率，乳腺癌也是如此。研究人员正在积极搜索 对于通过识别预后标记以识别DCI的方法来优化筛选过程 恶性潜力。我们的目标是（1）将当前筛选过程与全面的个性化比较 考虑了DCIS预后标记的乳腺癌筛查过程，例如正在研究的标记 项目1和2。我们进一步致力于（2）执行亚组分析，以确定新DCI的使用如何 预后标记会影响DCI率不同的女性筛查的益处和危害（例如，通过 年龄和种族），并（3）评估数字乳腺X线摄影和MRI使用对DCIS的影响 新DCIS预后标记的发病率，过度治疗和比较有效性。解决 这些目的，我们将使用威斯康星大学乳腺癌模拟（UWBC）模型检查 人口水平的比较效率。 UWBCS模型，作为癌症的一部分开发 Inten/Ention和监视建模网络（CISNET）是一个离散的事件，随机仿真模型 旨在复制美国人群的乳腺癌发病率和死亡率。来自 佛蒙特州乳腺癌监视系统和其他来源，包括威斯康星州的原位队列，将 为该项目的UWBCS模型提供必不可少的新输入。多种衡量利益和 将评估与乳腺癌筛查相关的危害。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， 数据源可以合并以解决差距，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新的预后标记的框架 评估了它们对筛查的益处和危害的潜在影响，重点是 癌症诊断具有出色的预后，主要是通过筛查发现的。这个项目将 解决了评估新型新的个性化治疗决策方法是否捆绑的关键需求 为了避免在所有人群中过度治疗，可以避免过度治疗，可以最大程度地提高筛查测试。