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.

在广泛进行乳房X光检查之前，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% 的筛查检测乳腺癌都是 DCIS。自从 我们对 DCIS 自然史了解的局限性阻碍了对 DCIS 肿瘤的识别 当进展为侵袭性癌症时，DCIS 的治疗需要与侵袭性癌症类似的治疗 乳腺癌，尽管 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查过程的方法，以识别 DCIS 恶性潜能。我们的目标是 (1) 将当前筛选流程与全面、个性化的筛选流程进行比较 乳腺癌筛查过程考虑 DCIS 预后标志物，例如正在研究的标志物 项目 1 和 2。我们进一步的目标是 (2) 进行亚组分析以确定如何使用新的 DCIS 预后标志物会影响对患有不同 DCIS 率的女性进行筛查的益处和危害（例如，通过 年龄和种族），以及 (3) 评估增加数字乳房 X 线摄影和 MRI 使用对 DCIS 的影响 发生率、过度治疗以及新 DCIS 预后标志物的相对有效性。致地址 为了实现这些目标，我们将使用威斯康星大学乳腺癌模拟 (UWBCS) 模型来检验 人口层面的比较有效性。 UWBCS 模型，作为癌症研究的一部分而开发 意图/意图和监视建模网络（CISNET），是一种离散事件、随机仿真模型 旨在复制美国人口中乳腺癌的发病率和死亡率。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 为本项目的 UWBCS 模型提供重要的新输入。多种效益衡量指标 将评估与乳腺癌筛查相关的危害。仿真建模非常适合 比较有效性，因为可以同时考虑许多筛选过程变量， 可以合并数据源以弥补差距，并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，通过该框架可以确定新的预后标志物 评估其对筛查的益处和危害的潜在影响，重点关注那些乳房 预后良好的癌症诊断主要只能通过筛查发现。该项目将 满足评估新颖的个性化治疗决策方法是否相关的关键需求 新兴的筛查测试可以避免所有人群的过度治疗，从而最大限度地提高生活质量。