The Mathematics of Breast Cancer Overtreatment: Improving Treatment Choice through Effective Communication of Personalized Cancer Risk

乳腺癌过度治疗的数学：通过有效沟通个性化癌症风险来改善治疗选择

• 批准号: 9788293
• 负责人: Marc Ryser
• 金额: $ 24.76万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9788293
• 关键词: Access to Information; Address; Area; Benign; Biological; Breast Carcinoma; Carcinoma in Situ; Clinical; Communication; Data; Data Sources; Decision Aid; Decision Making; Diagnosis; Diagnostic; Doctor of Philosophy; Europe; Evaluation; Goals; Health; In Situ Lesion; Intuition; Investments; Knowledge; Lesion; Lung; Mathematics; Mentors; Mission; Modeling; Operative Surgical Procedures; Outcome; Patients; Phase; Physicians; Physics; Population; Positioning Attribute; Prevention; Prostate; Public Health; Quality of life; Randomized Controlled Trials; Recording of previous events; Research; Research Design; Risk; Screening for cancer; Selection for Treatments; Source; Technology; Testing; Thyroid Gland; Time; Uncertainty; United States National Institutes of Health; Validation; Woman; Work; base; cancer care; cancer risk; career development; clinically actionable; cognitive interview; cohort; design; evidence base; experience; improved; knowledge base; malignant breast neoplasm; mathematical model; mortality; overtreatment; personalized cancer care; personalized decision; predictive modeling; synergism; tool; translational impact; treatment choice; tumor; virtual

Project Summary/Abstract This Career Development Application provides targeted coursework and mentored research to enable pro- gression to independent research in the highly cross-disciplinary areas of mathematical modeling and person- alized breast cancer care. Every year, close to 60,000 women in the US undergo radical surgery after diagno- sis with screen-detected breast carcinoma in situ (BCIS), yet as many as 45,000 of these are treated for be- nign lesions that would not progress to invasive breast cancer in their lifetime. The resulting overtreatment of non-progressive BCIS lesions can cause substantial harms and significantly reduce the patient's quality of life without reducing breast cancer mortality. Although the widespread overtreatment of women with BCIS is well documented at the population level, its prevention at the patient level is hindered by the current treatment par- adigm, which dictates that virtually all patients undergo immediate treatment. This in turn perpetuates the lack of data needed for the evaluation of management strategies other than immediate treatment, such as active surveillance. To resolve this conundrum, randomized controlled trials on active surveillance have been initiated, but only recently and only in Europe. It is anticipated that these trials, even if successful, will not yield clinically actionable data for at least 10 years. At the same time, however, there is a wealth of existing clinical and bio- logical data on BCIS that is dispersed across a large number of data and knowledge sources. In the absence of quantitative models that enable the integration of these dispersed sources, the bulk of the existing data re- mains inaccessible to patients. Thus, to enable informed decision making among patients with BCIS, there is a critical need (i) to develop predictive models that integrate available patient- and tumor-specific data to make personalized risk and uncertainty projections for different management strategies, and (ii) to effectively com- municate these personalized projections to patients. In the absence of tools for the quantification and commu- nication of personalized risk projections, it remains difficult for patients and physicians to weigh the trade-offs associated with different management strategies and to make an informed, evidence-based decision that re- duces the risk of potentially harmful overtreatment of BCIS. The long-term goal is to develop personalized de- cision aids that maximize informed decision-making and minimize overtreatment in patients with BCIS. The overall objective of this proposal comprises the first three steps towards this goal: (i) to develop personalized risk projection models for different management strategies of BCIS, (ii) to use these projections to develop a personalized decision aid, and (iii) to evaluate its impact in in a test cohort of women without a history of breast cancer. Our central hypothesis is that communication of model-based personalized risk projections leads to an improved understanding of the trade-offs associated with different management strategies for BCIS. The ra- tionale for the proposed research is that with personalized outcome estimates, patients gain access to the in- formation needed for an evidence-based decision that is aligned with their personal risk tolerance. The specific aims for the mentored (K) and independent (R) research phases of this K99/R00 are as follows. Aim K1: Discover data and knowledge sources that are relevant for personalized risk projections in BCIS pa- tients, and curate them into a harmonized data store and knowledge base, respectively. Aim K2: Develop mathematical models that use the data store and knowledge base to compute personalized risk projections for different BCIS management strategies, including active surveillance. Aim K3: Design a two-stage study to develop, refine and evaluate a model-based personalized decision aid for BCIS patients through cognitive interviews (Stage 1) and a RCT (Stage 2). Aim R1: Perform model validation and uncertainty quantification to maximize model confidence. Aim R2: Stage 1: Conduct cognitive interviews to develop and refine an interactive decision aid for the effec- tive communication of personalized risk projections in BCIS patients. Aim R3: Stage 2: Implement a RCT to test the main hypothesis that the use of personalized decision aids leads to (i) an increase in the proportion of women who would consider active surveillance as a viable management strategy for BCIS, and (ii) an increase in knowledge of the associated risk trade-offs. The deliverables will include a data-driven mathematical modeling framework, expected to yield the best pos- sible patient-specific risk projections for different management strategies of BCIS. The interactive decision aid is expected to provide an intuitive understanding of the risks and uncertainties that are associated with different BCIS management strategies. Moreover, this approach will have widespread application in other screen- detected lesions of unknown progression risk, such as those increasingly diagnosed in the prostate, thyroid and lung. The applicant has completed graduate studies in physics (MSc) and mathematics (PhD) and has ini- tiated projects with the primary mentor who has extensive experience in early stage breast cancer, including BCIS. Based on his history of successful collaborative research with clinicians, the applicant is in the unique position to bridge the divide between mathematical modeling and personalized cancer care.

项目摘要/摘要 此职业发展应用程序提供有针对性的课程作业和指导研究，以支持 回归到高度跨学科的数学建模和个人研究领域的独立研究 乳腺癌护理的标准化。在美国，每年有近6万名女性在确诊后接受根治性手术。 筛查发现的乳腺原位癌(BCIS)的SIS，但其中多达45,000人因BE-BE而接受治疗。 在有生之年不会进展为浸润性乳腺癌的病变。由此导致的过度治疗 非进展性BCIS病变可造成实质性损害，并显著降低患者的生活质量 而不会降低乳腺癌死亡率。尽管对患有BCIS的女性普遍过度治疗是好的 在人群水平上记录的，其在患者水平上的预防受到当前治疗标准的阻碍。 Adigm，它规定几乎所有的患者都要立即接受治疗。这反过来又延续了这种缺乏 评估除立即治疗以外的管理战略所需的数据，如积极的 监视系统。为了解决这一难题，关于主动监测的随机对照试验已经启动， 但只是在最近，而且只在欧洲。预计这些试验即使成功，在临床上也不会产生效果。 至少10年的可操作数据。然而，与此同时，有丰富的现有临床和生物- BCI上分散在大量数据和知识源中的逻辑数据。在缺席时 在能够集成这些分散的源的量化模型中，现有的大部分数据重新 病人无法接触到电源。因此，为了使BCIS患者能够做出明智的决定，有一种 迫切需要(I)开发预测模型，将可用的患者和肿瘤特定数据整合在一起，以使 针对不同管理战略的个性化风险和不确定性预测，以及(Ii)有效地将风险和不确定性预测 向患者传达这些个性化的预测。在缺乏量化和沟通的工具的情况下- 个性化风险预测的应用，患者和医生仍然很难权衡权衡 与不同的管理战略相关联，并做出知情的、以证据为基础的决策，以重新 导致潜在有害的过度治疗BCIS的风险。我们的长期目标是开发个性化的 精确度有助于最大限度地做出明智的决策，并最大限度地减少BCIS患者的过度治疗。这个 该提案的总体目标包括实现这一目标的前三个步骤：(I)发展个性化 BCIS不同管理策略的风险预测模型，(Ii)使用这些预测来制定 个性化决策辅助，以及(Iii)在没有乳房史的女性测试队列中评估其影响 癌症。我们的中心假设是，基于模型的个性化风险预测的沟通会导致 提高了对BCIS不同管理战略的权衡的理解。Ra- 这项拟议研究的方案是，通过个性化的结果估计，患者可以访问 形成一个基于证据的决策，与他们的个人风险承受能力保持一致。具体的 K99/R00的指导(K)和独立(R)研究阶段的目标如下。 目标K1：在BCIS页面中发现与个性化风险预测相关的数据和知识来源- 并将其分别整理成统一的数据存储和知识库。 目标K2：开发使用数据存储和知识库来计算个性化的数学模型 对不同的BCIS管理战略进行风险预测，包括主动监测。 目标K3：设计一个分两个阶段的研究，以开发、改进和评估基于模型的个性化决策辅助工具 BCI患者通过认知访谈(阶段1)和随机对照试验(阶段2)。 目的R1：进行模型验证和不确定性量化，以最大化模型置信度。 目标R2：阶段1：进行认知访谈，以开发和改进交互式决策辅助工具 BCIS患者个性化风险预测的动态沟通。 目标R3：阶段2：实现RCT以测试使用个性化决策辅助工具的主要假设 导致：(1)认为积极监测是可行的妇女比例增加 国际独联体的管理战略，以及(2)增加对相关风险权衡的认识。 交付成果将包括数据驱动的数学建模框架，预计将产生最好的POS- 适用于BCIS不同管理策略的合理的患者特定风险预测。交互式决策辅助工具 预计将提供对与不同的 BCI管理策略。此外，该方法还将在其他屏幕上广泛应用- 检测到进展风险未知的病变，例如那些日益被诊断为前列腺、甲状腺 还有肺。申请人已完成物理学(理学硕士)和数学(博士)的研究生课程，并拥有以下学位： 与在早期乳腺癌方面有丰富经验的主要导师合作的项目，包括 BCI。基于他与临床医生成功合作研究的历史，申请人是独一无二的 在数学建模和个性化癌症护理之间架起一座桥梁。