Analytical Investigation of Breast Cancer Progression: DCIS, Overdiagnosis

乳腺癌进展的分析调查：DCIS、过度诊断

• 批准号: 9069764
• 负责人: Sandra J Lee
• 金额: $ 35.68万
• 依托单位: DANA-FARBER CANCER INST
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-03 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9069764
• 关键词: Abnormal Cell; Applications Grants; Area; Breast; Breast Cancer Detection; Breast Cancer Early Detection; Breast Cancer Model; Clinical; Contralateral; Country; Data; Development; Diagnosis; Disease; Disease Progression; Disputes; Duct (organ) structure; Early Diagnosis; Exhibits; Film; Incidence; Indolent; Investigation; Knowledge; Lead; Life; Light; Likelihood Functions; Malignant Neoplasms; Mammographic screening; Mammography; Maximum Likelihood Estimate; Medical Surveillance; Menopause; Meta-Analysis; Methodology; Methods; Modeling; Natural History; Nature; Noninfiltrating Intraductal Carcinoma; Norway; Persons; Probability; Process; Publishing; Research; Research Personnel; Residual state; Risk; Risk Factors; Screening for cancer; Staging; Statistical Methods; Symptoms; Time; Woman; Work; base; case finding; clinical practice; digital; disease diagnosis; malignant breast neoplasm; pre-clinical; programs; screening; treatment as usual; tumor progression

DESCRIPTION (provided by applicant): This grant proposal is targeted at research problems arising in the early detection of cancer with special emphasis on the early diagnosis of breast cancer. The principal research areas include:(i) Develop a stochastic model for the disease progression of breast ductal carcinoma in situ (DCIS), (ii) Investigate the natural history of breast cancer, including methods for estimating the pre-clinical sojourn time and lead time distributions and (iii) Develop a new method for investigating the over diagnosis of disease. The diagnosis of breast DCIS in the U.S. has risen rapidly consistent with the dissemination of mammographic screening. Little is known about the natural history of DCIS. However women diagnosed with DCIS are known to be at elevated risk for invasive breast cancer (IBC). Knowledge on the natural history of DCIS, i.e. how it develops, if and when it will progress to IBC, would be an essential first step to the management of DCIS. Modeling the natural history of DCIS is challenging as there are no "ideal" data on DCIS progression of untreated DCIS cases. The first main aim of this research is to develop a stochastic model to evaluate the DCIS progression process. This model will utilize the U.S. Surveillance Epidemiology and End Results (SEER) and Norwegian data. Another aim involves a theoretical investigation of the sojourn time distribution in the early stage of breast cancer and the lead time associated with early detection of cancer. The last aim develops a new method for over diagnosis estimation. It arises in the early detection of disease setting, by screening programs finding cases which would never have become clinical in one's life time. Currently the actual level of over diagnosis from breast cancer screening is in dispute. It is planned to develop a new statistical method for estimating over diagnosis. This method will be unique in that it will make use of the disease incidence data. Applications will be made using the data from the Norwegian Breast Cancer Screening Program. Overall the proposed research will contribute greatly to the management of DCIS in clinical practice and shed some light on ongoing debate of 'harms" of breast cancer screening.

描述(申请人提供)：这项资助建议是针对癌症早期检测中出现的研究问题，特别强调乳腺癌的早期诊断。主要研究内容包括：(I)建立乳腺导管原位癌(DCIS)疾病进展的随机模型；(Ii)研究乳腺癌的自然病史，包括临床前停留时间和领先时间分布的估计方法；(Iii)开发一种新的疾病过度诊断的研究方法。随着乳房X光检查的普及，乳房DCIS的诊断率在美国迅速上升。人们对DCIS的自然历史知之甚少。然而，众所周知，被诊断为DCIS的女性患浸润性乳腺癌(IBC)的风险更高。了解DCIS的自然历史，即它是如何发展的，如果以及何时将发展到IBC，将是DCIS管理的重要第一步。对DCIS的自然历史进行建模是具有挑战性的，因为没有关于未经治疗的DCIS病例的DCIS进展的“理想”数据。本研究的第一个主要目的是建立一个随机模型来评估DCIS的进展过程。该模型将利用美国流行病学和最终结果监测(SEER)和挪威的数据。另一个目的是对乳腺癌早期的逗留时间分布和与癌症早期发现相关的提前时间进行理论研究。最后一个目的是开发一种新的过度诊断估计方法。它出现在疾病的早期检测环境中，通过筛选程序发现在一个人的有生之年永远不会成为临床病例。目前乳腺癌过度诊断的实际水平 筛查存在争议。计划开发一种新的统计方法来估计过度诊断。这种方法将是独一无二的，因为它将利用疾病发病率数据。申请将使用挪威乳腺癌筛查计划的数据。总体而言，拟议的研究将对临床实践中的DCIS管理做出重大贡献，并为正在进行的关于乳腺癌筛查“危害”的辩论提供一些启示。