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: 8371645
• 负责人: Sandra J Lee
• 金额: $ 39.22万
• 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 2017-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8371645
• 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; Mammography; Maximum Likelihood Estimate; Medical Surveillance; Menopause; Meta-Analysis; Methodology; Methods; Modeling; Natural History; Nature; Noninfiltrating Intraductal Carcinoma; Norway; Persons; Probability; Process; Publishing; Relative (related person); Research; Research Personnel; Residual state; Risk; Risk Factors; Screening for cancer; Screening procedure; Staging; Statistical Methods; Symptoms; Time; Woman; Work; base; case finding; clinical practice; digital; disease diagnosis; malignant breast neoplasm; pre-clinical; programs; 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. PUBLIC HEALTH RELEVANCE: This research application investigates important issues in the early detection of breast cancer through stochastic modeling. The investigation on the natural history of breast ductal carcinoma in situ (DCIS) will greatly contribute to the management of DCIS in clinical practice. The proposed estimation method for over diagnosis (i.e., being diagnosed by screening which never be incident in one's life time) will shed some light in the ongoing debate of "harms" of breast cancer screening.

描述（由申请人提供）：该拨款提案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。主要研究领域包括：（i）开发乳腺导管原位癌（DCIS）疾病进展的随机模型，（ii）调查乳腺癌的自然史，包括估计临床前逗留时间和前置时间分布的方法，以及（iii）开发一种新的方法来调查疾病的过度诊断。在美国，乳腺DCIS的诊断率迅速上升，与乳腺X线筛查的普及一致。对DCIS的自然历史知之甚少。然而，已知诊断为DCIS的女性患浸润性乳腺癌（IBC）的风险较高。了解DCIS的自然历史，即它如何发展，是否以及何时发展到IBC，将是DCIS管理的重要第一步。对DCIS的自然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开发一个随机模型来评估DCIS进展过程。该模型将利用美国监测流行病学和最终结果（SEER）和挪威数据。另一个目标是对乳腺癌早期的逗留时间分布和与癌症早期检测相关的前置时间进行理论研究。最后一个目的是提出一种新的过诊断估计方法。它出现在疾病的早期发现中，通过筛查程序发现在一个人的一生中永远不会成为临床的病例。目前，乳腺癌过度诊断的实际水平 筛选有争议。计划开发一种新的统计方法来估计过度诊断。这一方法的独特之处在于它将利用疾病发病率数据。申请将使用挪威乳腺癌筛查方案的数据。总的来说，拟议的研究将大大有助于DCIS在临床实践中的管理，并阐明了一些正在进行的辩论的“危害”的乳腺癌筛查。 公共卫生相关性：该研究应用程序通过随机建模调查乳腺癌早期检测中的重要问题。对乳腺导管原位癌（DCIS）自然病程的研究有助于指导临床治疗。所提出的用于过度诊断的估计方法（即，通过筛查被诊断出来，这在一个人的一生中从来没有发生过）将在正在进行的关于乳腺癌筛查“危害”的辩论中有所启发。