Volume and Morphology of Fibroglandular Tissue for Breast Cancer Risk Prediction

纤维腺组织的体积和形态用于乳腺癌风险预测

• 批准号: 8450061
• 负责人: Jeon-Hor Chen
• 金额: $ 20.06万
• 依托单位: UNIVERSITY OF CALIFORNIA-IRVINE
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-15 至 2014-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8450061
• 关键词: 3-Dimensional; Accounting; Adipose tissue; Advisory Committees; Affect; Age; American Cancer Society; Area; Attention; Benign; Bilateral; Boston; Breast; Breast Cancer Detection; Breast Cancer Prevention; California; Cancer Control; Cancer Patient; Case-Control Studies; Communities; Computer software; Consensus; Data Set; Databases; Densitometry; Development; Diagnostic; Disease; Family; Foundations; Guidelines; Health; High Risk Woman; Hormonal; Image; Individual; Intervention; Live Birth; Longitudinal Studies; Magnetic Resonance Imaging; Malignant Neoplasms; Mammographic Density; Mammography; Measurement; Measures; Methods; Modeling; Morphology; Outcome; Patients; Pattern; Play; Preventive; Radiology Specialty; Recording of previous events; Relative (related person); Research; Research Design; Research Personnel; Risk; Risk Assessment; Risk Estimate; Risk Factors; Risk Management; Role; Services; Source Code; Testing; Three-Dimensional Imaging; Time; Tissues; Universities; Weight; Woman; base; breast density; cancer risk; case control; cohort; density; follow-up; high risk; imaging modality; improved; indexing; malignant breast neoplasm; prospective; public health relevance; screening; success; tool; tv watching

DESCRIPTION (provided by applicant): Volume and Morphology of Fibroglandular Tissue for Breast Cancer Risk Prediction The role of breast density as a strong risk predictor for development of breast cancer has been established by many studies. The Breast Cancer Prevention Collaborative Group has recommended that quantitative breast density should be incorporated into the cancer risk prediction model, but how to reliably measure quantitative density parameters is still an active research area. In addition to the amount of density, the morphological distribution pattern of dense tissue may also play a role in risk prediction, which can only be analyzed on 3-dimensional images. In this R21 application we will evaluate the role of MRI-based density parameters, including the volume and the morphology of the fibroglandular tissue, and build a risk prediction model using a case-control study design. Three aims are proposed. Aim-1 will develop a fully automated segmentation software to segment the breast and the fibroglandular tissue. This software will be made available for sharing, and it will provide a very useful tool for researchers in the breast densitometry research field to analyze large datasets. Aim-2 will develop a risk prediction model based on the MRI-analyzed fibroglandular tissue volume and the morphological distribution pattern, in combination with six basic risk factors (age, hormonal use, family history, prior benign disease, weight, number of live birth) to differentiate between patients who were found to have cancer in screening MRI (cases) vs. matching controls. We have access to a large screening MRI database for retrospective analysis. It is estimated that 220 cancer cases will be available, and by using a 1:5 ratio we will select 1,100 matching controls for analysis. Then Aim-3 will evaluate how the fibroglandular tissue volume and morphological index may be used to improve the risk prediction accuracy, by comparing to the risks estimated by using existing standard models. The history sheet that each subject filled out will be used to calculate the risk scores by using Gail, Claus, BRCAPRO and Tyrer-Cuzick models. The ability of these existing models in differentiating between the cancer cases and controls will be compared to that analyzed using the MRI-density model developed in Aim-2, and the results will allow us to evaluate the added value of breast density in risk prediction. The success of this R21 will build a great foundation for a subsequent longitudinal study, using a prospective screening database that is being collected now within the Athena Breast Health Network formed by five University of California campuses.

描述（由申请人提供）：许多研究已经确定了乳腺癌风险预测乳腺癌风险预测乳腺癌风险的作用的数量和形态。乳腺癌预防合作组建议将定量乳房密度纳入癌症风险预测模型，但是如何可靠地衡量定量密度参数仍然是一个活跃的研究领域。除了密度量之外，密集组织的形态分布模式也可能在风险预测中起作用，只能在3维图像上进行分析。在此R21应用中，我们将评估基于MRI的密度参数的作用，包括纤维球组织的体积和形态，并使用病例对照研究设计建立风险预测模型。提出了三个目标。 AIM-1将开发一个全自动分割软件，以分割乳房和纤维状组织。该软件将用于共享，它将 为乳腺密度计研究领域的研究人员提供了一个非常有用的工具，以分析大型数据集。 AIM-2将基于MRI分析的纤维球组织量和形态分布模式开发风险预测模型，并结合六个基本危险因素（年龄，荷尔蒙使用，家族史，先前的良性疾病，体重，活产数，活体育次数， ）区分在筛查MRI（病例）与匹配对照中发现癌症的患者。我们可以访问大型筛选MRI数据库进行回顾性分析。据估计，将有220例癌症病例，并使用1：5 比率我们将选择1,100个匹配控件进行分析。然后，AIM-3将通过比较使用现有标准模型估计的风险来评估如何使用纤维结构组织体积和形态指数来提高风险预测准确性。每个受试者填写的历史表将使用盖尔（Gail）来计算风险评分 Claus，Brcapro和Tyrer-Cuzick模型。将这些现有模型在区分癌症病例和对照组中的能力与使用AIM-2中开发的MRI密度模型进行了比较，结果将使我们能够评估风险预测中乳房密度的附加值。 R21的成功将为随后的纵向研究奠定了良好的基础，该研究使用了一个预期的筛查数据库，该数据库现在正在加州大学五个校园组成的雅典娜乳房健康网络中收集。