Volume and Morphology of Fibroglandular Tissue for Breast Cancer Risk Prediction

描述（由申请人提供）：乳腺癌风险预测的纤维腺体组织的体积和形态学乳腺密度作为乳腺癌发展的强风险预测因子的作用已被许多研究所确立。乳腺癌预防协作组建议将定量乳腺密度纳入癌症风险预测模型，但如何可靠地测量定量密度参数仍然是一个活跃的研究领域。除了密度的多少，致密组织的形态分布模式也可能在风险预测中发挥作用，这只能在三维图像上进行分析。在本R21应用中，我们将评估基于MRI的密度参数的作用，包括纤维腺体组织的体积和形态，并使用病例对照研究设计构建风险预测模型。提出了三个目标。Aim-1将开发一个全自动分割软件来分割乳腺和纤维腺体组织。该软件将用于共享，它将 为乳腺密度测量研究领域的研究人员分析大型数据集提供了一个非常有用的工具。目标-2将根据MRI分析的纤维腺体组织体积和形态分布模式，结合6个基本风险因素（年龄、激素使用、家族史、既往良性疾病、体重、活产数量）开发风险预测模型，以区分筛选MRI（病例）中发现患有癌症的患者与匹配对照。我们可以访问大型筛选MRI数据库进行回顾性分析。据估计，220个癌症病例将可用，并通过使用1：5 我们将选择1,100个匹配的对照进行分析。然后，Aim-3将通过与使用现有标准模型估计的风险进行比较，评价如何使用纤维腺体组织体积和形态指数来提高风险预测准确性。每例受试者填写的病史表将用于使用Gail计算风险评分， Claus、BRCAPRO和Tyrer-Cuzick模型。这些现有模型在区分癌症病例和对照组方面的能力将与使用Aim-2中开发的MRI密度模型分析的能力进行比较，结果将使我们能够评估乳腺密度在风险预测中的附加值。这项R21的成功将为随后的纵向研究奠定坚实的基础，该研究使用了一个前瞻性筛查数据库，该数据库目前正在由加州五所大学组成的雅典娜乳腺健康网络中收集。

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.