Subregional measurements of breast features to assess breast cancer risk

描述(由申请人提供)：尽管已经确定了乳房密度等强烈的风险因素，但识别乳腺癌风险女性并不是当前女性健康临床范例的一部分。乳房X光摄影结构的空间分布与乳腺癌风险之间的关系尚不清楚。这一知识鸿沟为我们进一步了解乳房结构和乳腺癌之间的关系提供了一个重要机会。这项拟议研究的长期目标是确定用于高死亡率癌症风险评估的乳房特征的最佳局部测量。此应用程序的目的是描述浸润性癌症和DCIS病例中特定的局部纹理测量与癌症风险的关系，并发现纹理特征的空间分布如何基于已知的癌症风险因素而变化。中心假设是，与全球质地测量相比，分区域质地测量是更强的局部和全球乳腺癌风险因素。我们的第二个假设是，癌症风险直接影响乳房形态。这一假设是基于在申请人的实验室(方法)中测量的初步数据提出的，该数据显示，与某些次区域特征测量的乳房X光检查密度相比，风险关联更强。这一假设将通过追求以下两个具体目标来检验：1)确定与乳腺癌风险相关的乳房纹理和子区域；2)确定与已知的影响乳房密度、风险和功能的生物标记物相关的乳房纹理结构。在第一个目标下，我们将从一组预先定义的纹理特征(方法)开始，其中许多已经被申请者证明是乳房密度的独立风险因素，并使用我们建立的SFMR乳房X光摄影队列来比较不同的次区域纹理特征。在第二个目标下，每个特征将在网格区域内进行估计，并根据具有不同临床风险因素的女性的特征地形进行分类。我们的方法是独一无二的，因为它测量乳房局部区域的纹理特征，并且我们将使用校准的体积测量(SXA)来控制乳房密度，SXA将纹理变化与乳房密度分开，与乳房X光摄影密度不同。这项拟议研究的基本原理是，了解乳房结构的局部风险关联有可能转化为更强的临床风险分类，并允许识别与癌症相关的乳房结构。这一贡献将是重大的，因为它将从根本上改变我们对乳房质地和风险之间关系的理解，因为它确定了哪些结构导致乳癌。从这项研究中获得的知识有可能通过更好地针对乳腺癌高危女性来降低乳腺癌死亡率。

DESCRIPTION (provided by applicant): Identifying women at risk for breast cancer is not part of the current clinical paradigm for women's health even though strong risk factors, such as breast density, have been identified. The relationship between the spatial distribution of mammographic structures and breast cancer risk is not known. This knowledge gap represents an important opportunity to further our understanding of the relationship between breast structure and breast cancer. The long-term goal of this proposed research is to determine the best local measures of breast features for risk assessment of high-mortality cancers. The objective of this application is to describe the relationship of specific local textural measures to cancer risk in invasive cancers as well as DCIS cases and to discover how the spatial distribution of textural features varies based on known cancer risk factors. The central hypothesis is that subregional measurements of textural measures are stronger local and global breast cancer risk factors than global textural measures. Our secondary hypothesis is that cancer risk directly affects breast morphology. This hypothesis has been formulated based on preliminary data measured in the applicant's laboratory (Approach) showing stronger risk association than that found for mammographic density for certain subregional feature measures. The hypothesis will be tested by pursuing the following two specific aims: 1) Identify textures and subregions of the breast that are associated with breast cancer risk and 2) Identify breast texture topographies that are associated with biomarkers known to impact breast density, risk, and function. Under the first aim, we will start with a predefined set of textural features (Approach), many already shown by the applicants to be independent risk factors to breast density and use our established SFMR mammography cohort to compare different subregional textural features. Under the second aim, each feature will be estimated within grid regions and classified according to feature topography for women with different clinical risk factors. Our approach is unique in that it measures textural features on local regions of the breast and in that we will control for breast density using a calibrated volumetric measure (SXA) that separates textural variations from breast density, unlike mammographic density. The rationale for the proposed research is that understanding the local risk association of breast structure has the potential to translate into stronger clinical risk classification and allow identification of breast structures associated with cancer. This contribution will be significant because it will fundamentally change our understanding of the relationship between breast texture and risk by identifying what structures drive cancer in the breast. The knowledge gained from this research has the potential to reduce breast cancer mortality by allowing better targeting of women at risk for breast cancers.