Subregional measurements of breast features to assess breast cancer risk

乳房特征的次区域测量以评估乳腺癌风险

• 批准号: 8513944
• 负责人: Fred William Duewer
• 金额: $ 2.22万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2011
• 资助国家: 美国
• 起止时间: 2011-08-01 至 2013-10-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8513944
• 关键词: Affect; Age; Area; Biological Markers; Breast; Breast Cancer Risk Factor; Caliber; Classification; Clinical; Data; Data Set; Descriptor; Dimensions; Entropy; Family; Fractals; Future; Goals; Health; Hormones; Image; Knowledge; Laboratories; Malignant Neoplasms; Mammary Gland Parenchyma; Mammographic Density; Mammography; Manduca; Measurement; Measures; Mission; Morphology; Noninfiltrating Intraductal Carcinoma; Outcome; Public Health; Questionnaires; Recording of previous events; Relative (related person); Research; Risk; Risk Assessment; Risk Factors; Roentgen Rays; Spatial Distribution; Structure; Testing; Texture; Tissues; Translating; Variant; Woman; Women' s Health; Work; base; breast density; cancer initiation; cancer risk; cancer site; clinical risk; cohort; density; improved; malignant breast neoplasm; molecular imaging; mortality; parity; screening

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.

描述（由申请人提供）：尽管已经确定了乳腺密度等强风险因素，但识别有乳腺癌风险的女性并不是当前女性健康临床模式的一部分。乳房X线摄影结构的空间分布与乳腺癌风险之间的关系尚不清楚。这一知识差距代表了一个重要的机会，以进一步了解乳房结构和乳腺癌之间的关系。这项拟议研究的长期目标是确定最佳的乳腺特征局部测量方法，用于高死亡率癌症的风险评估。本申请的目的是描述特定局部纹理测量与浸润性癌症以及DCIS病例中癌症风险的关系，并发现纹理特征的空间分布如何根据已知的癌症风险因素而变化。中心假设是，次区域测量的纹理措施是更强的地方和全球乳腺癌的风险因素比全球纹理措施。我们的次要假设是癌症风险直接影响乳房形态。该假设是根据申请人实验室（方法）测量的初步数据制定的，表明某些次区域特征测量的风险关联性比乳腺X线摄影密度更强。将通过追求以下两个特定目标来检验该假设：1）识别与乳腺癌风险相关的乳房纹理和子区域，以及2）识别与已知影响乳房密度、风险和功能的生物标志物相关的乳房纹理地形。在第一个目标下，我们将从一组预定义的纹理特征（方法）开始，申请人已经证明许多纹理特征是乳腺密度的独立风险因素，并使用我们建立的SFMR乳腺X射线摄影队列来比较不同的次区域纹理特征。根据第二个目标，将在网格区域内估计每个特征，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。我们的方法是独特的，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体积测量（SXA）来控制乳房密度，该测量将纹理变化与乳房密度分开，这与乳房X光密度不同。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。这一贡献将是重要的，因为它将从根本上改变我们对乳房纹理和风险之间关系的理解，通过确定什么结构驱动乳房中的癌症。从这项研究中获得的知识有可能通过更好地针对有乳腺癌风险的妇女来降低乳腺癌死亡率。