Understanding the biological basis for the association between parenchymal texture features and breast cancer risk

了解实质纹理特征与乳腺癌风险之间关联的生物学基础

基本信息

批准号：
10697306
负责人：
Sarah Jane Nyante
金额：
$ 48.55万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10697306
关键词：
3-Dimensional Anxiety Area Automobile Driving Benign Biological Biological Factors Biological Markers Biological Process Biology Biopsy Biopsy Specimen Biosensor Breast Breast Cancer Detection Breast Cancer Patient Breast Cancer Prevention Breast Cancer Risk Factor Breast Carcinogenesis Breast Diseases Breast biopsy Cancer Burden Cancer Prognosis Characteristics Classification Communities Complement Complex Cross-Sectional Studies Data Development Dimensions Electronic Health Record Estradiol Estrogen Antagonists Estrogen Therapy Estrogens Estrone Foundations Fractals Goals Health Healthcare Systems Heterogeneity Histologic Histology Hormonal Individual Knowledge Length Lobular Malignant Neoplasms Mammographic Density Mammography Measurement Measures Medical Care Costs Menopausal Status Methods Mission Monitor Morphology National Cancer Institute Newly Diagnosed Non-Malignant North Carolina Outcome Pathway interactions Patients Pattern Population Prevention Property Public Health Radiology Specialty Recording of previous events Reporting Reproducibility Research Risk Role Running Structure Testing Texture Time Tissues Universities Unnecessary Procedures Urine Variant Visual Woman Women&apos s Group breast cancer diagnosis breast density breast imaging breast lesion case control clinical trial participant density evidence base improved insight lobular breast carcinoma in situ longitudinal analysis malignant breast neoplasm mammography registry member multidisciplinary novel precision medicine prospective radiomics response screening spatial relationship standard care statistics tool treatment response unnecessary treatment urinary

项目摘要

Breast composition is a potential breast biomarker, but its utility has been limited by measurement methods. Visually-assessed qualitative scales capture within-breast heterogeneity but are subjective and lack reproducibility. In contrast, quantitative automated assessments of global breast density are reproducible, but contain no information about within-breast variation. Limitations of both of these approaches can be overcome with the measurement of parenchymal texture features. Texture features are quantitative measures that estimate complex characteristics of pixel density in the breast image, ranging from descriptive statistics to higher order statistics that describe spatial relationships and structural patterns. Prior studies have shown that texture features independently predict breast cancer risk. However, little is known about the biological mechanisms driving that risk relationship. The objective of this study is to identify the biological processes associated with parenchymal texture features. The rationale is that direct evidence that texture features reflect specific biological properties will provide the basis for development of texture features as a dynamic marker of breast cancer risk and prognosis. This study will pursue three aims. Using a case-control analysis, Aim 1 will identify the texture features that are independently associated with newly-diagnosed breast cancer among women attending breast cancer screening. Aim 2 will evaluate how the texture features that were associated with breast cancer in this population vary with estrogen levels, through (i) cross-sectional analysis of texture features and 15 urinary estrogens and estrogen metabolites, and (ii) analyses of longitudinal change in texture features among breast cancer patients treated with anti-estrogenic therapy. Aim 3 will evaluate associations between texture features and breast histologic characteristics (tissue composition, benign breast disease/LCIS, measures of lobular involution) among women with a benign biopsy. Analyses will draw on existing mammograms, biopsy specimens, and electronic health records from women participating in mammography at the University of North Carolina; urine will be collected prospectively. Texture features will be measured using a novel lattice-based grid method developed and validated by members of the study team that allows information from the whole breast to inform the texture measurements. These analyses will establish: the magnitude of the relationship between lattice-based texture features and breast cancer in a general screening population (Aim 1); the extent to which texture features may act as biosensors of breast estrogen/anti-estrogen activity (Aim 2); and whether texture features can serve as a radiologic surrogate of histologic characteristics that have known associations with breast cancer risk (Aim 3). These results will clarify the potential role of parenchymal texture features as predictors of breast cancer risk and therapeutic response; such new uses have the potential to identify new prevention targets and reduce unnecessary procedures and treatments for women at risk for and being treated for breast cancer.

乳房成分是一种潜在的乳房生物标志物，但其实用性受到测量方法的限制。视觉评估的定性量表捕捉了乳房内的异质性，但具有主观性且缺乏再现性。相比之下，全球乳腺密度的定量自动评估是可重复的，但是不包含有关乳房内变异的信息。这两种方法的局限性都可以克服与实质纹理特征的测量。纹理特征是估计的定量测量乳房图像中像素密度的复杂特征，范围从描述性统计到高阶描述空间关系和结构模式的统计数据。先前的研究表明，纹理特征独立预测乳腺癌风险。然而，人们对驱动这一现象的生物学机制知之甚少。风险关系。本研究的目的是确定与实质相关的生物过程纹理特征。基本原理是纹理特征反映特定生物的直接证据这些特性将为开发纹理特征作为乳腺癌的动态标志物提供基础风险和预后。这项研究将追求三个目标。通过病例对照分析，目标 1 将确定与女性新诊断乳腺癌独立相关的纹理特征参加乳腺癌筛查。目标 2 将评估与相关的纹理特征如何通过 (i) 纹理特征的横断面分析，该人群中的乳腺癌随雌激素水平而变化和 15 种尿液雌激素和雌激素代谢物，以及 (ii) 质地纵向变化分析接受抗雌激素治疗的乳腺癌患者的特征。目标 3 将评估纹理特征与乳腺组织学特征（组织成分、良性）之间的关联进行良性活检的女性中的乳腺疾病/LCIS（小叶复旧测量）。分析将利用现有的乳房 X 光检查、活检标本和参与妇女的电子健康记录北卡罗来纳大学乳房X线摄影专业；将前瞻性地收集尿液。纹理特征将使用由研究成员开发和验证的新型基于晶格的网格方法进行测量团队允许来自整个乳房的信息来通知纹理测量。这些分析将建立：基于晶格的纹理特征与乳腺癌之间关系的大小在一般筛查人群中（目标 1）；纹理特征可以在多大程度上充当生物传感器乳房雌激素/抗雌激素活性（目标 2）；以及纹理特征是否可以作为放射学的已知与乳腺癌风险相关的组织学特征的替代（目标 3）。这些结果将阐明实质纹理特征作为乳腺癌风险预测因子的潜在作用治疗反应；这种新用途有可能确定新的预防目标并减少对有乳腺癌风险和正在接受乳腺癌治疗的女性进行不必要的手术和治疗。