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

• 批准号: 10241446
• 负责人: Sarah Jane Nyante
• 金额: $ 60.47万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 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/10241446
• 关键词: 3-Dimensional; Address; 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; 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; Woman; Women' s Group; base; 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

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，但其应用受到测量方法的限制。 肉眼评估的定性尺度反映了乳房内部的异质性，但具有主观性和缺乏 再现性。相比之下，全球乳房密度的定量自动评估是可重现的，但 不包含有关乳房内变异的信息。这两种方法的局限性都是可以克服的 与测量的实质纹理特征。纹理特征是一种量化度量，用于估计 乳房图像中像素密度的复杂特征，从描述性统计到更高阶 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，人们对导致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学过程。 纹理特征。其基本原理是质地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记物提供基础 风险和预后。本研究将追求三个目标。使用病例对照分析，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将评估如何将纹理特征与 这一人群中的乳腺癌随着雌激素水平的变化而变化，通过(I)质地特征的横断面分析 和15种尿雌激素和雌激素代谢物，以及(Ii)质地纵向变化的分析 接受抗雌激素治疗的乳腺癌患者的特点。AIM 3将评估 质地特征与乳腺组织学特征(组织成分、良性 乳房疾病/乳房疾病/乳房小叶退化的测量)。分析将 利用现有的乳房X光照片、活组织检查样本和参与研究的妇女的电子健康记录 在北卡罗来纳大学的乳房X光检查中，尿液将被前瞻性地收集。纹理特征 将使用一种新的基于网格的方法进行测量，该方法由研究成员开发和验证 允许来自整个乳房的信息来通知质地测量的团队。这些分析 将建立：基于网格的纹理特征与乳腺癌之间的关系的大小 在一般筛查人群中(目标1)；纹理特征可作为生物传感器的程度 乳房雌激素/抗雌激素活性(目标2)；以及质地特征是否可以作为放射学检查 替代已知与乳腺癌风险相关的组织学特征(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和 治疗反应；这种新用途有可能确定新的预防目标并减少 对于有乳腺癌风险和正在接受治疗的妇女来说，不必要的程序和治疗。