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: 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
• 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' 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

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.

乳腺成分是一种潜在的乳腺生物标志物，但其实用性受到测量方法的限制。 视觉评估的定性尺度捕捉乳房内异质性，但主观和缺乏 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 不包含关于乳房内变化的信息。这两种方法的局限性都可以克服 与实质纹理特征的测量。纹理特征是一种定量的度量， 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，人们对驱动这一过程的生物学机制知之甚少。 风险关系。本研究的目的是确定与脑实质相关的生物学过程， 纹理特征基本原理是，纹理特征反映特定生物学特征的直接证据 这些特性将为纹理特征作为乳腺癌的动态标记物的发展提供基础 风险和预后。这项研究将追求三个目标。使用病例对照分析，目标1将确定 与新诊断的女性乳腺癌独立相关的纹理特征 参加乳腺癌筛查目标2将评估如何纹理特征，与 通过（i）纹理特征的横截面分析， 和15个尿雌激素和雌激素代谢产物，和（ii）纵向纹理变化的分析 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）之间的相关性 乳腺疾病/LCIS，小叶复旧的措施）的妇女与良性活检。分析将 利用现有的乳房X线照片、活检标本和参与妇女的电子健康记录， 在北卡罗来纳州大学进行乳房X线摄影;将前瞻性收集尿液。纹理特征 将使用一种新的基于网格的网格方法进行测量，该方法由该研究的成员开发和验证 该团队允许来自整个乳房的信息为纹理测量提供信息。这些分析 将建立：基于网格的纹理特征和乳腺癌之间的关系的大小 在一般筛选人群（目标1）;纹理特征可以作为生物传感器的程度 乳腺雌激素/抗雌激素活性（目标2）;以及纹理特征是否可以作为放射学检查的指标。 已知与乳腺癌风险相关的组织学特征的替代物（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， 这些新用途有可能确定新的预防目标， 对有乳腺癌风险和正在接受乳腺癌治疗的妇女进行不必要的程序和治疗。