Mammographic density and texture features in relation to breast cancer risk

乳腺X线密度和纹理特征与乳腺癌风险的关系

• 批准号: 8741957
• 负责人: Rulla M Tamimi
• 金额: $ 35.21万
• 依托单位: BRIGHAM AND WOMEN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-30 至 2017-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8741957
• 关键词: Automation; Benign; Biology; Breast; Breast Cancer Risk Factor; Breast Diseases; Chemoprevention; Clinical; Columnar Cell; Film; Goals; Heterogeneity; Image; Individual; Intention; Intervention; Lead; Lobular; Mammographic Density; Mammography; Manduca; Measurement; Measures; Mediating; Menopausal Status; Methods; Molecular; Motivation; Nurses; Nurses' Health Study; Pathology; Pattern; Population; Prevention; Proliferative Type Breast Fibrocystic Change; Reader; Reporting; Research; Research Personnel; Resources; Risk; Risk Assessment; Specimen; Statistical Methods; Surrogate Markers; Techniques; Texture; Time; Variant; Woman; Work; base; breast density; cancer risk; density; digital; malignant breast neoplasm; novel; public health relevance; radiologist; screening

DESCRIPTION (provided by applicant): Mammographic density is one of the strongest risk factors for breast cancer. Despite this, the current measurement of breast density in the clinical setting (i.e., BI-RADS) is relatively subjective and utilization of this measure is minimal. The motivation for assessing BI-RADS is to alert radiologists because sensitivity of mammography is lower in women with dense breasts; the intention was not for risk assessment The most widely accepted research measure of mammographic density utilizes an operator-assisted technique based on the percentage of mammographic density (PMD). While these measures are well accepted to predict risk of breast cancer, they still require a reader which is both time intensive and can lead to measurement error. The lack of automation is an impediment to clinical utilization. Further, there is additional information in mammographic images that are not captured by current PMD measurements. This heterogeneity in patterns of breast density is often referred to as 'texture'. We propose to evaluate the following three complementary automated measures of mammographic breast features in relation to subsequent breast cancer risk (Aim 1): (1) an automated measure of percent mammographic density, (2) individual texture measures and (3) a new measure, called V that captures a wide-band of textural information including spatial variation in a single measure. Each of these measures has demonstrated to predict breast cancer risk in at least one population. The three proposed measures developed by co-investigators are objective, automated techniques that are applicable to digitized film mammograms as well as digital mammograms. In Aim 2, we will evaluate breast cancer risk factor in relation to the texture features and will determine the extent to which breast cancer ris factors are mediated through mammographic density (i.e., automated PMD) and textural features (i.e., individual texture measures and V). Very little is known about the biology underlying mammographic texture features. We will determine if texture features on a mammogram are related to specific morphologic changes in the normal breast that are associated with breast cancer risk by examining these features on women whose benign breast disease specimens have undergone centralized pathology review (expected n=1304) (Aim 3). This proposal builds on a wealth of existing resources within the Nurses' Health Studies. As part of this study, we expect to have digitized screening film mammograms from 3480 breast cancer cases and 6974 controls. Because PMD is one of the strongest risk factors for breast cancer, a proposal to mandate the reporting of a relatively subjective non-automated measure of PMD, BI-RADS, to women undergoing screening is currently under Congressional review. The major goals of this proposal are to determine if automated measures of PMD and texture are associated with breast cancer, and to better understand the mechanisms by which they influence risk. Having automated and validated measures that strongly predict breast cancer risk has important implications for breast cancer risk prediction, screening, and chemoprevention.

描述（由申请人提供）：乳腺X线摄影密度是乳腺癌最强的危险因素之一。尽管如此，在临床环境中（即BI-RADS）中乳腺密度的当前测量是相对主观的，并且该度量的利用率很小。评估BIRADS的动机是提醒放射科医生，因为乳房密集的女性乳房X线摄影的敏感性较低。目的不是为了评估风险评估，最广泛接受的乳房X线学密度研究指标利用了基于乳房X线照相密度（PMD）百分比的操作员辅助技术。尽管这些措施被广泛接受以预测乳腺癌的风险，但它们仍然需要一个读者，这既是时间密集的 并可能导致测量误差。缺乏自动化是临床利用的障碍。此外，乳房X线图像中还有其他信息，这些信息未通过当前PMD测量来捕获。乳房密度模式的这种异质性通常称为“质地”。我们建议评估以下三种与随后的乳腺癌风险有关的乳房乳房乳房特征的互补自动措施（AIM 1）：（1）乳房X线学百分比百分比的自动量度；（2）单个纹理量度和（3）一种称为V的新措施，称为V，该措施捕获了一种捕获宽带的文本信息，包括单一措施中的空间变异，包括空间变异。这些措施中的每一个都证明可以预测至少一个人群的乳腺癌风险。共同投资者开发的三种提出的措施是客观的自动化技术，适用于数字化膜X线照片以及数字乳房X线照片。在AIM 2中，我们将评估与纹理特征相关的乳腺癌危险因素，并将确定乳腺癌因子因乳腺X线摄影密度（即自动化PMD）和纹理特征（即个人纹理测量和V）介导的程度。关于乳腺X线X型纹理特征的生物学知之甚少。我们将确定乳房X线照片上的纹理特征是否与正常乳腺的特定形态变化有关，这些特征与乳腺癌风险相关的特定形态变化是通过检查良性乳腺病标本对良性乳腺疾病标本进行集中病理学评论的这些特征（预期n = 1304）的（AIM 3）的。该提案以护士健康研究中的大量现有资源为基础。作为这项研究的一部分，我们希望从3480例乳腺癌病例和6974个对照组中进行数字化筛查膜乳房X线照片。由于PMD是乳腺癌最强的危险因素之一，因此目前正在接受筛查的妇女对妇女进行相对主观的非自动化量度的报告。该提案的主要目标是确定PMD和纹理的自动措施是否与乳腺癌相关，并更好地了解其影响风险的机制。强烈预测乳腺癌风险的自动化和验证措施对乳腺癌的风险预测，筛查和化学预防具有重要意义。