Mammographic density and texture features in relation to breast cancer risk

乳房X线照相密度和纹理特征与乳腺癌风险相关

• 批准号: 8896563
• 负责人: Rulla M Tamimi
• 金额: $ 36.38万
• 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 2016-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8896563
• 关键词: Automation; Benign; Biology; Breast; Breast Cancer Risk Factor; Breast Diseases; Chemoprevention; Clinical; Columnar Cell; Film; Goals; Health; 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; 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）：乳房 X 光密度是乳腺癌最强的危险因素之一。尽管如此，目前临床环境中乳腺密度的测量（即 BI-RADS）相对主观，并且该测量的利用率很少。评估 BI-RADS 的动机是提醒放射科医生，因为乳房致密的女性的乳房 X 光检查敏感性较低；目的不是为了风险评估 最广泛接受的乳腺 X 线密度研究测量方法是利用基于乳腺 X 线密度 (PMD) 百分比的操作员辅助技术。虽然这些措施在预测乳腺癌风险方面已被广泛接受，但它们仍然需要一个耗时的读者 并可能导致测量误差。缺乏自动化是临床应用的障碍。此外，乳房 X 线摄影图像中还存在当前 PMD 测量无法捕获的附加信息。这种乳房密度模式的异质性通常被称为“质地”。我们建议评估以下三种与后续乳腺癌风险相关的乳房 X 光检查乳房特征的互补自动测量（目标 1）：（1）乳房 X 光检查密度百分比的自动测量，（2）个体纹理测量和（3）新测量，称为 V，捕获宽带纹理信息，包括单个度量中的空间变化。这些措施中的每一项都已被证明可以预测至少一个人群的乳腺癌风险。共同研究人员提出的三项措施是客观的自动化技术，适用于数字化胶片乳房X光检查以及数字乳房X光检查。在目标 2 中，我们将评估与纹理特征相关的乳腺癌风险因素，并将确定乳腺癌风险因素通过乳房 X 光密度（即自动 PMD）和纹理特征（即个体纹理测量和 V）介导的程度。 ）。关于乳房X线照相纹理特征背后的生物学知之甚少。我们将通过检查良性乳腺疾病标本经过集中病理学审查的女性的这些特征（预期 n=1304）来确定乳房 X 光照片上的纹理特征是否与正常乳房中与乳腺癌风险相关的特定形态变化有关（目标3）。该提案建立在护士健康研究中丰富的现有资源的基础上。作为这项研究的一部分，我们预计将获得 3480 名乳腺癌病例和 6974 名对照者的数字化筛查胶片乳房 X 光照片。由于 PMD 是乳腺癌最严重的危险因素之一，因此一项要求接受筛查的女性强制报告相对主观的非自动化 PMD 测量（BI-RADS）的提案目前正在接受国会审查。该提案的主要目标是确定 PMD 和纹理的自动测量是否与乳腺癌相关，并更好地了解它们影响风险的机制。拥有强有力预测乳腺癌风险的自动化且经过验证的措施对于乳腺癌风险预测、筛查和化学预防具有重要意义。