Mammographic density and texture features in relation to breast cancer risk

描述(申请人提供)：乳房X光摄影密度是乳腺癌的最大危险因素之一。尽管如此，目前在临床环境中对乳房密度的测量(即BI-RADS)是相对主观的，并且该测量的利用率很低。评估BI-RADS的动机是为了提醒放射科医生，因为乳房致密的女性乳房X光检查的敏感度较低；其目的不是为了风险评估，最广泛接受的乳房X光检查密度的研究方法是利用基于乳房X光检查密度百分比(PMD)的操作员辅助技术。虽然这些方法被广泛接受来预测乳腺癌的风险，但它们仍然需要一位读者，因为这两种方法都是时间密集型的 并可能导致测量误差。缺乏自动化是临床应用的障碍。此外，目前的PMD测量没有捕捉到乳房X光摄影图像中的额外信息。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个与后续乳腺癌风险相关的乳房X光摄影特征的互补性自动化测量方法(目标1)：(1)乳房X光摄影密度百分比的自动测量，(2)单个纹理测量，以及(3)一种新的测量方法，称为V，它在单个测量中捕获包括空间变化在内的宽频带纹理信息。这些措施中的每一项都已证明可以预测至少一个人群中的乳腺癌风险。由合作调查人员开发的三项拟议措施是适用于数字化胶片乳房X光照相和数字乳房X光照相的客观、自动化技术。在目标2中，我们将评估乳腺癌风险因素与纹理特征的关系，并将确定乳腺癌RIS因素通过乳房X光摄影密度(即自动PMD)和纹理特征(即单个纹理测量和V)进行调节的程度。对乳房X光摄影纹理特征背后的生物学知识知之甚少。我们将通过对良性乳腺疾病样本进行集中病理检查的妇女的检查，确定乳房X光照片上的纹理特征是否与正常乳腺中与乳腺癌风险相关的特定形态变化有关(预期n=1304)(目标3)。这项建议是建基於护士健康研究部现有的大量资源。作为这项研究的一部分，我们希望从3480例乳腺癌病例和6974名对照中获得数字化筛查胶片乳房X光照片。由于PMD是乳腺癌的最大风险因素之一，一项强制向接受筛查的女性报告PMD的相对主观的非自动化测量指标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和质地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。拥有自动化和有效的方法来强烈预测乳腺癌风险，对乳腺癌风险预测、筛查和化学预防具有重要意义。

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.