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: 8629862
• 负责人: Rulla M Tamimi
• 金额: $ 42.21万
• 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/8629862
• 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）：乳房x线摄影密度是乳腺癌最强的危险因素之一。尽管如此，目前在临床环境中对乳腺密度的测量（即BI-RADS）是相对主观的，而且这种测量方法的使用很少。评估BI-RADS的动机是提醒放射科医生，因为乳房致密的女性乳房x光检查的敏感性较低；最广泛接受的乳房x线摄影密度测量方法是基于乳房x线摄影密度百分比（PMD）的手术辅助技术。虽然这些措施被广泛接受来预测乳腺癌的风险，但它们仍然需要一个阅读器，这是非常耗时的