Automated Density Measures for Estimating Breast Cancer Risk and Therapy Response

描述（由申请人提供）：通过乳腺X线摄影评估的乳腺密度是乳腺癌最强的风险因素之一，不仅为风险评估提供了重要信息，而且还为定制乳腺筛查方法和确定对治疗的反应提供了重要信息。迄今为止，乳腺密度的临床潜力尚未完全实现，部分原因是缺乏标准的、可重复的、客观的和自动化的临床密度测量。目前，美国放射学会乳腺成像报告和数据系统（BI-RADS）4类定性评估是最广泛使用的临床密度测量，但它是主观的，需要经过培训的放射科医生，并不总是报告，并且在评估响应t治疗中密度的微小但可能有意义的变化方面能力有限。我们和其他人已经开发了有前途的全视野数字乳腺X射线摄影（FFDM）的自动乳腺X射线摄影测量，包括面积，体积和变化密度测量，并显示其与乳腺癌的关联。迄今为止，研究尚未比较自动密度测量或其组合在相同女性的FFDM图像上的性能，以告知临床环境中使用的最佳测量。由于美国超过85%的乳腺X射线摄影是FFDM，因此需要在FFDM环境中开发和检查密度测量，以便在当今的临床护理中使用。我们建议检查和比较FFDM的乳房X线摄影测量与乳腺癌的相关性，并评估其检测2006-2015年间在马约Clinic和San弗朗西斯科乳房X线摄影登记处乳腺筛查实践中接受FFDM的35-75岁女性对绝经和激素治疗反应变化的能力。具体来说，我们建议1）在诊断前检索系列数字乳腺X线照片（或对照的相应日期）、BI-RADS密度和协变量，并估计所有图像上的自动面积、体积和变化密度测量; 2）检查来自最早和多次后续乳房X线照片的乳房密度测量与乳腺癌的关联，并评估这些关联是否因年龄、浸润性与原位乳腺癌以及ER、PR和HER-2定义的亚型而不同;和3）估计500名健康围绝经期妇女、1000名开始激素治疗的绝经期或绝经后妇女和1000名开始内分泌治疗的乳腺癌病例的FFDM密度测量值的绝经和治疗相关变化。该方案将回答：1）在FFDM环境中，哪种自动密度测量或测量组合作为乳腺癌的风险因素和检测密度对治疗的响应变化在临床上最有用？2)密度测量在年轻女性和老年女性以及特定类型的乳腺癌中是否表现相似？3）与单一指标相比，多项乳腺密度指标是否能改善风险相关性？该提案将影响美国各地的乳腺密度评估，使临床实践标准化，并有机会将密度测量纳入个性化风险评估和筛查。

DESCRIPTION (provided by applicant): Breast density assessed from film-screen mammography is one of the strongest risk factors for breast cancer and provides important information not only for risk assessment, but also for tailoring breast screening approaches and determining response to therapies. To date, the clinical potential of breast density has not been fully realized, in part due to lack of a standard, reproducible, objective and automated clinical density measure. Currently, the American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) 4-category qualitative assessment is the most widely used clinical density measure, but it is subjective, requires a trained radiologist, is not always reported, and s limited in its ability to assess small, but potentially meaningful changes in density in response t therapies. We and others have developed promising automated mammographic measures on full field digital mammography (FFDM), including area, volumetric and variation density measures, and shown their associations with breast cancer. To date, studies have not compared the performance of automated density measures or their combination on FFDM images from the same women, to inform the best measure(s) for use in the clinical setting. Since over 85% of mammography in the US is FFDM, density measures need to be developed and examined in the FFDM environment to translate for use in today's clinical care. We propose to examine and compare the association of mammographic measures from FFDM with breast cancer and evaluate their ability to detect changes in response to menopause and hormone therapies among women ages 35-75 receiving FFDM within the Mayo Clinic and San Francisco Mammography Registry breast screening practices between 2006-2015. Specifically, we propose 1) To retrieve serial, digital mammograms prior to diagnosis (or corresponding date for controls), BI-RADS density and covariates on a large nested case-control study of 2800 incident breast cancers and 5600 matched controls, and to estimate automated area, volumetric and variation density measures on all images; 2) To examine the association of breast density measures from both the earliest and multiple subsequent mammograms with breast cancer and to assess whether these associations differ by age, invasive vs. in situ breast cancer and ER, PR and HER-2 defined subtypes; and 3) To estimate menopause- and therapy-related changes in density measures on FFDM from 500 healthy perimenopausal women, 1000 peri or postmenopausal women initiating hormone therapy, and 1000 breast cancer cases initiating endocrine therapy. This protocol will answer: 1) Which automated density measure or combination of measures are most clinically useful in the FFDM environment as a risk factor for breast cancer and for detecting changes in density in response to therapy? 2) Do density measures perform similarly in younger vs. older women and for specific types of breast cancer? And 3) Do multiple measures of breast density improve risk associations compared to a single measure? This proposal will impact breast density assessment across the US, allowing for standardization in clinical practice and opportunities to integrate density measures into individualized risk assessment and screening.