An Automated System for Breast Cancer Biomarker Analysis

描述（由申请人提供）：长期以来，研究一直认为乳腺密度是乳腺癌（BC）的重要风险因素。与年龄、家族史、激素暴露、产次等其他风险因素相比，乳腺密度与乳腺癌风险有同等或更大的相关性，比值从2到6或更高。然而，乳腺密度-BC风险关联因几个事实而复杂化。首先，在某种程度上，绝大多数女性都存在致密的乳房X线摄影组织。第二，乳腺密度测量和BC风险之间的相关性在研究中显示出广泛的变异性，第三，乳腺组织是BC风险以及过去和现在激素相互作用的生物标志物。此外，目前还没有普遍适用或接受的测量乳房密度的标准。在这项工作中，我们将建立在我们之前在校准组织指标和数字化乳腺X线照片自动分析方面的工作基础上，并完全开发一种方法，用于自动评估使用通用电气Senographe 2000 D全视野数字乳腺X线摄影（FFDM）系统采集的乳腺X线照片中的组织风险。目的是标准化输出图像，以便将每个像素校准到X射线束在相关探测器位置上方（像素上方）穿过的致密组织的量。这项工作包括比较体积与校准的二维措施。我们以前在这方面的工作将被修改，并建立在一个总的工具，用于评估密度的研究环境。将通过病例对照研究验证所得组织指标的灵敏度和特异性。为了更好地理解和量化乳腺组织密度-风险相关性，未来将需要进行大型多中心系列研究。然而，这首先需要了解校准随时间的稳定性，以及设计适当的质量保证程序，如本文所建议的。将通过对位于Pi研究中心和另一个研究中心（Via Christi Regional Medical Center，威奇托，堪萨斯）的两个类似FFDM系统执行相同校准，评估成像仪内序列稳定性和成像仪间一致性。将采用行业中用于监测系列质控品的耐用方法进行系列比较。这项工作将产生一个校准的自动化密度评估包，可以很容易地在机构之间实施，而无需修改用于FFDM系统，是专门为组织相关的风险研究。

DESCRIPTION (provided by applicant): Research has long implicated breast density as an important risk factor for breast cancer (BC). Compared to other risk factors, such as age, family history, hormone exposure, parity, etc., breast density shows an equivalent or greater association with BC risk, with odd ratios ranging from two through six or higher. Nevertheless, the breast density-BC risk association is complicated by several facts. First, dense mammographic tissue is present, to some degree, in the vast majority of women. Second, reported associations between breast density measures and BC risk show broad variability across studies, third, breast tissue is a biomarker both for BC risk and for past and present hormonal interactions. Moreover, currently there are no universally applied or accepted standards for measuring breast density. In this work we will build on our previous work in calibrated tissue metrics and the automated analysis of digitized mammograms and fully develop an approach for automatically assessing tissue-risk in mammograms acquired with the General Electric Senographe 2000D full field digital mammography (FFDM) system. The aim is to standardize the output images so that each pixel is calibrated to the amount of dense tissue that the x-ray beam passed through above the related detector location (above the pixel). The work includes comparing volumetric with calibrated two-dimensional measures. Our previous work in this area will be modified and built into a total tool for assessing density in the research environment. The resulting tissue metrics will be validated in terms of sensitivity and specificity with a case-control study. In order to better understand and quantify the breast tissue density-risk associations, large multi-center serial based studies will be necessary in the future. However, this will first require understanding the calibration stability over time, as well as designing the appropriate quality assurance procedures as proposed here. The intra-imager serial stability and the inter-imager concordance will be assessed by performing the same calibration on two similar FFDM systems located at the Pi's site and another site (Via Christi Regional Medical Center, Wichita, Kansas). The serial comparisons will be made with robust methods applied in industry for monitoring serial quality control. This work will produce a calibrated automated density assessment package that may be easily implemented across institutions without modifications for use in the FFDM systems that is tailored for tissue-related risk research.