An Automated System for Breast Cancer Biomarker Analysis

描述(由申请人提供)：研究长期以来一直认为乳房密度是乳腺癌(BC)的一个重要风险因素。与年龄、家族史、荷尔蒙暴露、产次等其他风险因素相比，乳房密度与乳腺癌风险的相关性相当或更大，奇比从2到6或更高。然而，乳房密度与BC风险之间的关联因几个事实而变得复杂。首先，在某种程度上，绝大多数女性都存在致密的乳房X光检查组织。第二，已报道的乳房密度测量与乳腺癌风险之间的关联在研究中显示出广泛的变异性，第三，乳房组织是乳腺癌风险以及过去和现在荷尔蒙相互作用的生物标志物。此外，目前还没有一个普遍适用或接受的标准来测量乳房密度。在这项工作中，我们将在我们以前在校准组织度量和数字化乳房X光摄影自动分析方面的工作的基础上，全面开发一种在使用通用电气SenogRaphe 2000D全场数字乳房X光摄影(FFDM)系统采集的乳房X光摄影中自动评估组织风险的方法。其目的是标准化输出图像，以便将每个像素校准为X射线束在相关探测器位置上方(像素上方)通过的致密组织的数量。这项工作包括比较体积测量和校准的二维测量。我们以前在这一领域的工作将被修改并构建为评估研究环境中密度的总体工具。由此产生的组织指标将通过病例对照研究在敏感性和特异性方面进行验证。为了更好地理解和量化乳房组织密度与风险的关系，未来有必要进行大型的多中心系列研究。然而，这首先需要了解校准随时间变化的稳定性，以及按照这里的建议设计适当的质量保证程序。将通过对位于PI站点和另一个站点(通过堪萨斯州威奇托市的Christi区域医疗中心)的两个类似的FFDM系统执行相同的校准来评估成像器内序列稳定性和成像器间一致性。将与工业中应用的用于监测系列质量控制的稳健方法进行系列比较。这项工作将产生一个经过校准的自动密度评估程序包，该程序包可以在各机构之间轻松实施，而不需要进行修改，以用于为组织相关风险研究量身定做的FFDM系统。

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.