An Automated System for Breast Cancer Biomarker Analysis

用于乳腺癌生物标志物分析的自动化系统

基本信息

批准号：
7886709
负责人：
JOHN J HEINE
金额：
$ 25.29万
依托单位：
H. LEE MOFFITT CANCER CTR & RES INST
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-08-03 至 2012-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7886709
关键词：
Accounting Address Adjuvant Adjuvant Study Age Algorithms Area Biological Biological Markers Breast Breast Cancer Risk Factor Calibration Case-Control Studies Computing Methodologies Coupled Data Data Collection Data Set Data Storage and Retrieval Detection Development Diagnostic Digital Mammography Disease Management Dissection Electronics Eligibility Determination Engineering Ensure Environment Family history of Film Financial compensation Foundations Future Goals Hormonal Hormone replacement therapy Hormones Image Indium Industry Institution Intervention Kansas Knowledge Lesion Location Malignant Neoplasms Mammary Gland Parenchyma Mammographic Density Mammography Maps Measurement Measures Medical center Metabolic Methods Metric Modification Monitor Nature Noise Odds Ratio Output Pathology Patients Photons Physics Principal Investigator Procedures Property Publishing Qualifying Quality Control Radiation Reporting Reproducibility Reproductive History Research Research Design Research Personnel Risk Risk Factors Screening procedure Sensitivity and Specificity Signal Transduction Sister Site Solid Somatotropin Source Staging Standardization Structure System Tamoxifen Techniques Thick Time Tissues Tube Uncertainty Variant Wichita Woman Work attenuation base breast density cancer risk case control cholesterol-binding protein computer code data integration data sharing density design detector digital improved innovation malignant breast neoplasm parity programs quality assurance response solid state standard measure standardize measure statistics tool transmission process two-dimensional voltage

项目摘要

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.

描述（由申请人提供）：研究长期以来一直表明乳腺密度是乳腺癌 (BC) 的重要危险因素。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 BC 风险具有同等或更大的相关性，奇数比范围为 2 至 6 或更高。然而，乳腺密度与 BC 风险之间的关联由于几个事实而变得复杂。首先，绝大多数女性在某种程度上都存在致密的乳房X线照相组织。其次，报道的乳腺密度测量值与 BC 风险之间的关联显示了不同研究之间的广泛差异，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，目前还没有普遍应用或接受的测量乳房密度的标准。在这项工作中，我们将基于之前在校准组织指标和数字化乳房 X 光检查自动分析方面的工作，全面开发一种自动评估使用通用电气 Senographe 2000D 全视野数字乳房 X 光检查 (FFDM) 系统获取的乳房 X 光检查中的组织风险的方法。目的是标准化输出图像，以便将每个像素校准到 X 射线束穿过相关探测器位置（像素上方）上方的致密组织的数量。这项工作包括将体积测量与校准的二维测量进行比较。我们之前在该领域的工作将被修改并构建成一个用于评估研究环境中密度的完整工具。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腺组织密度与风险的关联，未来有必要进行大型多中心连续研究。然而，这首先需要了解校准随时间的稳定性，以及设计此处建议的适当的质量保证程序。将通过对位于 Pi 站点和另一个站点（堪萨斯州威奇托市 Via Christi 区域医疗中心）的两个类似 FFDM 系统执行相同的校准来评估成像器内串行稳定性和成像器间一致性。连续比较将采用工业中应用的用于监测连续质量控制的稳健方法进行。这项工作将产生一个校准的自动密度评估包，可以轻松地跨机构实施，无需修改即可用于专为组织相关风险研究定制的 FFDM 系统。