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Quantitative breast cancer risk index from routine 3-D imaging

常规 3D 成像定量乳腺癌风险指数

基本信息

批准号：
8697025
负责人：
SRINIVASAN VEDANTHAM
金额：
$ 17.67万
依托单位：
UNIV OF MASSACHUSETTS MED SCH WORCESTER
依托单位国家：
美国
项目类别：
财政年份：
2013
资助国家：
美国
起止时间：
2013-08-01 至 2015-07-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8697025
关键词：
3-Dimensional Age Agreement Algorithms American College of Radiology Area Breast Breast Cancer Detection Breast Cancer Risk Factor Cancer Prognosis Caring Categories Clinical Clinical Research Custom Data Development Diagnostic Digital Breast Tomosynthesis Disease Ensure Evaluation FDA approved Feasibility Studies Federal Government Future Health Image Image Analysis Individual Information Systems Intervention Lesion Magnetic Resonance Imaging Malignant Neoplasms Mammographic Density Mammography Manufacturer Name Masks Measurement Measures Meta-Analysis Methods Metric Modeling Mutation Organ Pattern Population Preparation Recording of previous events Regimen Relative Risks Reporting Research Retrospective Studies Risk Risk Assessment Risk Factors Simulate Site Statutes and Laws System Techniques Texture Three-Dimensional Image Three-Dimensional Imaging Tissues United States Vendor Woman Work base breast density cancer risk clinically significant design high risk imaging modality improved indexing malignant breast neoplasm radiologist reconstruction screening simulation tool two-dimensional

项目摘要

DESCRIPTION: This application is responsive to PA-10-026, "Development, Application, and Evaluation of Prediction Models for Cancer Risk and Prognosis (R21)". This research is disease and organ-specific and is aimed at developing and validating an accurate method for determining breast density, a known risk factor for breast cancer. Developing an accurate method for estimating breast density is essential to ensure the accuracy of prediction models for cancer risk and prognosis. Several studies have shown the association between breast density and breast cancer risk. A meta-analysis observed that the relative risk was ~4.7 for women with greater than 75% fibroglandular content compared to those with less than 5% fibroglandular content. Breast density is now considered the third highest risk factor in terms of relative risk after age and BRCA mutation. Recognizing the significance of breast density as a risk factor and the reduced sensitivity of mammography for women with dense breasts, several states now regulate that a woman undergoing screening mammography is informed of her breast density. In the United States, breast density is reported as per the American College of Radiology, Breast Imaging Reporting and Data System by the interpreting Radiologist that uses four categories. It is has been shown that agreement between Radiologists for such categorical assignment is only moderate. Hence, there is a need to develop accurate quantitative techniques for estimating breast density. While some studies use quantitative estimates of breast density from the projected area of the breast, often referred to as percent mammographic density, recent research has shown that volumetric estimates of breast density are more accurate predictors of breast cancer risk. In this research, we propose to develop and evaluate a quantitative algorithm that serves as a tool for the estimation of volumetric breast density and associated measures based on parenchymal texture analysis using 3-D images provided by digital breast tomosynthesis (DBT). At least one DBT system manufacturer has obtained FDA-approval for routine clinical use and several manufacturers are working towards FDA approval. Hence, the developed quantitative tool will be designed to be compatible with DBT systems from multiple vendors, facilitating its widespread use for clinical studies. This is particularly important for studies where image data are pooled from multiple sites that may use DBT systems from different vendors. The specific aims of this research include developing the quantitative tool, evaluating its quantitative accuracy, and conducting a feasibility study aimed a demonstrating its accuracy in a clinical population with breast MRI serving as the truth. The validated 3-D imaging based quantitative tool can be used in future studies to accurately determine the cancer risk associated with breast density, to assess the effect of pharmacologic intervention on breast density and cancer risk, and to develop personalized breast cancer screening regimens.

产品说明：本申请是对PA-10-026“癌症风险和预后预测模型的开发、应用和评估（R21）”的响应。这项研究是疾病和器官特异性的，旨在开发和验证一种准确的方法来确定乳腺密度，这是乳腺癌的一个已知风险因素。开发一种准确的乳腺密度估计方法对于确保癌症风险和预后预测模型的准确性至关重要。一些研究表明乳腺密度和乳腺癌风险之间存在关联。一项荟萃分析观察到，与纤维腺体含量低于5%的女性相比，纤维腺体含量大于75%的女性的相对风险约为4.7。乳腺密度现在被认为是仅次于年龄和BRCA突变的相对风险的第三高风险因素。认识到乳房密度作为一个危险因素的重要性以及乳房密度高的妇女乳房X光检查的灵敏度降低，一些州现在规定，接受乳房X光检查的妇女应被告知其乳房密度。在美国，乳腺密度由解读放射科医生根据美国放射学会乳腺成像报告和数据系统报告，使用四个类别。它已被证明，这种分类分配的放射科医生之间的协议只是中等。因此，需要开发用于估计乳腺密度的准确定量技术。虽然一些研究使用乳房投影面积的乳房密度定量估计，通常称为乳房X线摄影密度百分比，但最近的研究表明，乳房密度的体积估计是乳腺癌风险的更准确预测指标。在这项研究中，我们建议开发和评估一种定量算法，作为一种工具，用于估计体积乳腺密度和相关措施的基础上，实质纹理分析，使用数字乳腺断层合成（DBT）提供的3-D图像。至少一家DBT系统制造商已获得FDA批准用于常规临床使用，并且多家制造商正在努力获得FDA批准。因此，开发的定量工具将被设计为与多个供应商的DBT系统兼容，以促进其在临床研究中的广泛使用。这对于从可能使用不同供应商的DBT系统的多个站点汇集图像数据的研究尤其重要。本研究的具体目的包括开发定量工具，评估其定量准确性，并进行可行性研究，旨在证明其在临床人群中的准确性，乳腺MRI作为真相。经验证的基于三维成像的定量工具可用于未来的研究，以准确确定与乳腺密度相关的癌症风险，评估药物干预对乳腺密度和癌症风险的影响，并制定个性化的乳腺癌筛查方案。