Imaging-based Determination of Breast Cancer Risk

拟议研究的长期目标是开发多模态，基于图像的标记， 评估乳腺密度和实质可以单独使用或与临床 测量，以及生物标志物，用于确定乳腺癌的风险。一般的假设是 包括对实质的自动分析将改善对乳腺癌风险的评估。 本研究的具体目标是：（1）对患者进行基于语义的分类 基于乳腺密度、实质形态学和实质动力学的数据库[将 自动提取]，（2）执行乳房密度的各种描述符的相关和建模 和具有已知的风险替代标志物（例如， BRCA1和BRCA2杂合子和对侧乳腺癌的存在），以产生新的基于图像的 风险标志物，（3）进行乳腺密度和实质组织的各种描述符的相关性 特征（即图像标记）与发展生物标志物和候选基因，以产生更好的 了解乳腺癌风险，以及（4）进行临床前评估和密度转换 和实质特征的妇女在高风险使用这些新的模型。临床翻译 这一部分将涉及与盖尔风险评估的现行方法进行定量比较， 模型和病例对照研究与其他机构的数据库有关的基于图像的标记， 癌症的发病。在未来，预计这种基于图像的标记物将用于改进的生物标记物。 评估乳腺癌高危患者，并监测预防措施的反应， 治疗。这项研究是新颖的，因为其他相关的研究乳腺癌的风险， 基于图像的分析仅涉及乳房密度。然而，在这里，我们结合了两个额外的， 潜在的互补性，乳腺实质的相关性和建模研究的分析。 芝加哥大学非常适合进行多模态的相关研究 基于图像的乳腺癌风险分析，因为它有20年的发展历史， 乳腺X线摄影、超声摄影和MRI的计算机辅助诊断方法及其集成 与芝加哥大学癌症风险诊所合作。

The long-term goal of the proposed research is to develop multi-modality, image-based markers for assessing breast density and parenchyma! structure that may be used alone or together with clinical measures, as well as biomarkers, for use in determining risk of breast cancer. The general hypothesis is that inclusion of automated analyses of the parenchyma will improve the assessment of breast cancer risk. The specific objectives of the proposed research are (1) to perform mage-based categorization of patient databases based on breast density, parenchyma morphology, and parenchyma kinetics [that will be automatically extracted], (2) perform correlation and modeling of the various descriptors of breast density and parenchymal characteristics (i.e. image-markers) with known surrogate markers of risk (such as BRCA1 and BRCA2 heterozygotes and presence of cancer on the contralateral breast) to yield new imagebased markers of risk, (3) perform correlation of the various descriptors of breast density and parenchymal characteristics (i.e. image-markers) with developing biomarkers and candidate genes to yield better understanding of breast cancer risk, and (4) perform preclinical assessment and translation of the density and parenchymal characteristics of women at high risk using these new models. This clinical translational component will involve quantitative comparison with the current method of risk assessment of the Gail model and a case control study with databases from other institutions relating the image-based markers to onset of cancer. In the future, it is expected that such image-based markers will be useful for improved assessment of patients at high risk for breast cancer and for monitoring the response of preventive treatments. The proposed research is novel in that other correlative research in breast cancer risk with image-based analyses involves only breast density. However, here we incorporate two additional, potentially complementary, analyses of the breast parenchyma into the correlative and modeling research. The University of Chicago is extremely well-positioned to perform this correlative research on multimodality image-based analyses for breast cancer risk because of its 20-year history of developing multimodality computer-aided diagnosis methods for mammography, sonography, and MRI, and its integration with the University of Chicago Cancer Risk Clinic.