Quantitative Parenchyma Descriptor as an Imaging Biomarker of Breast Cancer Risk

 描述(由申请人提供)：定量实质描述符作为乳腺癌风险项目的成像生物标记物摘要/摘要乳腺癌仍然是40岁及以上女性死亡的主要原因之一。乳房X光检查已被用作乳腺癌的一种低成本筛查工具。最近关于乳腺癌筛查建议的争议增加了公众对基于个性化风险估计的筛查和卫生保健选项的知情咨询的认识和兴趣。这项拟议项目的目标是开发一种基于计算机图像的生物标记物，以评估筛查人群中个别患者的乳腺癌风险。我们方法的创新之处在于，定量乳房实质描述符(Q-BPD)将被设计为不仅考虑致密组织(PD)的百分比，而且考虑个体乳房的间质和上皮结构模式，其与 代理的，乳房密度。Q-BPD是通过联合分析在全视野数字乳腺X光(FFDM)上成像时的实质分布模式(乳房摄影实质模式，MPP)和致密组织的数量(PD)的复杂性而获得的。我们假设，建议的Q-BPD是乳腺癌的独立危险因素，并且将比以前的方法(如PD或BI-RADS密度类别)具有更强的预测能力。为了验证这一假设，我们有以下具体目标：(1)收集500例乳腺癌病例和2000例匹配对照的病例对照数据集，这些病例对照数据具有5年的FFDM(病例组被诊断为癌症之前)。我们将把整个数据集拆分成独立的子集进行训练和验证；(2)利用先进的机器学习和计算机视觉技术设计Q-BPD，以最大限度地提高个人水平的区分力；(3)通过病例对照研究和统计分析，研究已开发的Q-BPD与乳腺癌风险的关联；以及(3)通过病例对照研究和统计分析，研究已开发的Q-BPD与乳腺癌风险的关联，例如放射科医生通过病例对照研究和统计分析对BI-RADS密度类别的估计和FFDM上的交互PD。当完全开发后，自动Q-BPD可以作为常规乳腺癌筛查的一部分。它不仅对个体患者的乳腺癌风险预测有用，而且 也用于监测由于治疗或其他因素导致的风险随时间的消退或进展。新的风险预测工具预计将在为不同风险水平的女性进行个性化乳腺癌筛查方面发挥关键作用，从而在降低医疗成本的同时使高危女性受益。该项目的成功将为未来的大规模临床试验奠定基础，以解决这些局限性，并调查拟议的Q-BPD在乳腺癌风险预测中的临床应用。关键词：定量乳腺组织分析、基于图像的生物标记物、乳腺癌风险预测、全场数字化乳房X光摄影(FFDM)

 DESCRIPTION (provided by applicant): Quantitative Parenchyma Descriptor as an Imaging Biomarker of Breast Cancer Risk Project Summary/Abstract Breast cancer remains one of the leading causes of death among women at the age of 40 and older. Mammography has been used as a low-cost screening tool for breast cancer. The recent controversy on breast cancer screening recommendations has increased public awareness and interests for informed counseling of screening and health care options based on individualized estimates of risk. The goal of this proposed project is to develop a computerized image-based biomarker to assess the breast cancer risk of individual patients in the screening population. The innovation of our approach lies in the fact that the quantitative breast parenchyma descriptor (q-BPD) will be designed to take into account not only the percentage of dense tissue (PD) but also the stromal and epithelial structural pattern of an individual's breast that is complementary to, rather than a surrogate of, the breast density. The q-BPD is obtained by a joint analysis of the complexity of the parenchymal distribution pattern (mammographic parenchymal pattern, MPP) and the amount of dense tissue (PD) as they are imaged on full-field digital mammograms (FFDMs). We hypothesize that the proposed q-BPD is an independent risk factor for breast cancer and will have a stronger predictive power than previous approaches such as PD or BI-RADS density categories alone. To test the hypothesis, we have the following specific aims: (1) to collect a matched case-control data set of 500 breast cancer cases and 2000 matched controls with 5 years of prior FFDMs (prior to cancer diagnosis for the case group). We will split the entire data set into independent subsets for training and validation; (2) to design a q- BPD by using advanced machine learning and computer vision techniques to maximize the discriminatory power at the personal level; (3) to investigate the association of developed q-BPD with breast cancer risk in comparison with commonly used density descriptors, such as radiologist's estimates of BI-RADS density categories and interactive PD on FFDMs by case-control studies and statistical analyses, taking into account other confounding risk factors. When fully developed, the automated q-BPD can be incorporated as a part of routine breast cancer screening. It will not only be useful for breast cancer risk prediction for individual patients but also for monitoring of risk regression or progression over time due to treatment or other factors. The new risk prediction tool is expected to play a key role in personalized breast cancer screening for women at different risk levels, thereby reducing health care costs while benefiting high risk women. The success of this project will lay the foundation for future large-scale clinica trials to address the limitations and investigate the clinical utilities of the proposed q-BPD for breast cancer risk prediction. Key Words: quantitative breast parenchyma analysis, image-based biomarker, breast cancer risk prediction, full-field digital mammogram (FFDM)