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志物摘要/摘要乳腺癌仍然是40岁及以上妇女死亡的主要原因之一。乳房X线摄影已被用作乳腺癌的低成本筛查工具。最近关于乳腺癌筛查建议的争议增加了公众对基于个体化风险估计的筛查和医疗保健选择的知情咨询的意识和兴趣。该项目的目标是开发一种计算机化的基于图像的生物标志物，以评估筛查人群中个体患者的乳腺癌风险。我们的方法的创新在于，定量乳腺实质描述符（q-BPD）将被设计为不仅考虑致密组织（PD）的百分比，而且考虑个体乳腺的基质和上皮结构模式，其是对乳腺实质的补充，而不是对乳腺实质的补充。 乳房密度的替代物。q-BPD是通过联合分析实质分布模式（乳腺X线实质模式，MPP）的复杂性和致密组织（PD）的数量获得的，因为它们在全视野数字乳腺X线照片（FFDM）上成像。我们假设提出的q-BPD是乳腺癌的独立危险因素，并且比以前的方法（如单独的PD或BI-RADS密度分类）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，这些病例对照数据集具有5年的既往FFDM（病例组在癌症诊断之前）。我们将整个数据集分成独立的子集进行训练和验证;（2）通过使用先进的机器学习和计算机视觉技术来设计q-BPD，以最大限度地提高个人层面的区分能力;（3）与常用的密度描述符相比，研究发展的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密度类别和FFDM交互PD的估计，同时考虑其他混杂风险因素。当完全开发时，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， 还用于监测由于治疗或其他因素而随时间推移的风险消退或进展。新的风险预测工具预计将在不同风险水平的女性的个性化乳腺癌筛查中发挥关键作用，从而降低医疗保健成本，同时使高风险女性受益。该项目的成功将为未来大规模临床试验奠定基础，以解决所提出的q-BPD用于乳腺癌风险预测的局限性和研究临床效用。关键词：定量乳腺实质分析，基于图像的生物标志物，乳腺癌风险预测，全视野数字乳腺X线摄影（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)