Quantitative Parenchyma Descriptor as an Imaging Biomarker of Breast Cancer Risk

定量实质描述符作为乳腺癌风险的影像生物标志物

• 批准号: 9110921
• 负责人: Jun Wei
• 金额: $ 37.78万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-01 至 2020-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9110921
• 关键词: Accounting; Address; Age; Area; Awareness; Biological Markers; Breast; Breast Cancer Detection; Breast Cancer Early Detection; Breast Cancer Patient; Breast Cancer Risk Assessment Tool; Breast Cancer Risk Factor; Cancer Patient; Case-Control Studies; Categories; Cause of Death; Characteristics; Clinical; Clinical Trials; Collection; Computer Vision Systems; Computer software; Contralateral; Counseling; Data; Data Set; Databases; Descriptor; Development; Drug or chemical Tissue Distribution; Effectiveness; Epithelial; Ethnic Origin; Exhibits; Foundations; Future; Goals; Health Care Costs; Healthcare; High Risk Woman; Image; Image Analysis; Individual; Joints; Machine Learning; Malignant Neoplasms; Mammography; Measures; Methods; Modeling; Monitor; Patients; Pattern; Performance; Play; Population; Preventive; Preventive treatment; Race; Recommendation; Risk; Risk Assessment; Risk Estimate; Risk Factors; Risk Management; Stratification; Structure; Techniques; Testing; Time; Tissues; Training; Treatment Protocols; Validation; Visual; Woman; abstracting; base; breast cancer diagnosis; breast density; cancer diagnosis; cancer risk; case control; compare effectiveness; computerized; cost; cost effective; density; design; digital; follow-up; imaging biomarker; improved; individual patient; innovation; interest; malignant breast neoplasm; personalized medicine; predictive modeling; public health relevance; radiologist; research and development; screening; statistics; success; tool; tumor

 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)

 描述（由适用提供）：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/抽象乳腺癌仍然是40岁及以上妇女的主要死亡原因之一。乳房X线摄影已被用作乳腺癌的低成本筛查工具。关于乳腺癌筛查建议的最新争议提高了公众意识和利益，以根据个性化的风险估计来了解筛查和医疗保健选择的知情咨询。该拟议项目的目的是开发基于计算机的基于图像的生物标志物，以评估筛查人群中个别患者的乳腺癌风险。我们方法的创新在于一个事实，即定量乳腺实质描述符（Q-BPD）将被设计为不仅要考虑到密集组织（PD）的百分比，而且还要考虑到个人乳房的基质和上皮结构模式，而不是完整的乳房 代理，乳房密度。 Q-BPD是通过对副型分布模式（乳腺X线副副标模式，MPP）的复杂性和密集组织（PD）的量的复杂性进行的联合分析而获得的，因为它们是在全场数字乳房X线照片（FFDMS）上成像的。我们假设所提出的Q-BPD是乳腺癌的独立危险因素，并且比以前的方法（例如PD或BI-RADS密度类别）具有更强的预测能力。为了检验假设，我们具有以下具体目的：（1）收集500例乳腺癌病例的匹配病例对照数据集，并具有5​​年的先前FFDMS（病例组的癌症诊断之前）匹配的对照组。我们将将整个数据集分为独立的子集进行培训和验证； （2）通过使用高级机器学习和计算机视觉技术来设计Q-bpd，以最大程度地发挥个人水平的歧视能力； （3）与常用密度描述符相比，研究了开发的Q-BPD与乳腺癌风险的关联，例如放射科医生对BI-RADS密度类别的估计以及通过病例对照研究和统计分析的FFDMS对FFDMS的互动PD的估计，并考虑了其他混淆风险因素。完全开发后，可以将自动化的Q-BPD作为常规乳腺癌筛查的一部分合并。它不仅对个别患者的乳腺癌风险预测有用，而且对 还用于监测由于治疗或其他因素而导致的风险回归或进展。预计新的风险预测工具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的大规模临床试验奠定基础，以解决局限性并研究拟议的Q-BPD乳腺癌风险预测的临床实用性。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，全场数字乳房X线照片（FFDM）