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: 9321215
• 负责人: Jun Wei
• 金额: $ 27.32万
• 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/9321215
• 关键词: Address; Age; Area; Awareness; Biological Markers; Breast; Breast Cancer Detection; Breast Cancer Early Detection; Breast Cancer Risk Assessment Tool; Breast Cancer Risk Factor; Breast Cancer Treatment; Cancer Patient; Case-Control Studies; Categories; Cause of Death; Characteristics; Clinical; Clinical Trials; Collection; Computer Vision Systems; Computer software; Contralateral; Counseling; 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; Individual; Intervention; 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; Statistical Data Interpretation; Stratification; Structure; Techniques; Testing; Time; Tissues; Training; Treatment Protocols; Validation; Visual; Woman; base; breast cancer diagnosis; breast density; cancer biomarkers; cancer diagnosis; cancer risk; case control; compare effectiveness; computerized; cost; cost effective; data modeling; 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（由申请人提供）：作为乳腺癌风险影像生物标志物的定量实质描述符项目摘要/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线照相术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提高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认识和兴趣。该项目的目标是开发一种基于计算机图像的生物标志物，以评估筛查人群中个体患者的乳腺癌风险。我们方法的创新在于，定量乳房实质描述符（q-BPD）的设计不仅要考虑致密组织（PD）的百分比，还要考虑个体乳房的间质和上皮结构模式，该模式是互补的，而不是单一的。 乳房密度的替代物。 q-BPD 是通过对在全场数字乳房 X 光照片 (FFDM) 上成像时的实质分布模式（乳房 X 线摄影实质模式，MPP）的复杂性和致密组织 (PD) 的数量进行联合分析来获得的。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（例如单独的 PD 或 BI-RADS 密度类别）具有更强的预测能力。为了检验这一假设，我们有以下具体目标：（1）收集 500 例乳腺癌病例和 2000 例具有 5 年 FFDM 史（病例组癌症诊断之前）的匹配对照数据集。我们将整个数据集分割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层面的歧视力； (3) 与常用的密度描述符（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 上的交互式 PD 的估计）进行比较，研究发达的 q-BPD 与乳腺癌风险的关联，同时考虑到其他混杂的风险因素。完全开发后，自动化 q-BPD 可以纳入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，而且对 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针对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，从而降低医疗成本，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 的局限性并研究其在乳腺癌风险预测中的临床效用。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, 全视野数字乳房X光检查(FFDM)