Lesion Composition and Quantitative Imaging Analysis on Breast Cancer Diagnosis

乳腺癌病灶构成及影像学定量分析

• 批准号: 10316696
• 负责人: Maryellen L. Giger
• 金额: $ 69.8万
• 依托单位: UNIVERSITY OF HAWAII AT MANOA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-09 至 2026-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10316696
• 关键词: Address; Age; Benign; Biological; Biological Markers; Biopsy; Breast; Breast Cancer Detection; Breast Cancer Early Detection; Cancer Detection; Characteristics; Clinical; Communities; Complement; Computer Assisted; Contrast Media; Diagnosis; Diagnostic; Diagnostic Imaging; Diagnostic Specificity; Digital Breast Tomosynthesis; Digital Mammography; Effectiveness; Emerging Technologies; FDA approved; Goals; Health; Hormone Receptor; Image; Image Analysis; Lesion; Lipids; Machine Learning; Malignant - descriptor; Malignant Neoplasms; Mammary Gland Parenchyma; Mammography; Measures; Methods; Mission; Modeling; Morphology; Outcome; Pain; Participant; Performance; Personal Satisfaction; Probability; Procedures; Proteins; Protocols documentation; Public Health; Reader; Recommendation; Research; Research Support; Risk Factors; Sensitivity and Specificity; Specificity; System; Technology; Texture; Tissues; United States National Institutes of Health; Water; Woman; breast cancer diagnosis; breast density; breast imaging; breast lesion; cancer subtypes; cancer type; clinical risk; clinical translation; computer aided detection; contrast enhanced; deep learning; design; diagnostic accuracy; diagnostic screening; disorder prevention; experience; human disease; imaging system; improved; innovation; insight; malignant breast neoplasm; medical specialties; programs; prospective; quantitative imaging; radiologist; research clinical testing; screening; standard of care; tomosynthesis; tool

Project Summary/Abstract. Women with dense breast have not been shown to benefit by increased cancer detection of volumetric digital breast tomosynthesis (DBT) but may benefit by lower recall rates. DBT screening biopsy rates are similar to 2D digital mammography; higher for first screening exams, lower thereafter with adjustment for age and breast density. In the U.S., 71% of biopsies do not result in a breast cancer diagnosis among women ages 40-79 who undergo breast cancer screening. To address the high rate of unnecessary biopsies, an innovative way to use FDA-approved breast imaging protocols has been developed to acquire multispectral images to measure the lipid/water/protein (L/W/P) composition of suspicious breast lesions. Malignant breast tissue has unique L/W/P composition fractions when compared to normal or benign breast tissue. This proposal aims to increase biopsy yield (BI-RADS-PPV3) through combining L/W/P biological biomarkers with quantitative morphological and textural image analysis. This combination of composition and physical descriptions of suspicious breast lesions is called q3CB. The benefits of adding q3CB to the current DBT screening/diagnostic imaging paradigm, that may already include computer aided detection, is not known. This study is designed to compare the expected biopsy yield with and without q3CB in a clinical reader study and explore how q3CB may be combine with existing technologies. The central hypothesis is that biological L/W/P fractions in breast tissue in combination with analysis of morphological and textural tissue characteristics will yield significantly higher breast cancer specificity than conventional interpretation of DBT alone. The objective is to better identify suspicious breast lesions that need to be biopsied for malignancy in women currently recommended for biopsy. The long-term goal is to reduce unnecessary biopsies and increase biopsy yield. Our rationale for the proposed research is that biological L/W/P descriptions of breast lesions will lead to more specific biopsy decisions and a better understanding of cancer types. Specifically, the project aims are 1) develop q3CB lesion signatures for distinguishing breast cancer lesions from benign lesions, using 600 prospectively-acquired DBT exams of women recommended to undergo biopsy; 2) conduct a clinical reader study to compare radiologists' performance on standard-of-care FFDM or DBT without and with the inclusion of q3CB signatures; 3) Investigate the utility of q3CB lesion signatures in a screening paradigm to improve sensitivity and specificity on CADe-identified suspicious lesions in the tasks of assessing malignancy as well as in associating with their association with cancer subtypes; Exploratory) explore the added sensitivity and specificity of dual-energy DBT in phantom studies that explore lesion size, composition, and breast density. The innovation of this study is the full characterization of lipid/water/protein lesion composition with DBT and how it complements existing computer aided diagnostic programs paired with clinical radiologists providing evidence ready for clinical translation of this unique and emerging technology.

项目摘要/摘要。乳腺肿大的妇女尚未受益于癌症 检测体积数字乳房合成（DBT），但可能会受益于较低的召回率。 DBT筛选 活检率类似于2D数字乳房摄影；首次筛选考试较高，此后较低 调整年龄和乳房密度。在美国，有71％的活检不会导致乳腺癌诊断 在40-79岁的妇女中，他们接受了乳腺癌筛查。解决不必要的高率 活检是一种使用FDA批准的乳房成像协议的创新方式来获取 多光谱图像测量可疑乳房病变的脂质/水/蛋白质（L/W/P）组成。 与正常或良性乳房相比，恶性乳腺组织具有独特的L/W/P组成部分 组织。该建议旨在通过结合L/W/P生物学来提高活检产量（BI-RADS-PPV3） 具有定量形态和质地图像分析的生物标志物。构图和 可疑乳房病变的物理描述称为Q3CB。将Q3CB添加到电流的好处 DBT筛选/诊断成像范式（可能已经包括计算机辅助检测）尚不清楚。 这项研究旨在比较临床读者研究中有和没有Q3CB的预期活检产量 并探讨Q3CB如何与现有技术结合使用。中心假设是生物学 乳腺组织中的L/W/P级分与形态和质地组织的分析结合 与DBT的常规解释相比，特征将产生明显更高的乳腺癌特异性 独自的。目的是更好地识别需要进行活检的可疑乳房病变 妇女目前建议进行活检。长期目标是减少不必要的活检并增加 活检产量。我们对拟议研究的理由是，乳房病变的生物学L/W/P描述 导致更具体的活检决策和对癌症类型的更好理解。具体来说，该项目 目的是1）开发Q3CB病变特征，以区分乳腺癌病变和良性病变，并使用 600名前瞻性获得的DBT考试建议进行活检； 2）进行临床 读者的研究比较放射科医生在没有和与dbt的标准标准FFDM或DBT上的表现 包括Q3CB签名； 3）调查在筛选范式中Q3CB病变特征的实用性 在评估恶性肿瘤的任务中，提高对CADE识别的可疑病变的敏感性和特异性 以及与癌症亚型的关联；探索）探索增加的灵敏度 在探索病变大小，组成和乳房的幻影研究中双能DBT的特异性 密度。这项研究的创新是脂质/水/蛋白质病变组成的全部表征 DBT及其如何补充现有的计算机辅助诊断程序与临床放射科医生配对 为这项独特而新兴技术的临床翻译提供了证据。