Lesion Composition and Quantitative Imaging Analysis on Breast Cancer Diagnosis

乳腺癌诊断中的病灶构成和定量影像分析

• 批准号: 9438084
• 负责人: Maryellen L. Giger
• 金额: $ 6.5万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-03-06 至 2018-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9438084
• 关键词: 3-Dimensional; Algorithmic Analysis; Benign; Biological; Biological Markers; Biological Neural Networks; Biopsy; Breast; Breast Cancer Early Detection; Breast biopsy; Cancer Detection; Cancerous; Characteristics; Clinical; Complement; Computer Assisted; Contrast Media; Descriptor; Diagnosis; Diagnostic; Diagnostic Imaging; Diagnostic Sensitivity; Diagnostic Specificity; Digital Breast Tomosynthesis; Digital Computers; Digital Mammography; Effectiveness; Fibroadenoma; Goals; Health; Image; Image Analysis; Knowledge; Lead; Lesion; Lipids; Logistic Models; Logistic Regressions; Malignant - descriptor; Malignant Neoplasms; Mammography; Measurement; Measures; Methods; Mission; Modeling; Morphology; Noninfiltrating Intraductal Carcinoma; Outcome; Outcome Study; Output; Pain; Participant; Performance; Probability; Procedures; Proteins; Public Health; Recruitment Activity; Research; Research Support; Risk Factors; Risk Marker; Sensitivity and Specificity; Shapes; Specificity; System; Technology; Testing; Texture; Thick; Three-Dimensional Imaging; Tissues; United States National Institutes of Health; Water; Woman; base; breast cancer diagnosis; breast imaging; breast lesion; cancer diagnosis; cancer invasiveness; cancer risk; clinical risk; design; diagnostic accuracy; disorder prevention; human disease; imaging biomarker; improved; innovation; malignant breast neoplasm; novel; predictive modeling; programs; prospective; public health relevance; quantitative imaging; radiologist; screening; tool; validation studies

DESCRIPTION (provided by applicant): The full impacts of digital mammography and computer-aided diagnostic (CAD/QIA) systems on the performance of diagnostic mammography are yet to be realized. Lesion composition as described by its 3 compositional thicknesses of protein, lipid, and water (3CB) was recently discovered to be a strong descriptor of abnormal breast lesions. The long-term goal of this project is to reduce unnecessary breast biopsies by creating diagnostic imaging models using the strongest CAD/QIA algorithms incorporating advances such as 3CB. Our objective is to quantify lipid-protein-water signatures around CAD/QIA markers to better predict malignant findings. Our central hypothesis is that lesion composition can be combined with existing CAD/QIA methods to improve the specificity of cancer diagnosis and reduce the number of unnecessary biopsies. Our specific aims are as follows: to 1) investigate the sensitivity and specificity of localized 3CB to distinguish breast cancer from benign lesions on prospectively acquired diagnostic mammograms of women recommended to undergo biopsy, 2) compare the sensitivity and specificity of 3CB to an established CAD/QIA method and conventional morphological BI-RADS descriptors, 3) develop a predictive diagnostic model to quantify the probability mammographic findings require biopsy versus don't require biopsy based on clinical risk factors, CAD/QIA measures and 3CB measures, and secondary) investigate advantages of 3-dimensional 3CB signatures using dual-energy digital breast tomosynthesis. The working hypotheses for each are as follows: aim 1 - that unique 3CB signatures of lipid, protein, and water exist for breast cancer versus benign lesions and that this information can be used to better identify lesions that require breast biopsy aim 2 - that automated diagnostic CAD/QIA will yield quantitative lesion features that either correlate with or complement (independent) to the compositional signatures, aim 3 - that localized 3CB measures and established CAD/QIA measures are independent methods that assess different predictors of breast cancer and benign lesions and that the combination of measures from the two methods in a single model will increase the sensitivity and specificity from either one alone, secondary aim - that 3D mammography will provide more accurate lesions compositions than 2D imaging. The research's innovation is the combination of the two independent imaging risk markers: 3CB and a powerful CAD/QIA model, and compares it to the clinical standards used by radiologist. The expected outcomes include 1) a novel 3CB and CAD/QIA combined and accessible technology that will yield improved discernibility between cancerous and benign mammographic findings, 2) extensive, and biologically relevant knowledge on how lesion composition correlates with CAD/QIA findings, and 3) an demonstration of an optimized image-based predictive model for malignancy. We expected an important positive impact because more accurately identification of women with and without breast cancer will reduce the harm of unnecessary biopsies.

描述（由申请人提供）：数字乳房X线摄影和计算机辅助诊断（CAD/QIA）系统对诊断乳房X线摄影的性能的全部影响尚待实现。如其3个蛋白质，脂质和水（3CB）的3个成分厚度所描述的病变成分最近被发现是异常乳腺病变的强烈描述。该项目的长期目标是通过使用最强的CAD/QIA算法（例如3CB）创建诊断成像模型来减少不必要的乳房活检。我们的目标是量化CAD/QIA标记物周围脂质 - 蛋白质 - 水的特征，以更好地预测恶性发现。我们的中心假设是，可以将病变组成与现有的CAD/QIA方法结合使用，以改善癌症诊断的特异性并减少不必要的活检数量。 Our specific aims are as follows: to 1) investigate the sensitivity and specificity of localized 3CB to distinguish breast cancer from benign lesions on prospectively acquired diagnostic mammograms of women recommended to undergo biopsy, 2) compare the sensitivity and specificity of 3CB to an established CAD/QIA method and conventional morphological BI-RADS descriptors, 3) develop a predictive diagnostic model to quantify the probability mammographic发现需要活检与不需要基于临床风险因素，CAD/QIA测量和3CB测量以及次要的活检）研究了使用双能量数字乳房合成的3维3CB特征的优势。每个人的工作假设如下：目标1-存在脂质，蛋白质和水的独特3CB特征，存在用于乳腺癌与良性病变的脂肪，并且可以使用这些信息来更好地识别需要乳房活检目标的病变。措施和已建立的CAD/QIA度量是独立的方法，可以评估乳腺癌和良性病变的不同预测因子，并且单个模型中两种方法的措施的组合将提高单独的次要目标的敏感性和特异性 - 3D乳房摄影将提供比2D成像更准确的病变组成。该研究的创新是两个独立成像风险标记的组合：3CB和强大的CAD/QIA模型，并将其与放射科医生使用的临床标准进行了比较。预期的结果包括1）新颖的3CB和CAD/QIA组合和可访问的技术，将在癌性和良性乳腺X线摄影发现之间提高可见度，2）广泛的，以及有关病变组成与CAD/QIA的相关性与CAD/QIA发现如何相关的生物学相关知识，以及3）基于图像基于图像的预测模型的优化模型。我们期望有重要的积极影响，因为更准确地识别患有和没有乳腺癌的女性将减少不必要的活检的危害。