Lesion Composition and Quantitative Imaging Analysis on Breast Cancer Diagnosis

描述(申请人提供)：数字乳房X光检查和计算机辅助诊断(CAD/QIA)系统对诊断性乳房X光检查性能的全面影响尚未实现。最近发现，由蛋白质、脂肪和水三种成分厚度(3CB)描述的病变成分是异常乳腺病变的一个强有力的描述。该项目的长期目标是通过使用结合3CB等先进技术的最强大的CAD/QIA算法创建诊断成像模型，从而减少不必要的乳腺活检。我们的目标是量化CAD/QIA标记物周围的脂质-蛋白质-水信号，以更好地预测恶性病变。我们的中心假设是，病变成分可以与现有的CAD/QIA方法相结合，以提高癌症诊断的特异性，减少不必要的活检数量。我们的具体目标如下：1)根据临床危险因素、CAD/QIA测量方法和3CB测量方法，探讨定位3CB对乳腺癌和乳腺良性病变鉴别诊断的敏感性和特异性；2)比较3CB与已建立的CAD/QIA方法和传统形态BI-RADS描述符的敏感性和特异性；3)开发预测诊断模型，根据临床危险因素、CAD/QIA测量和3CB测量结果，量化乳腺摄影结果需要活检和不需要活检的概率；以及)使用双能数字乳腺断层成像技术研究三维3CB信号的优势。每一项的工作假设如下：目标1--对于乳腺癌和良性病变存在独特的脂、蛋白质和水的3CB特征，并且该信息可被用来更好地识别需要乳房活检的病变目标2-自动化诊断CAD/QIA将产生与成分特征相关或互补(独立)的定量病变特征，目标3-定位的3CB测量和已建立的CAD/QIA测量是评估乳腺癌和良性病变的不同预测因素的独立方法，并且来自两种方法的测量在单个模型中的组合将增加单独一种方法的敏感性和特异性，次要目标-3D乳房X光摄影将提供比2D成像更准确的病变成分。这项研究的创新之处在于结合了两个独立的成像风险标记物：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。预期成果包括1)一种新颖的3CB和CAD/QIA相结合的可获得的技术，该技术将提高对乳腺癌和良性乳房X光检查结果的区分度；2)关于病变成分如何与CAD/QIA结果相关联的广泛的生物学相关知识；以及3)展示优化的基于图像的恶性肿瘤预测模型。我们期待着一个重要的积极影响，因为更准确地识别患有和不患有乳腺癌的女性将减少不必要的活检的危害。

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.