Lesion Composition and Quantitative Imaging Analysis on Breast Cancer Diagnosis

描述（由申请人提供）：数字乳腺X射线摄影和计算机辅助诊断（CAD/QIA）系统对诊断性乳腺X射线摄影性能的全面影响尚未实现。最近发现，由蛋白质、脂质和水（3CB）的3种组成厚度描述的病变组成是异常乳腺病变的强有力描述符。该项目的长期目标是通过使用最强大的CAD/QIA算法创建诊断成像模型，减少不必要的乳腺活检，该算法结合了3CB等先进技术。我们的目标是量化CAD/QIA标志物周围的脂质-蛋白质-水特征，以更好地预测恶性结果。我们的中心假设是，病变组成可以与现有的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）研究局部3CB在推荐进行活检的女性的前瞻性获得的诊断性乳房X线照片上区分乳腺癌与良性病变的灵敏度和特异性，2）将3CB的灵敏度和特异性与已建立的CAD/QIA方法和常规形态学BI-RADS描述符进行比较，3）基于临床风险因素、CAD/QIA测量和3CB测量，开发预测诊断模型以量化需要活检与不需要活检的乳腺X线摄影发现的概率，和次级）研究使用双能量数字乳腺断层合成的3维3CB签名的优点。每一项的工作假设如下：目标1 -乳腺癌与良性病变存在脂质、蛋白质和水独特3CB特征，且该信息可用于更好地识别需要乳腺活检的病变（独立于）组成签名，目标3 -使3CB措施本地化，并建立CAD/QIA测量是评估乳腺癌和良性病变的不同预测因子的独立方法，并且在单个模型中组合两种方法的测量将增加任一种方法的灵敏度和特异性。第二个目标是，3D乳房X线摄影将提供比2D成像更准确的病变成分。该研究的创新之处在于结合了两个独立的成像风险标记：3CB和一个强大的CAD/QIA模型，并将其与放射科医生使用的临床标准进行比较。预期结局包括1）一种新型3CB和CAD/QIA组合和可访问技术，将改善癌性和良性乳腺X线检查结果之间的一致性，2）关于病变组成如何与CAD/QIA结果相关的广泛的生物学相关知识，以及3）恶性肿瘤的优化图像预测模型的演示。我们期待着一个重要的积极影响，因为更准确地识别妇女与非乳腺癌将减少不必要的活检的伤害。

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.