Quantitative diagnosis of breast cancer with ultrasound

描述(申请人提供)：这项研究的目标是设计定量的方法，临床医生可以用来补充他们的超声图像的视觉解释，以区分良性和恶性的实质性乳腺肿块。假设是，将定量方法与临床医生对图像的评估相结合将提高诊断的准确性，并减少假阳性或不必要的活检数量。我们的初步研究表明，根据病变边缘、形状和回声特征得出的某些超声特征可以帮助鉴别良恶性实性肿块。在这项应用中，我们建议在我们最初成功的基础上开发一个基于超声扫描仪的诊断系统，该系统可以通过对乳腺超声图像的定量分析为最终用户提供恶性概率的在线估计。该计划有四个具体目标。在具体目标1中，将在受控和明确的实验条件下采集400名患者的乳腺肿块的超声图像。在具体目标2中，将开发新的方法来检测质量边际并定量描述这些特征。临床医生在常规诊断中使用的肿块的质量特征也将被识别。定量和定性特征集将分别与基于Logistic回归、神经网络和径向基函数分类器的新分类方法一起用于癌症诊断决策树。每种分类方案和特征集的诊断性能将通过ROC分析进行评估。在具体目标3中，将定性和定量特征集相结合，将临床医生的直观医学经验与定量测量的精度相结合。在项目的最后阶段，具体目标4，最佳表现的特征集和分类方案将在超声扫描仪上实施，用于在线诊断乳腺肿块的良恶性。该程序集成了定性、临床和定量计算机诊断乳腺癌的方法。我们希望开发一种新的诊断系统来确定恶性肿瘤的可能性，临床医生可以将其作为在线第二意见，在乳房超声检查期间做出诊断决定。与公共健康相关：乳腺癌是美国癌症死亡的第二大原因。目前，超声成像被用于通过肉眼检查图像来诊断乳腺癌。这一应用引入了一种新的范式，将使用定量方法来区分乳腺肿块的良恶性。如果成功，这项拟议的研究可能会减少假阳性或不必要的活检数量。

DESCRIPTION (provided by applicant): The goal of this study is to design quantitative methods that clinicians can use to supplement their visual interpretation of sonograms for differentiating benign and malignant solid breast masses. The hypothesis is that combining quantitative methods with clinicians' assessment of images will improve the accuracy of diagnosis and reduce the number of false positive or unnecessary biopsies. Our preliminary study shows that certain sonographic features derived from lesion margin, shape, and echo characteristics can help differentiate benign and malignant solid masses. In this application, we propose to build on our initial success and develop a diagnostic system on an ultrasound scanner that provides the end user with online estimates of probability of malignancy from quantitative analysis of the breast ultrasound images. The program has four specific aims. In Specific Aim 1, ultrasound images of breast masses from 400 patients will be acquired under controlled and well- defined experimental conditions. In Specific Aim 2, new approaches will be developed to detect mass margins and to describe these features quantitatively. The qualitative features of the masses that clinicians use in routine diagnosis will also be identified. The quantitative and the qualitative feature sets will be used individually with novel classification methods based on logistic regression, neural networks and radial basis function classifiers to formulate a decision tree for cancer diagnosis. The diagnostic performance of each classification scheme and feature set will be evaluated by ROC analysis. In Specific Aim 3, the qualitative and the quantitative feature sets will be combined, integrating the intuitive medical experience of the clinicians with the precision of quantitative measurements. In the final phase of the program, Specific Aim 4, the best performing feature set and classification scheme will be implemented on an ultrasound scanner for online diagnosis of malignant and benign breast masses. This program integrates qualitative clinical and quantitative computer approaches for breast cancer diagnosis. We expect to develop a new diagnostic system that determines probability of malignancy, which clinicians could use as an online second opinion when making diagnostic decisions during the performance of a breast ultrasound examination. PUBLIC HEALTH RELEVANCE: Breast cancer is the second leading cause of cancer death in the US. Currently ultrasound imaging is used for diagnosing breast cancer by visual inspection of the images. This application introduces a new paradigm that will use quantitative methods to differentiate malignant and benign breast masses. If successful, the proposed research could reduce the number of false positive or unnecessary biopsies.