Quantitative diagnosis of breast cancer with ultrasound

乳腺癌的超声定量诊断

• 批准号: 8196858
• 负责人: CHANDRA M SEHGAL
• 金额: $ 31.7万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-12-01 至 2013-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8196858
• 关键词: Algorithms; Area; Benign; Biological Neural Networks; Biopsy; Breast; CH3OCF2CH(CF3)OCH2F; Cancer Etiology; Cessation of life; Characteristics; Classification; Classification Scheme; Clinical; Computer Analysis; Computers; Core Biopsy; Cyst; Data; Decision Trees; Detection; Diagnosis; Diagnostic; Evaluation; Excision biopsy; Goals; Health; Image; Image Analysis; Individual; Intraobserver Variability; Lesion; Logistic Regressions; Malignant - descriptor; Malignant Neoplasms; Mammography; Measurement; Medical; Medicine; Methods; Pathologic; Patients; Pattern; Performance; Phase; Predictive Value; Probability; Process; Radial; Receiver Operating Characteristics; Recommendation; Reproducibility; Research; Scanning; Second Opinions; Shapes; Solid; System; Techniques; Testing; Time; Tissues; Training; Ultrasonography; Visual; base; breast cancer diagnosis; breast scan; cancer diagnosis; clinical application; clinical practice; computer design; design; diagnostic accuracy; experience; imaging modality; improved; malignant breast neoplasm; novel; novel diagnostics; novel strategies; programs; radiologist; research study; success

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.

描述（由申请人提供）：本研究的目的是设计临床医生可用于补充其超声图像视觉解释的定量方法，以区分良性和恶性实性乳腺肿块。其假设是，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，某些超声特征来源于病变的边缘，形状和回声特征可以帮助区分良恶性实性肿块。在此应用中，我们建议建立在我们的初步成功，并开发一个诊断系统的超声扫描仪，提供最终用户的恶性肿瘤的概率从定量分析的乳腺超声图像的在线估计。该计划有四个具体目标。在特定目标1中，将在受控和明确定义的实验条件下采集400例患者的乳腺肿块超声图像。在具体目标2中，将开发新的方法来检测肿块边缘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定性特征也将被识别。定量和定性特征集将分别用于基于逻辑回归，神经网络和径向基函数分类器的新分类方法，以制定癌症诊断的决策树。将通过ROC分析评价每个分类方案和特征集的诊断性能。在Specific Aim 3中，定性和定量特征集将被结合，将临床医生的直观医疗经验与定量测量的精度相结合。在该计划的最后阶段，具体目标4，最佳性能的特征集和分类方案将在超声扫描仪上实现，用于恶性和良性乳腺肿块的在线诊断。该程序集成了定性临床和定量计算机方法用于乳腺癌诊断。我们希望开发一种新的诊断系统，确定恶性肿瘤的概率，临床医生可以使用它作为在线第二意见时，在乳房超声检查的性能作出诊断决定。公共卫生相关性：乳腺癌是美国癌症死亡的第二大原因。目前，超声成像用于通过视觉检查图像来诊断乳腺癌。该应用程序介绍了一种新的范例，将使用定量方法来区分恶性和良性乳腺肿块。如果成功，拟议的研究可以减少假阳性或不必要的活检数量。