Quantitative diagnosis of breast cancer with ultrasound

乳腺癌的超声定量诊断

• 批准号: 7742149
• 负责人: CHANDRA M SEHGAL
• 金额: $ 32.68万
• 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/7742149
• 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; Cyst; Data; Decision Trees; Detection; Diagnosis; Diagnostic; Evaluation; Excision biopsy; Goals; 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; Work; 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; public health relevance; 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）：这项研究的目的是设计定量方法，临床医生可以使用这些方法来补充其对超声图的视觉解释，以区分良性和恶性的固体乳房肿块。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的数量。我们的初步研究表明，从病变边缘，形状和回声特征得出的某些超声图特征可以帮助区分良性和恶性固体肿块。在此应用程序中，我们建议以最初的成功为基础，并在超声扫描仪上开发诊断系统，该系统通过对乳房超声图像的定量分析，为最终用户提供了在线估计恶性肿瘤的可能性。该计划具有四个具体目标。在特定的目标1中，将在受控且定义良好的实验条件下获得400例患者的乳房肿块的超声图像。在特定的目标2中，将开发新的方法来检测质量边缘并定量描述这些特征。还将确定临床医生在常规诊断中使用的群众的定性特征。定量和定性特征集将基于逻辑回归，神经网络和径向基函数分类器分别使用新的分类方法，以制定决策树以进行癌症诊断。每个分类方案和特征集的诊断性能将通过ROC分析评估。在特定的目标3中，将结合定性和定量特征集，将临床医生的直觉医学经验与定量测量的精确度相结合。在该计划的最后阶段，特定的目标4，最佳性能功能集和分类方案将在超声扫描仪上实施，用于在线诊断恶性肿瘤和良性乳房肿块。该程序集成了定性临床和定量计算机方法进行乳腺癌诊断。我们希望开发一种新的诊断系统，以决定恶性肿瘤的可能性，在乳房超声检查过程中，临床医生在做出诊断决策时可以用作在线第二意见。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，超声成像用于通过目视检查图像来诊断乳腺癌。该应用程序引入了一种新的范式，该范式将使用定量方法来区分恶性肿瘤和良性乳房。如果成功，拟议的研究可能会减少假积极或不必要的活检的数量。