Doppler Vascularity/Sonography--Breast Cancer Diagnosis

多普勒血管/超声检查--乳腺癌诊断

• 批准号: 6514660
• 负责人: CHANDRA M SEHGAL
• 金额: $ 20.09万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-03-01 至 2006-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6514660
• 关键词: bioimaging /biomedical imaging; breast neoplasm /cancer diagnosis; breast neoplasms; clinical research; computer assisted diagnosis; diagnosis design /evaluation; female; human subject; neoplasm /cancer blood supply; noninvasive diagnosis; phantom model; progesterone; ultrasound blood flow measurement; women's health

Our previous studies suggest that gray-scale ultrasound and Doppler imaging complement one another. While gray-scale imaging can characterize masses with high specificity, Doppler imaging has high sensitivity. This application proposes an innovative integration of the two modes such that both high sensitivity and specificity can be achieved. An intense 5-year multidisciplinary program encompassing three specific goals is proposed. In Aim 1 color-Doppler, power-Doppler and gray- scale images will be acquired from 300 patients with suspicious breast masses. All modes of imaging will be conducted on the same patients and on the same day. The issues related to optimal imaging will be emphasized by careful control of experimental conditions. Among other things, this will include regular image calibration and monitoring of progesterone levels in the patients to account for variations in blood flow during the normal menstrual cycle as well as for variations due to hormonal therapy. In Aim 2 we introduce new approaches for deriving quantitative features from Doppler and gray-scale images. The emphasis will be on quantifying features that physicians use in evaluating images. In Aim 3 these features will be supplemented with qualitative assessments to develop a multifactorial model for a comprehensive diagnosis of breast lesions. Novel approaches based on advanced nonlinear methods, including neural networks, will be developed to formulate a decision tree for cancer diagnosis. Each strategy will be evaluated by ROC analysis. This approach will provide objective measures to demonstrate the importance of sonographic and Doppler features and how they can be used optimally to accurately diagnose breast cancers. Taken together, this program takes advantage of unique breadth of clinical experience and basic sciences at the University of Pennsylvania. An integrated approach in which one mode represents tissue property and the other its function will result in a systematic and comprehensive diagnosis of breast cancers.

我们之前的研究表明灰度超声和多普勒成像是相辅相成的。灰度成像可以高特异性地表征肿块，而多普勒成像则具有高灵敏度。该应用提出了两种模式的创新集成，从而可以实现高灵敏度和特异性。提出了一项为期 5 年的多学科计划，涵盖三个具体目标。在目标 1 中，将从 300 名患有可疑乳房肿块的患者获取彩色多普勒、能量多普勒和灰度图像。所有模式的成像将在同一天对同一患者进行。通过仔细控制实验条件，将强调与最佳成像相关的问题。除其他外，这将包括定期图像校准和监测患者的黄体酮水平，以考虑正常月经周期期间血流的变化以及激素治疗引起的变化。在目标 2 中，我们引入了从多普勒和灰度图像中获取定量特征的新方法。重点是量化医生在评估图像时使用的特征。在目标 3 中，这些特征将辅以定性评估，以开发用于乳腺病变综合诊断的多因素模型。将开发基于先进非线性方法（包括神经网络）的新方法来制定癌症诊断的决策树。每个策略都将通过 ROC 分析进行评估。这种方法将提供客观的措施来证明超声检查和多普勒特征的重要性以及如何最佳地使用它们来准确诊断乳腺癌。总的来说，该项目利用了宾夕法尼亚大学独特的广泛临床经验和基础科学。采用一种综合方法，其中一种模式代表组织特性，另一种模式代表其功能，这将导致对乳腺癌进行系统和全面的诊断。