Doppler Vascularity/Sonography--Breast Cancer Diagnosis

多普勒血管/超声检查——乳腺癌诊断

• 批准号: 6712790
• 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/6712790
• 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.

我们以前的研究表明，灰阶超声和多普勒成像相辅相成。虽然灰度成像可以以高特异性表征肿块，但多普勒成像具有高灵敏度。本申请提出了两种模式的创新集成，使得可以实现高灵敏度和特异性。提出了一个为期5年的多学科计划，包括三个具体目标。在目标1中，将从300例可疑乳腺肿块患者中采集彩色多普勒、能量多普勒和灰度图像。所有成像模式将在同一天对相同患者进行。与最佳成像有关的问题将通过仔细控制实验条件来强调。除其他外，这将包括定期图像校准和监测患者的孕酮水平，以考虑正常月经周期期间血流的变化以及由于激素治疗引起的变化。在目标2中，我们介绍了从多普勒和灰度图像中获得定量特征的新方法。重点将是量化的功能，医生在评估图像。在目标3中，这些特征将辅以定性评估，以开发一种多因素模型，用于乳腺病变的综合诊断。将开发基于先进非线性方法（包括神经网络）的新方法，以制定用于癌症诊断的决策树。将通过ROC分析评价每种策略。这种方法将提供客观的措施，以证明超声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，该计划利用了宾夕法尼亚大学独特的临床经验和基础科学。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，将导致系统和全面的乳腺癌诊断。