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Tumor Detection and Classification using QUS Technology's Structure Function

Tumor Detection and Classification using QUS Technology's Structure Function
使用QUS技术的结构功能进行肿瘤检测和分类
批准号:
10381481
负责人:
William D. O'Brien
金额:
$46.74万
依托单位国家:
美国
项目类别:
财政年份:
2019
资助国家:
美国
项目状态:
未结题
起止时间:
2019-04-10 至 2025-03-31

项目摘要

项目成果

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中文摘要
翻译
项目摘要 我们的合作基础科学和临床团队将阐明超声散射的机制(S)在 通过系统研究超声散射的一个重要组成部分--生物组织的结构 功能(SF),使用来自动物模型的实体瘤和成人肝细胞癌(HCC) 从而提高定量超声(QUS,组织的量化)的准确性 微结构)技术在非侵入性肿瘤检测和分类中的应用。科学基金的概念产生于 波散射的背景:一组散射体的散射能力不仅取决于 单个散射体的属性(由形状系数建模),还取决于空间相关性 散射体(由SF建模)。 结构函数是新的:SF在很大程度上被忽略了,其中散射体的位置是 假设是不相关的(即,SF是单位)。单位SF假设不适用于稠密 散布介质(如实体瘤和大多数实质组织)或稀疏介质显示特殊 散射体的分布模式。非单位散射场的研究大多是在简单的散射介质上进行的,例如 身体上的幻影和血液。这项研究是新的,有许多基础科学和技术 坚持创新。我们的目标是利用动物实体瘤和人类系统地研究SF 肝脏和肝细胞癌超声散射模型的改进及最终QUS的诊断价值 结果。这项研究在基础科学水平上为阐明散射做出了贡献 通过体内动物肿瘤模型开发和验证SF模型的机制,以及在 翻译水平,以提高使用人类肝脏和肝细胞癌数据进行肿瘤检测/分类的准确性。 中心假设和目标:1)SF是阐明超声散射机制的关键(S) 在生物组织中。2)SF对某些疾病类型和阶段敏感。3)QUS to的准确性 使用SF将显著提高体内组织/肿瘤的非侵入性检测/分类 与它不被利用时相比。为了检验这些假设,我们设计了一个研究计划, 以下目标:目标1.建立与散射体空间分布相匹配的理论SF模型 正在调查的肿瘤/组织类型。目的2.用小鼠/大鼠实体瘤验证SF模型。目标3. 应用100例非酒精性脂肪性肝病的临床资料检验SF的诊断价值 非酒精性脂肪肝(NAFLD)患者,150名肝硬变患者,50名肝细胞癌患者和150名正常参与者。 摘要/影响:SF是一种超声回波成分,由其确定并对其敏感 组织微结构的结构模式(例如,肝细胞核)。SF将具有临床价值 用于疾病诊断的成像生物标记物,因为许多疾病过程(例如,肝细胞癌)显著改变 从而改变从超声信号导出的SF。
英文摘要
Project Summary Our collaborative basic science and clinical team will elucidate the mechanism(s) of ultrasound scattering in biological tissues by systematically studying a significant component of ultrasonic scattering, the structure function (SF), using solid tumors from animal models and hepatocellular carcinoma (HCC) in adult human subjects, and thereby improve the accuracy of Quantitative Ultrasound (QUS, the quantification of tissue microstructure) techniques in noninvasive tumor detection and classification. The SF concept arises in the context of wave scattering: the scattered power of a collection of scatterers depends not only on the properties of the individual scatterers (modeled by the form factor), but also on the spatial correlation among the scatterers (modeled by the SF). Structure function is new: The SF has largely been overlooked wherein the scatterer positions are assumed to be uncorrelated (i.e., SF is unity). The unity SF assumption is not appropriate for dense scattering media (e.g., solid tumors and most parenchymal tissues) or sparse media showing special patterns of scatterer distribution. Non-unity SF has mostly been studied on simple scattering media such as physical phantoms and blood. This investigation is new, and numerous basic science and technical innovations will be pursued. Our goal is to systematically study the SF using animal solid tumors and human liver and HCC data to improve ultrasonic scattering models and ultimately the diagnostic value of QUS outcomes. The research makes contributions at the basic science level to elucidate the scattering mechanisms by SF model development and validate using animal tumor models in vivo, and at the translational level to improve the accuracy of tumor detection/classification using human liver and HCC data. Central hypotheses and aims: 1) The SF is critical to elucidate ultrasonic scattering mechanism(s) in biological tissues. 2) The SF is sensitive to certain disease types and stages. 3) The accuracy of QUS to noninvasively detect/classify tissues/tumors in vivo will be significantly improved when the SF is utilized compared to when it is not utilized. To test these hypotheses, we have designed a research program with the following aims: Aim 1. Develop theoretical SF models that match scatterer spatial distributions of tumor/tissue types under investigation. Aim 2. Validate SF models using solid tumors in mice/rats. Aim 3. Test the diagnostic value of SF using clinical human liver data from 100 nonalcoholic fatty liver disease (NAFLD) participants, 150 cirrhotic participants, 50 HCC participants, and 150 normal participants. Summary/Impact: The SF is an ultrasound echo component that is determined by and sensitive to the architectural pattern of tissue microstructure (e.g., liver cell nuclei). The SF will be a clinically valuable imaging biomarker for disease diagnosis because many disease processes (e.g., HCC) remarkably change to a unique architectural pattern and consequently change the SF derived from the ultrasound signal.
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Tumor Detection and Classification using QUS Technology's Structure Function
Tumor Detection and Classification using QUS Technology's Structure Function
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