Detection and Grading of Fatty and Fibrotic Liver Using Quantitative Ultrasound

使用定量超声检测脂肪肝和纤维化肝并对其进行分级

基本信息

批准号：
9142321
负责人：
Michael L. Oelze
金额：
$ 19.07万
依托单位：
UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-15 至 2018-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9142321
关键词：
Acute Affect American Biopsy Specimen Chronic Hepatitis C Cirrhosis Classification Clinical Data Detection Development Diagnosis Diet Diffuse Discriminant Analysis Disease Evaluation Fatty Liver Fatty acid glycerol esters Fibrosis Goals Gold Health Hepatic Mass Imaging Techniques Injury Investigation Laboratories Liver Liver Failure Liver Fibrosis Liver diseases Magnetic Resonance Imaging Medical Medicine Methods Modeling Normal Range Obesity Oryctolagus cuniculus Pathologist Patients Performance Polynomial Models Primary carcinoma of the liver cells Research Research Design Research Personnel Resolution Risk Roentgen Rays Role Sampling Serum Serum Markers Statistical Data Interpretation Stress Techniques Testing Text Tissues Ultrasonics Ultrasonography base biomarker panel chronic liver disease cost diagnosis standard elastography experience in vivo liver biopsy liver injury meetings non-alcoholic fatty liver novel quantitative ultrasound research study screening success tool

项目摘要

DESCRIPTION (provided by applicant): The goal of the proposed research is to quantify the contrast provided by quantitative ultrasound (QUS) interrogation techniques for classifying liver as normal, fatty, fibrotic or a combination of fatty fibrotic and to grade the degree of fat or fibrosis in the liver. Fatty liver disease is the most common cause of chronic liver disease and is increasingly leading to more severe liver conditions e.g., hepatocarcinoma, cirrhosis, or complete liver failure. It is estimated that nonalcoholic fatty liver disease (NAFLD) alone affects over 30% of Americans and with increasing problems with obesity in the U.S., NAFLD is poised to become an even more serious medical concern. At present, accurate detection and classification of steatosis (fatty liver) represents a significant medical challenge. Furthermore, the ability to differentiate fatty liver from fibrotic liver and the ability to quantify fibrosis i the liver represents an even more significant medical challenge because the fibrotic liver may be more indicative of severe liver conditions. Liver biopsy continues to be the gold standard for diagnosis of diffuse liver disease. However, there are significant limitations associated with liver biopsy and its use as a screening tool. The use of laboratory and conventional radiological tests has also been explored for the detection and quantification of diffuse liver disease. To date these techniques are also limited because these tests are not sensitive or specific enough to distinguish the grade of fatty liver disease, the grade of fibrosis, and/or are unable to distinguih between fatty and fibrotic liver. Therefore, it is of great importance to medicine to develop noninvasive techniques capable of detecting, classifying and grading normal, fatty and fibrotic liver. We have demonstrated in previous studies the ability of QUS to classify disease based on microstructural features. In addition, our preliminary data suggest that QUS can differentiate fatty liver from normal liver and grade steatosis. Based on this evidence, we believe QUS will provide capabilities that can fill an important and missing role in noninvasive detection and quantification of diffuse liver disease. To accomplish this objective, two specific aims are proposed. The first specific aim is to quantify contrast provided by QUS to noninvasively classify and grade both fatty liver and fibrotic liver in a rabbit model. This aim will be successfully completed by applying advanced QUS interrogation techniques in vivo to normal, fatty, and fibrotic livers produced in rabbit models of diffuse liver disease. Specifically, through a 5 by 5 factorial study design we will explore the ability of QUS to detect and grade normal, fatty, and fibrotic liver alone and the combined effects of fat and fibrosis on liver disease. The second specific aim is to provide statistical analysis of the QUS parameters provided in specific aim 1 for classification and grading of diffuse liver disease. This specific aim will be met by combining the QUS parameters acquired from the experiments conducted in the first specific aim in aggregate in normal, fatty and fibrotic liver to create a multiparameter classifier using linear discriminant analysis. A polynomial constraints method will be used to quantify the progression of combined diet and fibrotic injury on liver state.

描述（由申请人提供）：拟议研究的目标是量化定量超声（QUS）询问技术提供的对比度，用于将肝脏分类为正常、脂肪、纤维化或脂肪纤维化的组合，并对脂肪或脂肪的程度进行分级肝脏纤维化是慢性肝病的最常见原因。越来越多地导致更严重的肝脏疾病，例如肝癌、肝硬化或完全肝衰竭。据估计，非酒精性脂肪肝病（NAFLD）单独影响。超过 30% 的美国人患有肥胖症，并且随着美国肥胖问题的日益严重，NAFLD 有望成为一个更加严重的医学问题。目前，准确检测和分类脂肪变性（脂肪肝）是一项重大的医学挑战。区分脂肪肝和纤维化肝脏以及量化肝脏纤维化的能力是一个更重大的医学挑战，因为纤维化肝脏可能更能指示严重的肝脏疾病。肝活检仍然是诊断的金标准。然而，肝活检及其作为筛查工具的使用也存在显着的局限性，迄今为止，这些技术也已被探索用于检测和量化弥漫性肝病。也受到限制，因为这些测试不够灵敏或特异，无法区分脂肪肝疾病的级别、纤维化的级别，和/或无法区分脂肪肝和纤维化肝。因此，开发无创性对医学来说非常重要。能够检测的技术，我们在之前的研究中已经证明了 QUS 根据微观结构特征对疾病进行分类和分级的能力，此外，我们的初步数据表明 QUS 可以区分脂肪肝和正常肝脏并根据脂肪变性进行分级。根据这一证据，我们相信 QUS 将提供能够填补弥漫性肝病的无创检测和量化方面的重要且缺失的作用的能力。为了实现这一目标，我们提出了两个具体目标：第一个具体目标是量化 QUS 提供的对比。通过将先进的 QUS 体内检测技术应用于弥漫性肝病兔模型中产生的正常、脂肪和纤维化肝脏，对兔模型中的脂肪肝和纤维化肝脏进行无创分类和分级。通过 5 x 5 析因研究设计，我们将探讨 QUS 单独检测和分级正常、脂肪和纤维化肝脏的能力，以及脂肪和纤维化对肝病的综合影响。第二个具体目标是提供 QUS 的统计分析。具体目标 1 中提供的用于弥漫性肝病分类和分级的参数将通过结合来实现。从第一个具体目标中进行的实验中获得的 QUS 参数将在正常肝脏、脂肪肝脏和纤维化肝脏中进行聚合，以使用线性判别分析创建多参数分类器，将使用多项式约束方法来量化饮食和纤维化损伤相结合的进展。肝脏状态。

项目成果

期刊论文数量（5）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Effects of the container on structure function with impedance map analysis of dense scattering media.

密集散射介质阻抗图分析容器对结构功能的影响。

DOI：
10.1121/1.5031124
发表时间：
2018
期刊：
The Journal of the Acoustical Society of America
影响因子：
0
作者：
Luchies,AdamC;Oelze,MichaelL
通讯作者：
Oelze,MichaelL

Improving Spatial Resolution Using Incoherent Subtraction of Receive Beams Having Different Apodizations.

DOI：
10.1109/tuffc.2018.2876285
发表时间：
2019-01
期刊：
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
影响因子：
0
作者：
Agarwal A;Reeg J;Podkowa AS;Oelze ML
通讯作者：
Oelze ML

Use of a convolutional neural network and quantitative ultrasound for diagnosis of fatty liver.

DOI：
10.1016/j.ultrasmedbio.2020.10.025
发表时间：
2021-03
期刊：
Ultrasound in medicine & biology
影响因子：
2.9
作者：
Nguyen TN;Podkowa AS;Park TH;Miller RJ;Do MN;Oelze ML
通讯作者：
Oelze ML

Estimation of Backscatter Coefficients Using an In Situ Calibration Source.

使用原位校准源估计反向散射系数。

DOI：
10.1109/tuffc.2019.2944305
发表时间：
2020
期刊：
IEEE transactions on ultrasonics, ferroelectrics, and frequency control
影响因子：
0
作者：
Nguyen,TrongN;Tam,AlexJ;Do,MinhN;Oelze,MichaelL
通讯作者：
Oelze,MichaelL

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Michael L. Oelze其他文献

Detection and localization of small metastatic foci in human lymph nodes using three-dimensional high-frequency quantitative ultrasound methods

使用三维高频定量超声方法检测和定位人体淋巴结中的小转移灶

DOI：
发表时间：
2011
期刊：
影响因子：
0
作者：
Jonathan Mamou;Emi Saegusa-Beecroft;Alain Coron;Michael L. Oelze;Masaki Hata;Junji Machi;Eugene Yanagihara;Pascal Laugier;Tadashi Yamaguchi;Ernest J. Feleppa
通讯作者：
Ernest J. Feleppa