Detection and Grading of Fatty and Fibrotic Liver Using Quantitative Ultrasound

 描述(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，将肝脏分为正常、脂肪、纤维化或脂肪纤维化的组合，并对肝脏的脂肪或纤维化程度进行分级。脂肪肝是慢性肝病最常见的原因， 越来越多地导致更严重的肝脏疾病，例如肝癌、肝硬变或完全肝功能衰竭。据估计，仅非酒精性脂肪性肝病(NAFLD)就会影响 超过30%的美国人和美国日益严重的肥胖问题，NAFLD将成为一个更严重的医学问题。目前，脂肪变性(脂肪肝)的准确检测和分类是一个重大的医学挑战。此外，区分脂肪肝和肝纤维化的能力和量化肝纤维化的能力是一个更重大的医学挑战，因为肝纤维化可能更能反映严重的肝脏状况。肝活检仍然是诊断的金标准。 弥漫性肝病的症状。然而，肝活检及其作为筛查工具的使用有很大的局限性。还探索了实验室和常规放射检查的使用，以检测和量化弥漫性肝病。到目前为止，这些技术也是有限的，因为这些测试不够敏感或足够特异，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性肝。因此，发展能够对正常肝、脂肪肝和肝纤维化进行检测、分类和分级的非侵入性技术对医学具有重要意义。我们在以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，并可以分级脂肪变性。基于这一证据，我们相信QUS将提供在弥漫性肝病的非侵入性检测和量化方面填补重要和缺失的角色的能力。为了实现这一目标，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，以无创地对兔模型中的脂肪肝和纤维性肝进行分类和分级。这一目标将通过在体内将先进的QUS询问技术应用于在兔弥漫性肝病模型中产生的正常、脂肪和纤维化肝脏来成功实现。具体地说，通过5x5因素研究设计，我们将探索QUS单独检测正常、脂肪和纤维化肝脏并对其分级的能力，以及脂肪和纤维化对肝脏疾病的联合影响。第二个具体目标是为弥漫性肝病的分类和分级提供特定目标1中提供的QUS参数的统计分析。这一具体目标将通过以下方式实现 从实验中获得的QUS参数在第一个特定目标中聚合在正常肝、脂肪肝和肝纤维化中，使用线性判别分析创建多参数分类器。多项式约束方法将被用来量化联合饮食和肝纤维化损伤对肝脏状态的进展。

 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.