Development of a Machine Learning Model to Integrate Clinical, Laboratory, Sonographic, and Elastographic Data for Noninvasive Liver Tissue Characterization in NAFLD

摘要 非酒精性脂肪性肝病（NAFLD）非常常见，估计有1亿人 在美国受到折磨的人。这种非常常见的疾病的检测和风险分层仍然是一个问题。 重大挑战。尽管最近取得了进展，包括目前开发了许多治疗剂， 在2期和3期试验中，NAFLD仍然是一种沉默的疾病，其中绝大多数患者积累 进行性肝损伤，无体征或症状，未经诊断，得不到医疗护理。述NAFLD 肝硬化风险最高的患者是在诊断时具有中度或更高肝纤维化的患者， 被描述为患有高风险非酒精性脂肪性肝炎（hrNASH）的患者组。当前 鉴别hrNASH患者的参考标准是肝活检，这是昂贵的、侵入性的和有限的 通过观察者间的差异性。该项目的重点是开发和验证低成本的非侵入性诊断 诊断hrNASH的技术。我们建议在三个具体目标中实现这一目标。首先，我们将扩大 并注释现有的慢性肝病患者数据库，从328名受试者到1,000名受试者， 约40%的人将患有NAFLD。数据库将包含约20，000张图像（约10，000张超声弹性成像 图像和约10，000张常规超声图像）以及多个人口统计学和临床数据点， 每例受试者（总计约30，000个临床、实验室和人口统计学数据点）。我们先前已经 开发了先进的图像处理技术，使超声弹性成像更准确， 变量我们将使用这个大型数据库来开发，定制和完善我们的图像处理技术， NAFLD评价（目的1），旨在改善hrNASH的超声弹性成像诊断。第二、 我们将结合联合收割机传统的超声弹性成像，传统的超声成像，我们先进的 图像分析技术，以及机器学习模型中的人口统计学，临床和实验室数据， 预测hrNASH，并将我们的预测模型的性能与FIB 4进行比较，FIB 4是一种广泛使用的血液 基于测试的预测规则（目标2）。第三，我们将在一个独立的前瞻性研究中验证我们的预测模型。 接受活检以进行NAFLD风险分层的NAFLD受试者队列（目的3）。我们假设 图像处理增强弹性成像和常规超声成像的组合， 人口统计学、临床和实验室数据对hrNASH的预测能力比临床或 仅超声数据。所提出的预测模型具有以下潜力：（1）减少肝脏数量 （2）促进NAFLD治疗剂临床试验的招募，和 (3)提高美国最常见肝病的护理质量。

Abstract Non-alcoholic fatty liver disease (NAFLD) is exceptionally common, with an estimated one hundred million afflicted people in the United States. Detection and risk stratification of this very common disease remains a major challenge. Despite recent advances, including development of numerous therapeutic agents presently in phase 2 and 3 trials, NAFLD remains a silent disease in which the vast majority of patients accumulate progressive liver damage without signs or symptoms and, undiagnosed, receive no medical care. The NAFLD patients at highest risk of cirrhosis are those with moderate or greater liver fibrosis at the time of diagnosis, a group of patients who are described as having high risk non-alcoholic steatohepatitis (hrNASH). The current reference standard for identifying people with hrNASH is liver biopsy, which is expensive, invasive, and limited by interobserver variability. The focus of this project is to develop and validate low cost non-invasive diagnostic technology to diagnose hrNASH. We propose to accomplish this in three Specific Aims. First, we will expand and annotate an existing database of patients with chronic liver disease from 328 subjects to 1,000 subjects, ~40% of whom will have NAFLD. The database will contain ~20,000 images (~10,000 ultrasound elastography images and ~ 10,000 conventional ultrasound images) and multiple demographic and clinical data points for each subject (a total of ~30,000 clinical, laboratory, and demographic data points). We have previously developed advanced image processing techniques to make ultrasound elastography more accurate and less variable. We will use this large database to develop, customize and refine our image processing techniques for NAFLD evaluation (Aim 1), with the goal of improving ultrasound elastography diagnosis of hrNASH. Second, we will combine conventional ultrasound elastography imaging, conventional ultrasound imaging, our advanced image analysis techniques, and the demographic, clinical, and laboratory data in a machine learning model to predict hrNASH and will compare the performance of our predictive model with the FIB4, a widely-used blood test-based prediction rule (Aim 2). Third, we will validate our predictive model in an independent prospective cohort of NAFLD subjects undergoing biopsy for NAFLD risk stratification (Aim 3). We hypothesize that the combination of image processing-enhanced elastography and conventional ultrasound imagery combined with demographic, clinical, and laboratory data will have greater predictive power for hrNASH than clinical or sonographic data alone. The proposed predictive models have the potential to (1) reduce the number of liver biopsies performed for hrNASH detection, (2) facilitate recruitment for clinical trials of NAFLD therapeutics, and (3) improve care quality for the most common liver disease in the United States.