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)异常常见，估计有1亿人 受苦受难的美国人。这种非常常见的疾病的检测和风险分层仍然是 重大挑战。尽管最近取得了进展，包括目前正在开发的许多治疗剂 2期和3期试验，NAFLD仍然是一种沉默的疾病，绝大多数患者在其中积累 进行性肝损害，无体征或症状，未确诊，不接受医疗护理。NAFLD 肝硬变风险最高的患者是那些在诊断时有中度或更重度肝纤维化的患者，a 一组被描述为高危非酒精性脂肪性肝炎(HrNASH)的患者。海流 确定hrNASH患者的参考标准是肝活检，这是昂贵的、有创的和有限的。 通过观察者间的可变性。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。我们建议通过三个具体目标来实现这一目标。一是做大做强 并将现有的慢性肝病患者数据库从328名受试者注释到1000名受试者， 约40%的人将患有非酒精性脂肪肝。该数据库将包含约20,000张图像(约10,000张超声弹性成像 图像和约10,000个常规超声图像)和多个人口统计学和临床数据点 每个受试者(总共约30,000个临床、实验室和人口学数据点)。我们之前已经 开发了先进的图像处理技术，使超声弹性成像更准确、更少 变量。我们将使用这个大型数据库来开发、定制和改进我们的图像处理技术 NAFLD评估(目标1)，目的是改善超声弹性成像对hrNASH的诊断。第二, 我们将结合传统的超声弹性成像，传统的超声成像，我们的先进 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广泛使用的血液FIB4进行比较 基于测试的预测规则(目标2)。第三，我们将在独立的前瞻性中验证我们的预测模型 非酒精性脂肪肝患者接受活检进行非酒精性脂肪肝风险分层的队列(目标3)。我们假设 图像处理增强弹性成像与常规超声成像相结合 人口学、临床和实验室数据对hrNASH的预测能力将比临床或 单单是超声波数据。所提出的预测模型有可能(1)减少肝脏的数量 为检测hrNASH而进行的活组织检查，(2)促进招募非酒精性脂肪肝治疗的临床试验，以及 (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.