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

开发机器学习模型来整合临床、实验室、超声和弹性成像数据，用于 NAFLD 的无创性肝组织表征

• 批准号: 10321558
• 负责人: Anthony Edward Samir
• 金额: $ 44.84万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-01-20 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10321558
• 关键词: Algorithmic Analysis; Area; Biopsy; Blood Tests; Caring; Cirrhosis; Clinical; Clinical Data; Clinical Trials; Custom; Data; Databases; Detection; Development; Diagnosis; Diagnostic; Disease; Disease Outcome; Evaluation; Goals; Image; Image Analysis; Imagery; Individual; Interobserver Variability; Laboratories; Liver; Liver Fibrosis; Liver diseases; Magnetic Resonance Imaging; Measures; Medical; Methods; Modeling; Outcome; Pathology; Patient Recruitments; Patients; Performance; Persons; Pharmaceutical Preparations; Phase II/III Trial; Population; Prevalence; Primary carcinoma of the liver cells; Prospective cohort; Quality of Care; ROC Curve; Reference Standards; Research; Risk; Running; Schedule; Sensitivity and Specificity; Staging; Symptoms; Techniques; Technology; Test Result; Testing; Texture; Therapeutic; Therapeutic Agents; Therapeutic Clinical Trial; Time; Tissues; Ultrasonography; United States; Validation; Work; base; chronic liver disease; clinical care; cost; diagnostic technologies; diagnostic tool; disorder subtype; elastography; end stage liver disease; hepatocellular injury; high risk; image processing; improved; liver biopsy; liver imaging; liver injury; machine learning model; non-alcoholic fatty liver disease; nonalcoholic steatohepatitis; noninvasive diagnosis; patient population; predictive modeling; prospective; recruit; risk stratification; screening; standard of care; tool; ultrasound

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.

抽象的 非酒精性脂肪肝病 (NAFLD) 非常常见，估计有一亿人 美国受灾人民。这种非常常见的疾病的检测和风险分层仍然是一个难题 重大挑战。尽管最近取得了进展，包括目前许多治疗药物的开发 在 2 期和 3 期试验中，NAFLD 仍然是一种沉默的疾病，绝大多数患者都在这种疾病中积累 进行性肝损伤，没有体征或症状，并且未经诊断，不接受医疗护理。非酒精性脂肪肝病 肝硬化风险最高的患者是那些在诊断时患有中度或重度肝纤维化的患者， 一组被描述为患有高危非酒精性脂肪性肝炎 (hrNASH) 的患者。目前的 识别 hrNASH 患者的参考标准是肝活检，该方法昂贵、侵入性且有限 通过观察者间的变异性。该项目的重点是开发和验证低成本非侵入性诊断 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。首先，我们将扩展 并对现有的慢性肝病患者数据库进行注释，从 328 名受试者到 1,000 名受试者， 其中约 40% 的人患有 NAFLD。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）以及多个人口统计和临床数据点 每个受试者（总共约 30,000 个临床、实验室和人口统计数据点）。我们之前有过 开发了先进的图像处理技术，使超声弹性成像更加准确、更少 多变的。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 评估（目标 1），目标是改进 hrNASH 的超声弹性成像诊断。第二， 我们将结合传统的超声弹性成像、传统的超声成像、我们先进的 图像分析技术以及机器学习模型中的人口统计、临床和实验室数据 预测 hrNASH 并将我们的预测模型的性能与 FIB4（一种广泛使用的血液）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 接受活检以进行 NAFLD 风险分层的 NAFLD 受试者队列（目标 3）。我们假设 图像处理增强弹性成像与传统超声图像的结合 人口统计、临床和实验室数据对 hrNASH 的预测能力比临床或实验室数据更强 仅超声数据。所提出的预测模型有可能（1）减少肝脏数量 用于 hrNASH 检测的活检，(2) 促进 NAFLD 治疗临床试验的招募，以及 (3) 提高美国最常见肝病的护理质量。