Use of Deep Learning Algorithms to Enable Evaluation of the Determinants and Outcomes of Hepatic Steatosis, by HIV Status

使用深度学习算法根据 HIV 状态评估肝脂肪变性的决定因素和结果

• 批准号: 10677782
• 负责人: Jessie Torgersen
• 金额: $ 16.79万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-09 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10677782
• 关键词: Address; Aging; Alcohol abuse; Alcohol consumption; Algorithms; Artificial Intelligence; Bioinformatics; Biometry; CD4 Lymphocyte Count; Cardiovascular system; Caring; Cells; Cirrhosis; Classification; Clinical; Cohort Studies; Computed Tomography Scanners; Computer Assisted; Computerized Medical Record; Data; Development; Diabetes Mellitus; Diagnosis; Dyslipidemias; Electronic Health Record; Epidemiology; Evaluation; Fatty Liver; Future; General Population; HIV; HIV Infections; Hepatic; Hepatocyte; Hepatology; Hypertension; Image; Informatics; Intervention; Knowledge; Liver; Liver diseases; Magnetic Resonance Imaging; Manuals; Measurement; Mediating; Medicine; Mentors; Mentorship; Metabolic dysfunction; Methodology; Methods; Morbidity - disease rate; Obesity; Outcome; Outcome Study; Pathway interactions; Patients; Persons; Population; Primary carcinoma of the liver cells; Prospective, cohort study; Research; Research Personnel; Resources; Risk; Risk Factors; Risk Reduction; Sample Size; Sampling; Scanning; Science; Selection Bias; Selection for Treatments; Severities; Surrogate Markers; Time; Training; Triglycerides; United States; Veterans; Veterans Health Administration; Viral; Viral hepatitis; Viremia; Work; X-Ray Computed Tomography; abdominal CT; antiretroviral therapy; artificial intelligence method; clinical care; co-infection; cohort; deep learning; deep learning algorithm; elastography; follow-up; interest; large datasets; liver biopsy; liver inflammation; liver transplantation; mortality; multidisciplinary; novel; obesogenic; prevent; preventive intervention; radiological imaging; radiologist; repository; screening; tool

Hepatic steatosis, defined by >5% hepatocyte triglyceride content, may be potentiated in people with HIV (PWH) through viral-mediated mechanisms or metabolic dysfunction associated with antiretroviral therapy (ART). However, the epidemiology of hepatic steatosis remains unclear among PWH, primarily because studies have been limited to small patient samples that ascertained steatosis via specialized radiographic methods or liver biopsy. Since liver disease is a leading cause of morbidity and mortality among PWH, it is critically important to identify the determinants and consequences of hepatic steatosis in this group. Such studies will inform interventions and management strategies to mitigate HIV-specific steatosis mechanisms and its consequences, particularly hepatic decompensation and hepatocellular carcinoma (HCC). Recent advances in artificial intelligence have facilitated the development of automated computer-aided liver assessment to determine the presence and severity of hepatic steatosis within noncontrast abdominal computed tomography (CT) scans. The 8utomatic ,!:iver 8ttenuation Region-Of-Interest-based Measurement (ALARM) is a deep learning tool previously developed for the identification of moderate-to-severe hepatic steatosis. Preliminary studies conducted by the applicant demonstrate the high accuracy of ALARM compared to manual radiologist review across multiple centers and CT scanners, including within the Veterans Health Administration. To address the knowledge gaps of existing studies, this proposal will first establish a cohort of over 40,000 PWH and people without HIV (PWOH) in the Veterans Aging Cohort Study (VACS) who underwent noncontrast abdominal CT imaging for any indication in the context of clinical care between 2002- 2020. The VACS, an ongoing national prospective cohort study of PWH and PWOH across the United States, includes access to electronic health record data, including image files of CT scans. The ALARM tool will be applied to this repository of radiographic images to objectively classify the presence or absence of moderateto- severe hepatic steatosis. The research plan aims to: 1) identify the HIV-specific determinants associated with hepatic steatosis among PWH, 2) define how traditional determinants of steatosis differ by HIV status, and 3) determine the risk of liver complications associated with steatosis in PWH and how this risk differs by HIV status. The findings from these studies will inform interventions to prevent and mitigate the development of hepatic steatosis among persons with HIV, which will help lower the risk of liver complications and prolong survival in this population. This project will bring together a mentoring team of nationally recognized researchers and provide time for coursework and training in advanced epidemiology, biostatistics, informatics, artificial intelligence, hepatology, and HIV medicine that are needed to establish the applicant as an independent investigator in the field of HIV-related liver diseases.

肝脂肪变性（定义为肝细胞甘油三酯含量>5%）可能在HIV感染者中增强 (PWH)通过病毒介导的机制或与抗逆转录病毒治疗相关的代谢功能障碍 （ART）。然而，PWH中脂肪肝的流行病学尚不清楚，主要是因为 研究仅限于通过专门的放射学检查确定脂肪变性的小患者样本， 方法或肝活检。由于肝病是威尔斯亲王医院发病和死亡的主要原因， 确定该组中肝脂肪变性的决定因素和后果至关重要。等 研究将为干预和管理策略提供信息，以减轻艾滋病毒特异性脂肪变性机制， 其后果，特别是肝代偿失调和肝细胞癌（HCC）。 人工智能的最新进展促进了自动化计算机辅助肝脏的发展 评估以确定非造影剂腹部内肝脂肪变性的存在和严重程度 计算机断层扫描（CT）。第88章：基于艾弗衰减区域的测量 （ALARM）是一种深度学习工具，以前开发用于识别中度至重度肝损害。 脂肪变性申请人进行的初步研究证明，与 到多个中心和CT扫描仪的手动放射科医生审查，包括退伍军人健康 局为了填补现有研究的知识空白，本建议将首先建立一个队列， 在退伍军人老龄化队列研究（VACS）中， 在2002年至2010年期间，在临床护理背景下接受任何适应症的非对比腹部CT成像。 2020. VACS是一项正在美国进行的PWH和PWOH国家前瞻性队列研究， 包括访问电子健康记录数据，包括CT扫描的图像文件。报警工具将 应用于该放射线图像库，以客观地分类中度至中度的存在或不存在， 重度肝脂肪变性。该研究计划旨在：1）确定与艾滋病毒相关的特定决定因素 2）定义脂肪变性的传统决定因素如何因HIV状态而不同，以及 3)确定PWH中与脂肪变性相关的肝脏并发症的风险，以及这种风险与HIV的差异 status.这些研究的结果将为采取干预措施以预防和减轻 艾滋病毒感染者的肝脏脂肪变性，这将有助于降低肝脏并发症的风险， 在这个群体中生存。该项目将汇集一个全国公认的指导小组， 研究人员和提供时间的课程和培训，在先进的流行病学，生物统计学，信息学， 人工智能，肝病学和艾滋病毒医学，需要建立申请人作为一个 艾滋病毒相关肝病领域的独立调查员。