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

基本信息

批准号：
10548504
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
项目类别：
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/10548504
关键词：
Address Aging Alcohol abuse Alcohol consumption Algorithms Artificial Intelligence Bioinformatics Biometry CD4 Lymphocyte Count Cardiovascular system Caring Cells Cirrhosis Clinical Cohort Studies Computed Tomography Scanners Computer Assisted Computerized Medical Record Data Development Diabetes Mellitus Diagnosis Dyslipidemias Electronic Health Record Epidemiology Evaluation Fatty Liver Fibrinogen Future General Population HIV HIV Infections HIV risk 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 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 base 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

项目摘要

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（肝细胞甘油三酯含量>5%）在 HIV 感染者中可能会加剧 (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（艺术）。然而，PWH 中肝脂肪变性的流行病学仍不清楚，主要是因为研究仅限于通过专门的放射线照相确定脂肪变性的小患者样本方法或肝活检。由于肝病是感染者发病和死亡的主要原因，因此确定该组肝脂肪变性的决定因素和后果至关重要。这样的研究将为干预措施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和其后果，特别是肝功能失代偿和肝细胞癌（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腹部肝脏脂肪变性的存在及其严重程度计算机断层扫描 (CT) 扫描。 8utomatic ,!:iver 8tenuation 基于感兴趣区域的测量（ALARM）是之前开发的一种深度学习工具，用于识别中度至重度肝病脂肪变性。申请人进行的初步研究表明，相比之下，ALARM 具有较高的准确性。跨多个中心和 CT 扫描仪进行手动放射科医生审查，包括退伍军人健康中心行政。为了解决现有研究的知识差距，该提案将首先建立一个队列退伍军人老龄化队列研究 (VACS) 中超过 40,000 名 PWH 和未感染 HIV 的人 (PWOH) 2002 年至 2020.VACS 是一项正在进行的全美 PWH 和 PWOH 全国前瞻性队列研究，包括访问电子健康记录数据，包括 CT 扫描的图像文件。警报工具将是应用于此放射线图像存储库，以客观地对是否存在中度到- 严重的肝脂肪变性。该研究计划的目的是： 1) 确定与 HIV 相关的特定决定因素感染者中的肝脂肪变性，2) 定义脂肪变性的传统决定因素如何因 HIV 状况而异，以及 3) 确定 PWH 中与脂肪变性相关的肝脏并发症的风险，以及该风险因 HIV 有何不同地位。这些研究的结果将为预防和减轻疾病发展的干预措施提供信息 HIV 感染者的肝脂肪变性，这将有助于降低肝脏并发症的风险并延长肝脂肪变性的时间在这个人群中生存。该项目将汇集一支由国家认可的导师团队研究人员并提供时间进行高级流行病学、生物统计学、信息学、使申请人成为一名医生所需的人工智能、肝病学和艾滋病毒医学 HIV相关肝脏疾病领域的独立研究者。