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Risk Stratification for and Early Detection of Liver Cancer

肝癌的风险分层和早期发现

基本信息

批准号：
10736168
负责人：
Jagpreet Chhatwal
金额：
$ 96.3万
依托单位：
BAYLOR COLLEGE OF MEDICINE
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-09-13 至 2028-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10736168
关键词：
AFP gene Abdomen African American population Algorithms Applications Grants Artificial Intelligence Behavioral Biological Biological Markers Biological Specimen Banks Blinded Blood Cancer Detection Center for Translational Science Activities Cirrhosis Clinical Clinical Data Clinical/Radiologic Collaborations Costs and Benefits DNA Data Decision Analysis Development Dimensions Disease Early Detection Research Network Early Diagnosis Enrollment Ensure Equilibrium Evaluation Fatty acid glycerol esters Fibrosis Funding Future Genetic Markers Goals Guidelines Healthcare Systems Hepatitis C Hepatitis C virus Hispanic Populations Image Infrastructure Institution Liver Liver Fibrosis Liver diseases Magnetic Resonance Imaging Malignant Neoplasms Malignant neoplasm of liver Maps Metabolic Marker Methods Modality Modeling Morbidity - disease rate Nodule Patients Pattern Performance Phase Portal Hypertension Primary carcinoma of the liver cells Productivity Prospective cohort Reporting Research Research Personnel Resources Risk Risk Assessment Risk Factors Sampling Science Screening for Hepatocellular Cancer Screening for cancer Serum Severities Site Societies Source Spleen Subgroup Surveys Testing Texas Underrepresented Populations Visceral fat Visit Work biobank biomarker panel biomarker validation blood-based biomarker cancer biomarkers clinical decision-making clinical practice cohort comparative cost cost effectiveness design detection sensitivity early detection biomarkers ethnic diversity follow-up high risk image archival system imaging biomarker improved innovation liver function liver imaging machine learning pipeline mathematical model mortality multidisciplinary muscle form neoplasm registry non-alcoholic fatty liver disease novel phase 3 study programs prospective racial diversity radiomics recruit risk stratification sample collection screening subcutaneous surveillance strategy tool translational approach translational scientist ultrasound virtual

项目摘要

The Translational Research Center (TRC) includes a diverse team of clinical and translational researchers with a strong track record of working together to reduce hepatocellular cancer (HCC). The TRC uses data from a unique group of patients with cirrhosis who are being recruited from eight different sites throughout Texas (the TRC cohort). Our TRC cohort currently includes > 3700 patients with cirrhosis (>10,000 follow up surveillance visits and >190 HCC cases) who have diverse risk factors, including cured HCV and non-alcoholic fatty liver disease. These patients are under routine HCC surveillance and their biological samples and clinical and radiological data for each visit is stored; it is a valuable resource for our research and other Consortium projects. We also launched a separate prospective cohort of patients with indeterminate liver nodules, which is a high risk and high priority group for HCC risk stratification and early detection. We are collecting surveys, clinical data, images and biospecimens from this cohort. The catalytic effects of our TRC are seen in the productivity of our junior investigators, who started innovative new initiatives and programs. Our work had a broad impact, including helping develop various society guidelines on HCC surveillance. Using data and the strong network of investigators and institutions collaborating with our TRC, we have already generated important data on risk factors and risk stratification for HCC. We also developed and demonstrated the framework for adding patient- and liver disease-related factors to new blood-biomarker profiles to improve early HCC detection. Here, we propose to build a novel imaging repository, including MRI images from our cohort. We will annotate and incorporate data from abdominal ultrasound reports for our TRC cohort using novel, scalable machine learning pipelines. We will also strengthen our data by appending information from the State cancer registries. Using data from different sources available to us, we will develop and test new personalized methods to stratify risk that blends information from clinical factors, blood-based biomarkers, and imaging (radiomics) to predict future development of HCC in patients with cirrhosis across diverse risk factors (Aim 1). We showed that the HCC Early Detection Screening (HES) algorithm, when combined with HCC blood-based biomarkers (AFP‐L3, DCP) in HES version 2.0 (HES v2.0), substantially improved early detection. We will validate and compare HES v2.0 with GALAD, another early detection algorithm, and evaluate their performance versus the current standard ultrasound-based surveillance (Aim 2). In Aim 3, we will develop a new mathematical model to look at how useful current and emerging biomarkers are for detecting HCC. We will also assess the risks, costs, and benefits of HCC surveillance using different cutoffs of existing and novel blood and imaging biomarkers. We will develop and disseminate an online Simulator so that other investigators can evaluate the potential clinical utility of HCC early detection biomarkers as potential surveillance tools. Our translational approach will have both an immediate and long-lasting impact on HCC-related morbidity and mortality.

转化研究中心（TRC）包括一个由临床和转化研究人员组成的多元化团队，共同努力减少肝细胞癌（HCC）的良好记录。真相与和解委员会使用来自一组独特的肝硬化患者，他们从德克萨斯州的八个不同地点招募（ TRC群组）。我们的TRC队列目前包括> 3700例肝硬化患者（> 10，000例随访监测随访和>190例HCC病例），具有不同的风险因素，包括治愈的HCV和非酒精性脂肪肝疾病这些患者接受常规HCC监测，其生物学样品和临床和每次访问的放射性数据都被存储起来;这是我们研究和其他联盟项目的宝贵资源。我们还启动了一项单独的前瞻性队列研究，研究对象为不确定性肝结节患者，肝癌危险分层和早期发现的高优先级组。我们正在收集调查，临床数据，图像和生物样本我们的TRC的催化作用体现在我们的生产力中。初级调查员，他们开始了创新的新举措和计划。我们的工作产生了广泛的影响，包括帮助制定有关肝癌监测的各种社会指南。利用数据和强大的网络，与我们的TRC合作的调查人员和机构，我们已经产生了关于风险的重要数据， HCC的危险因素和危险分层。我们还开发并演示了添加患者的框架- 和肝脏疾病相关因素的新的血液生物标志物谱，以提高早期肝癌检测。这里我们我建议建立一个新的成像库，包括我们队列的MRI图像。我们将进行注释，使用新型可扩展的机器学习将来自腹部超声报告的数据纳入我们的TRC队列管道我们还将通过补充国家癌症登记处的信息来加强我们的数据。使用数据我们将开发和测试新的个性化方法来对风险进行分层，融合来自临床因素、血液生物标志物和成像（放射组学）的信息，肝硬化患者中HCC的发展跨越不同的风险因素（目的1）。我们发现肝细胞癌早期检测筛查（HES）算法，当与HCC血液生物标志物（AFP-L3，DCP）结合时在HES版本2.0（HES v2.0）中，大大改进了早期检测。我们将验证和比较HES v2.0 与GALAD，另一种早期检测算法，并评估其性能与当前标准超声监测（目标2）。在目标3中，我们将开发一个新的数学模型来研究如何有用的当前和新兴的生物标志物用于检测HCC。我们还将评估风险、成本和收益使用现有的和新的血液和成像生物标志物的不同截止值进行HCC监测。我们将开发并传播在线模拟器，以便其他研究人员可以评估HCC的潜在临床效用早期检测生物标志物作为潜在的监测工具。我们的翻译方法将立即并对HCC相关的发病率和死亡率产生长期影响。