Liver Surface Nodularity Score as a New Noninvasive Biomarker for Chronic Viral Hepatitis

肝脏表面结节评分作为慢性病毒性肝炎的新非侵入性生物标志物

基本信息

  • 批准号:
    9136274
  • 负责人:
  • 金额:
    $ 22.49万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2016
  • 资助国家:
    美国
  • 起止时间:
    2016-07-22 至 2017-12-31
  • 项目状态:
    已结题

项目摘要

 DESCRIPTION (provided by applicant): Chronic viral hepatitis is clinically silent until development of cirrhosis and hepatic decompensation. Decompensated cirrhosis is a precursor to liver failure and is associated with increased risk of severe esophageal bleeding, significant cardiovascular events, and death. The gold standard for diagnosing cirrhosis is liver biopsy, though problems with the procedure include the invasive approach, sampling errors, inability to assess the severity of cirrhosis, and complications such as pain, bleeding, infection and rarely death. Furthermore, in patients with an initial diagnosis of compensated (early stage) cirrhosis, there are currently no validated noninvasive methods for predicting future hepatic decompensation. Therefore, there is a need for widely applicable noninvasive methods to diagnose cirrhosis and advanced liver fibrosis and to predict future risk of hepatic decompensation and death. We have developed a computer algorithm for measuring the amount of liver surface nodularity on routine computed tomography (CT) and magnetic resonance (MR) images. We have preliminary data providing evidence that liver surface nodularity is a useful quantitative imaging biomarker that can be used to diagnose and stage cirrhosis and to predict future hepatic decompensation and death. Major strengths of the technology include the ability to assess previously acquired liver CT and MR images (making it possible to conduct large-scale retrospective population studies), wide availability and frequent use of CT and MR imaging in cirrhosis, rapid processing time (<4 min), no requirement for intravenous contrast, no need for new hardware or special image acquisition procedures, and no measurement failures in >1200 unique patients. While our initial results are compelling, further refinement and validation are necessary prior to successful commercialization and clinical implementation of this technology for use by liver specialists. This proposal is designed to fill those gaps. Project Aim 1: Convert the Liver Surface Nodularity Software into a plugin for a FDA-approved image viewer. Converting the algorithm into a plugin for OsiriX, an open source downloadable viewer, will make the algorithm easier to use because of the ability to receive images directly from the clinical PACS, make the software easier to distribute, and will allow us to pursue the FDA 510(k) approval pathway for the plugin. Project Aim 2: Assess the accuracy of the liver surface nodularity (LSN) score for diagnosing cirrhosis and advanced liver fibrosis in patients who underwent CT-guided liver biopsy (N=100). This aim is intended to extend the applicability of the technology to avoid an invasive liver biopsy. Project Aim 3: Establish the necessary team and infrastructure to support a large-scale multi-institutional study designed to validate LSN score as a new noninvasive biomarker for chronic liver disease. Validation of the LSN score as a new noninvasive biomarker to diagnose cirrhosis and advanced liver fibrosis and predict future hepatic decompensation and death will advance the commercialization of this technology, ultimately leading to improved care for patients with chronic liver disease.
 描述(由申请方提供):慢性病毒性肝炎在发展为肝硬化和肝功能失代偿之前临床上无症状。失代偿性肝硬化是肝衰竭的前兆,与严重食管出血、重大心血管事件和死亡的风险增加相关。诊断肝硬化的金标准是肝活检,尽管该程序的问题包括侵入性方法,采样错误,无法评估肝硬化的严重程度,以及疼痛,出血,感染等并发症,很少死亡。此外,对于初步诊断为代偿性(早期)肝硬化的患者,目前还没有经过验证的非侵入性方法来预测未来的肝脏失代偿。因此,需要广泛适用的非侵入性方法来诊断肝硬化和晚期肝纤维化,并预测未来肝失代偿和死亡的风险。我们已经开发了一种计算机算法,用于测量常规计算机断层扫描(CT)和磁共振(MR)图像上的肝脏表面结节的数量。我们有初步的数据提供证据,肝表面结节是一个有用的定量成像生物标志物,可用于诊断和分期肝硬化,并预测未来的肝失代偿和死亡。该技术的主要优势包括能够评估先前获得的肝脏CT和MR图像(使得进行大规模回顾性人群研究成为可能)、广泛可用性和在肝硬化中频繁使用CT和MR成像、快速处理时间(<4分钟)、不需要静脉内造影剂、不需要新硬件或特殊图像采集程序,并且在>1200个独特的患者中没有测量失败。虽然我们的初步结果令人信服,但在成功商业化和临床实施这项技术供肝脏专家使用之前,还需要进一步的改进和验证。本建议旨在填补这些空白。项目目标1:将肝脏表面结节度软件转换为FDA批准的图像查看器的插件。将算法转换为OsiriX(一种开源可下载查看器)的插件,将使算法更易于使用,因为它能够直接从临床PACS接收图像,使软件更易于分发,并使我们能够为插件寻求FDA 510(k)批准途径。项目目标2:评估肝表面结节(LSN)评分诊断肝硬化和晚期肝纤维化的准确性, 接受CT引导下肝活检的患者(N=100)。该目的旨在扩展该技术的适用性,以避免侵入性肝活检。项目目标3:建立必要的团队和基础设施,以支持大规模的多机构研究,旨在验证LSN评分作为慢性肝病的新的非侵入性生物标志物。验证LSN评分作为诊断肝硬化和晚期肝纤维化并预测未来肝功能失代偿和死亡的新的非侵入性生物标志物,将推动这项技术的商业化,最终改善慢性肝病患者的护理。

项目成果

期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

数据更新时间:{{ journalArticles.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ monograph.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ sciAawards.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ conferencePapers.updateTime }}

{{ item.title }}
  • 作者:
    {{ item.author }}

数据更新时间:{{ patent.updateTime }}

Andrew Dennis Smith其他文献

Andrew Dennis Smith的其他文献

{{ item.title }}
{{ item.translation_title }}
  • DOI:
    {{ item.doi }}
  • 发表时间:
    {{ item.publish_year }}
  • 期刊:
  • 影响因子:
    {{ item.factor }}
  • 作者:
    {{ item.authors }}
  • 通讯作者:
    {{ item.author }}

{{ truncateString('Andrew Dennis Smith', 18)}}的其他基金

Human Imaging Shared Facility
人体成像共享设施
  • 批准号:
    10362788
  • 财政年份:
    1997
  • 资助金额:
    $ 22.49万
  • 项目类别:
Human Imaging Shared Facility
人体成像共享设施
  • 批准号:
    9895650
  • 财政年份:
  • 资助金额:
    $ 22.49万
  • 项目类别:

相似海外基金

CAREER: Blessing of Nonconvexity in Machine Learning - Landscape Analysis and Efficient Algorithms
职业:机器学习中非凸性的祝福 - 景观分析和高效算法
  • 批准号:
    2337776
  • 财政年份:
    2024
  • 资助金额:
    $ 22.49万
  • 项目类别:
    Continuing Grant
CAREER: From Dynamic Algorithms to Fast Optimization and Back
职业:从动态算法到快速优化并返回
  • 批准号:
    2338816
  • 财政年份:
    2024
  • 资助金额:
    $ 22.49万
  • 项目类别:
    Continuing Grant
CAREER: Structured Minimax Optimization: Theory, Algorithms, and Applications in Robust Learning
职业:结构化极小极大优化:稳健学习中的理论、算法和应用
  • 批准号:
    2338846
  • 财政年份:
    2024
  • 资助金额:
    $ 22.49万
  • 项目类别:
    Continuing Grant
CRII: SaTC: Reliable Hardware Architectures Against Side-Channel Attacks for Post-Quantum Cryptographic Algorithms
CRII:SaTC:针对后量子密码算法的侧通道攻击的可靠硬件架构
  • 批准号:
    2348261
  • 财政年份:
    2024
  • 资助金额:
    $ 22.49万
  • 项目类别:
    Standard Grant
CRII: AF: The Impact of Knowledge on the Performance of Distributed Algorithms
CRII:AF:知识对分布式算法性能的影响
  • 批准号:
    2348346
  • 财政年份:
    2024
  • 资助金额:
    $ 22.49万
  • 项目类别:
    Standard Grant
CRII: CSR: From Bloom Filters to Noise Reduction Streaming Algorithms
CRII:CSR:从布隆过滤器到降噪流算法
  • 批准号:
    2348457
  • 财政年份:
    2024
  • 资助金额:
    $ 22.49万
  • 项目类别:
    Standard Grant
EAGER: Search-Accelerated Markov Chain Monte Carlo Algorithms for Bayesian Neural Networks and Trillion-Dimensional Problems
EAGER:贝叶斯神经网络和万亿维问题的搜索加速马尔可夫链蒙特卡罗算法
  • 批准号:
    2404989
  • 财政年份:
    2024
  • 资助金额:
    $ 22.49万
  • 项目类别:
    Standard Grant
CAREER: Efficient Algorithms for Modern Computer Architecture
职业:现代计算机架构的高效算法
  • 批准号:
    2339310
  • 财政年份:
    2024
  • 资助金额:
    $ 22.49万
  • 项目类别:
    Continuing Grant
CAREER: Improving Real-world Performance of AI Biosignal Algorithms
职业:提高人工智能生物信号算法的实际性能
  • 批准号:
    2339669
  • 财政年份:
    2024
  • 资助金额:
    $ 22.49万
  • 项目类别:
    Continuing Grant
DMS-EPSRC: Asymptotic Analysis of Online Training Algorithms in Machine Learning: Recurrent, Graphical, and Deep Neural Networks
DMS-EPSRC:机器学习中在线训练算法的渐近分析:循环、图形和深度神经网络
  • 批准号:
    EP/Y029089/1
  • 财政年份:
    2024
  • 资助金额:
    $ 22.49万
  • 项目类别:
    Research Grant
{{ showInfoDetail.title }}

作者:{{ showInfoDetail.author }}

知道了