CT and CXR Phenotyping Platform for Assessing COVID-19 Susceptibility and Severity

用于评估 COVID-19 敏感性和严重程度的 CT 和 CXR 表型平台

基本信息

批准号：
10382425
负责人：
Raul San Jose Estepar
金额：
$ 27.25万
依托单位：
BRIGHAM AND WOMEN'S HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-04-02 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10382425
关键词：
2019-nCoV Acute Architecture Artificial Intelligence Biological Markers COVID-19 COVID-19 patient COVID-19 severity COVID-19 susceptibility Case Fatality Rates Chest Chronic Lung Injury Chronic lung disease Clinical Communicable Diseases Communities Data Decision Trees Detection Development Disease Disease susceptibility Epidemiologic Factors Evolution Funding Goals Heterogeneity Image Immune response Infection Inflammatory Injury Intensive Care Lung Machine Learning Maps Measurement Measures Methodology Methods Modality Outcome Patient Care Pattern Phase Phenotype Play Predisposition Prognostic Marker Pulmonary Inflammation Radiology Specialty Research Resolution Response Elements Roentgen Rays Role SARS-CoV-2 infection Scanning Severity of illness Smoking Software Tools Stress Structure of parenchyma of lung Techniques Technology Thoracic Radiography Training Translating United States United States National Institutes of Health Virus Virus Diseases X-Ray Computed Tomography X-Ray Medical Imaging acute care acute symptom base chest computed tomography clinical investigation clinical translation deep learning deep neural network follow-up gradient boosting high risk image translation imaging platform interest learning strategy lung injury novel open data open source pandemic disease personalized approach prognostic model prognostication radiological imaging radiomics response severe COVID-19 systemic inflammatory response therapeutic development tool

项目摘要

Abstract COVID-19 was declared a pandemic by WHO on March 11. Since then, there have been 8.15 million confirmed cases worldwide with a case fatality rate ranging from 16.3% to 0.1%. In the US, there have been 2,187,202 cases with a 5.4% case fatality rate as of June 16, 2020. The magnitude of this infectious disease has stressed the need to develop novel methodologies to define who are at the highest risk of developing acute symptoms. X-Ray (CXR) and Computed Tomography (CT) play a fundamental role in the detection and follow-up of the COVID-19 lung injury. It also provides a unique opportunity to define quantitative biomarkers that may identify susceptible subjects to the acute phase of the disease using pre-infection and early infection radiological exams. This proposal's broad objective is to provide a better understanding of acute COVID-19 susceptibility markers based on artificial intelligence approaches on radiological exams, both CT and CXR. CT offers a unique way to phenotype the lung and its changes. Subtle changes of normal parenchyma have been associated with systemic inflammation that can be detected on CT. We hypothesize that susceptible subjects for acute COVID- 19 disease evolution will express inflamed normal parenchymal signatures that can be measured on CT scan prior to the infection or in the early phases of the viral infection. We will develop new computational approaches to identify radiographic patterns consistent with inflamed normal parenchyma as well as early COVID-19 injury and compute radiomics signature that can capture the heterogeneity of the radiographic expression for each lung pattern. We will define new CT-based biomarkers for acute COVID-19 susceptibility using Gradient Boosting decision trees and feature importance. We will then translate the quantification of the most relevant features in CXR image using image translation approaches based on deep neural networks. Finally, we will integrate these automated tools in the CIP workstation using clinically friendly end-to-end workflows to empower clinical investigations across the world. We will continue the support and dissemination of this tool across the research community. Over the last 15 years, our group has developed the Chest Imaging Platform (CIP), an NIH-funded open-source software tool for the automated phenotyping of chest CT scans that is widely used in the chronic lung disease research community. Since the beginning of the pandemic, CIP has been used to the characterization of COVID-19 using existing densitometric metrics. Our commitment to open science in the form of open toolkits that are freely distributed is fundamental to catalyze the application of AI and imaging in the context of this pandemic.

抽象的 3 月 11 日，世界卫生组织宣布 COVID-19 为大流行病。此后，已有 815 万人感染全球确诊病例数为16.3%至0.1%。在美国，已经有截至 2020 年 6 月 16 日，已有 2,187,202 例病例，病死率为 5.4%。这种传染病的严重程度强调需要开发新的方法来确定哪些人面临最高的患病风险急性症状。 X 射线 (CXR) 和计算机断层扫描 (CT) 在检测和诊断中发挥着基础作用。 COVID-19 肺损伤的随访。它还提供了定义定量生物标志物的独特机会可以通过感染前和早期感染来识别疾病急性期的易感人群放射学检查。该提案的广泛目标是更好地了解急性 COVID-19 易感性标志物基于 CT 和 CXR 放射检查的人工智能方法。 CT 提供了一种独特的方法肺表型及其变化。正常实质的微妙变化与 CT 可以检测到全身炎症。我们假设急性新冠病毒易感人群 19 种疾病的演变将表现出可通过 CT 扫描测量的发炎的正常实质特征在感染之前或病毒感染的早期阶段。我们将开发新的计算识别与发炎的正常实质一致的放射照相模式以及早期的方法 COVID-19 损伤和计算放射组学特征，可以捕获放射线照相的异质性每个肺部模式的表达。我们将为急性 COVID-19 易感性定义新的基于 CT 的生物标志物使用梯度提升决策树和特征重要性。然后我们将翻译量化使用基于深度神经网络的图像转换方法来识别 CXR 图像中最相关的特征。最后，我们将使用临床友好的端到端将这些自动化工具集成到 CIP 工作站中为世界各地的临床研究提供支持的工作流程。我们将继续支持和传播该工具在整个研究界的普及。在过去 15 年里，我们的团队开发了胸部影像 Platform (CIP)，一个由 NIH 资助的开源软件工具，用于胸部 CT 扫描的自动表型分析广泛应用于慢性肺病研究界。自疫情爆发以来，CIP 已用于使用现有的密度测量指标来表征 COVID-19。我们的承诺以免费分发的开放工具包形式存在的开放科学是促进应用的基础这次大流行背景下的人工智能和成像。