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，WHO宣布Covid-19被宣布为大流行。从那以后，有815万确认的案件在全球范围内，病例死亡率范围从16.3％到0.1％。在美国，有截至2020年6月16日，案件死亡率为5.4％的病例为2,187,202例。这种传染病的大小已经强调需要开发新颖的方法来定义谁是发展的最高风险急性症状。 X射线（CXR）和计算机断层扫描（CT）在检测中起着基本作用 COVID-19肺损伤的随访。它还提供了一个独特的机会来定义定量生物标志物可能会使用感染前和早期感染确定疾病急性阶段的敏感受试者放射学检查。该提议的广泛目标是更好地了解急性共同易感性标记基于CT和CXR的放射学检查的人工智能方法。 CT提供了一种独特的方式表型肺及其变化。正常实质的细微变化与可以在CT上检测到的全身性炎症。我们假设敏感受试者是急性共同的。 19疾病进化将表达可以在CT扫描上测量的正常实质特征的发炎在感染或病毒感染的早期阶段。我们将开发新的计算识别与正常实质以及早期发炎的射线照相模式的方法 COVID-19损伤和计算可以捕获射线照相的异质性的放射线学签名每个肺模式的表达。我们将定义新的基于CT的生物标志物，用于急性Covid-19易感性使用梯度提升决策树并具有重要性。然后，我们将翻译量化 CXR图像中的大多数相关特征使用基于深神经网络的图像翻译方法。最后，我们将使用临床上友好的端到端将这些自动化工具集成到CIP工作站工作流程以增强全球临床调查的能力。我们将继续支持和传播整个研究社区的工具。在过去的15年中，我们的小组开发了胸部成像平台（CIP），一种由NIH资助的开源软件工具，用于自动表型的胸部CT扫描这是在慢性肺部疾病研究界广泛使用的。自大流行开始以来已使用现有的光密度指标来表征COVID-19。我们对以自由分布的开放工具包的形式开放科学是催化应用的基础 AI和成像在这个大流行的背景下。