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

摘要 2019冠状病毒病于3月11日被世卫组织宣布为大流行。从那以后， 全球确诊病例，病死率为16.3%至0.1%。在美国， 截至2020年6月16日，2，187，202例，病死率为5.4%。这种传染病的严重性 强调有必要开发新的方法来确定谁是最高风险的发展， 急性症状。X射线（CXR）和计算机断层扫描（CT）在检测和 COVID-19肺损伤的随访。它还提供了一个独特的机会，以确定定量生物标志物 可以使用感染前和早期感染来识别对疾病急性期易感的受试者， 放射学检查。 该提案的总体目标是更好地了解急性COVID-19易感性标志物 基于人工智能方法的放射学检查，包括CT和CXR。CT提供了一种独特的方法， 表型的肺及其变化。正常软组织的细微变化与 全身性炎症，可以在CT上检测到。我们假设，急性COVID-1的易感人群- 19疾病演变将表达可在CT扫描上测量的发炎的正常实质特征 在感染之前或在病毒感染的早期阶段。我们将开发新的计算 方法来确定放射学模式符合发炎的正常实质以及早期 COVID-19损伤和计算放射组学特征，可以捕获放射成像的异质性 每个肺模式的表达。我们将定义新的基于CT的急性COVID-19易感性生物标志物 使用梯度提升决策树和特征重要性。然后，我们将翻译的量化 使用基于深度神经网络的图像翻译方法来识别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 工作流程，以支持世界各地的临床研究。我们将继续支持和传播 这个工具在整个研究界。在过去的15年里，我们的团队开发了胸部成像 平台（CIP），NIH资助的用于胸部CT扫描自动表型分析的开源软件工具 被广泛应用于慢性肺病研究领域。自疫情爆发以来， 已被用于使用现有密度度量表征COVID-19。我们致力于 以自由分发的开放工具包的形式出现的开放科学是促进 在这场大流行的背景下，人工智能和成像。

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.