Computed Tomography for COVID-19 Diagnosis

Study Overview
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Brief Summary
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The purpose of this study is to build a large dataset of Computed Tomography (CT) images for identification of accurate CT criteria and development of deep learning-based solutions for diagnosis, quantification and prognostic estimation.

Detailed Description
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The outbreak of the novel coronavirus SARS-CoV-2, initially epicentred in China and responsible for COVID-19 pneumonia has now spread to France, with 7730 confirmed cases and 175 deaths as on March 17th. Diagnosis relies on the identification of viral RNA by reverse-transcription polymerase chain reaction (RT-PCR), but its positivity can be delayed. A series based on 1014 chinese patients reported higher sensitivity for CT, with a mean interval time between the initial negative to positive RT-PCR results of 5.1 ± 1.5 days (PMID: 32101510). Moreover, obtaining RT-PCR results requires several hours, which is problematic for patients triage. Chest CT can allow early depiction of COVID-19, especially when performed more than 3 days after symptoms onset. It is important to distinguish between COVID-19 and bacterial causes of pulmonary infection, which requires expertise in thoracic imaging. Thus, it is important to identify reliable CT diagnostic criteria based on visual assessment, and also develop deep-learning based solutions for early positive diagnosis which could be used by less experienced readers, in a context of large epidemic. Several risk factors for poor outcome are already identified, such as older age, comorbidities, or an elevated d-dimer level at presentation (PMID: 32171076). Extensive CT abnormalities are linked to poor outcome, but some patients secondarily worsen despite non extensive abnormalities at first assessment, highlighting the need for worsening prediction based on initial imaging findings. Lastly, there is currently no drug with a proven efficacy for patients with acute respiratory distress syndrome, who for management relies on mechanical ventilation and supportive care. Some hypothesized that Remdesivir, an antiviral therapy could be effective (PMID: 32147516), with ongoing randomized trials conducted in China and the US. Automated tools allowing quantifying the disease extent on CT would be desirable in order to evaluate the efficacy of new treatments. Building a large dataset of CT images is needed for identification of accurate CT criteria and development of deep learning-based solutions for diagnosis, quantification and prognostic estimation. The aim of this project is three fold: (i) create a multi-centric open database repository on CT scans relative to COVID-19, (ii) create a multi-expert annotation protocol with different level of annotations depicting the severity of the disease, (iii) allow the development of non-proprietary computer aided solutions (academia & industry) for automatic quantification of the diseases and prognosis through the use of the latest advances in the field of artificial intelligence. For patients, the validation of reliable diagnostic criteria will allow early detection of the disease, and better distinction with other potential cause of acute respiratory symptoms, requiring a specific treatment, such as bacterial bronchopneumonia. It will contribute to a standardization of care as well as an equal access to diagnosis and treatment for the ensemble of the population. Public health benefit will be an access to CT diagnosis of COVID-19 independently from the availability of local expertise in thoracic imaging. The possibility to anticipate the need for ventilation, based on the developed CT severity scores, will also positively impact the management of patients in particular in the context of a massive flow of patients as expected at the epidemic peak. This project will allow evaluating the proportion of patients likely to present respiratory sequelae, based on the severity and extent of lung abnormalities at the acute phase of the disease. The availability of automated quantification tools will help evaluating treatment efficacy if new therapeutic approaches are developed. Lastly, the developed tools for early diagnosis, evaluation of severity and prediction of outcomes could prove useful if other viral pandemic occurs in the future. Indeed SARS-Cov2 outbreak has been preceded by SARS and MERS outbreaks due to other coronavirus.

Official Title
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Computed Tomography for Coronavirus Disease 19 Diagnosis

Conditions
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COVID-19

Intervention / Treatment
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* Diagnostic Test: Chest computed tomography (CT)
* Diagnostic Test: Reverse-transcription polymerase chain reaction (RT-PCR)


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Age>18 years CT examination performed for suspicion or follow-up of COVID-19 Non opposition for use of data Exclusion Criteria: Unavailability of RT-PCR results for SARS-Cov-2 Failure of CT image anonymized export

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Patients with suspicions of COVID-19 pneumonia<br>Patients with suspicions of COVID-19 pneumonia | Diagnostic Test: Chest computed tomography (CT)<br>* Chest computed tomography (CT) examination<br>Diagnostic Test: Reverse-transcription polymerase chain reaction (RT-PCR)<br>* Identification of viral RNA by reverse-transcription polymerase chain reaction<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Predictive values of CT criteria | Sensibility specificity positive and negative predictive values of CT criteria with RT-PCR results as standard of reference. | 1 month | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Accuracy of CT composite severity score | Accuracy (ROC curve analysis) of CT visual composite score to predict ventilation requirement and 1-month mortality | 1 month | 
| Accuracy of deep-learning based score | Accuracy (ROC curve analysis) of deep-learning based score to predict ventilation requirement and 1-month mortality | 1 month | 
| Predictive values of deep-learning based diagnostic algorithms | Sensibility specificity Positive and Negative predictive values of deep-learning based diagnostic algorithms | 1 month | 
| Dice similarity coefficient between manual and automated segmentation of lung disease abnormalities |  | 1 month | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Computed Tomography (CT), COVID-19, Artificial intelligence, Deep Learning