Computed Tomography for COVID-19 Diagnosis (STOIC)

December 18, 2020 updated by: Assistance Publique - Hôpitaux de Paris

Computed Tomography for Coronavirus Disease 19 Diagnosis

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.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

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.

Study Type

Observational

Enrollment (Actual)

10735

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

  • France
      • Paris, France, 75014
        • Cochin Hospital

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Non-Probability Sample

Study Population

Patients with suspicions of COVID-19 pneumonia

Description

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

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Patients with suspicions of COVID-19 pneumonia
Diagnostic test: Chest computed tomography (CT)
Chest computed tomography (CT) examination
Diagnostic test: Reverse-transcription polymerase chain reaction (RT-PCR)
Identification of viral RNA by reverse-transcription polymerase chain reaction

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Predictive values of CT criteria
Time Frame: 1 month
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
Time Frame: 1 month
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
Time Frame: 1 month
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
Time Frame: 1 month
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
Time Frame: 1 month
1 month

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Assistance Publique - Hôpitaux de Paris

Collaborators

GE Healthcare
Institut National de la Santé Et de la Recherche Médicale, France
Orange healthcare
TheraPanacea

Investigators

  • Principal Investigator: Marie-Pierre REVEL, MD,PhD, Assistance Publique - Hôpitaux de Paris

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

March 1, 2020

Primary Completion (Actual)

October 16, 2020

Study Completion (Actual)

October 16, 2020

Study Registration Dates

First Submitted

April 17, 2020

First Submitted That Met QC Criteria

April 17, 2020

First Posted (Actual)

April 21, 2020

Study Record Updates

Last Update Posted (Actual)

December 21, 2020

Last Update Submitted That Met QC Criteria

December 18, 2020

Last Verified

April 1, 2020

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • APHP200434

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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