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Computed Tomography for COVID-19 Diagnosis (STOIC)

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT04355507
Recruitment Status : Completed
First Posted : April 21, 2020
Last Update Posted : December 21, 2020
Sponsor:
Collaborators:
Institut National de la Santé Et de la Recherche Médicale, France
GE Healthcare
Orange healthcare
TheraPanacea
Information provided by (Responsible Party):
Assistance Publique - Hôpitaux de Paris

Brief Summary:
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.

Condition or disease Intervention/treatment
COVID-19 Diagnostic Test: Chest computed tomography (CT) Diagnostic Test: Reverse-transcription polymerase chain reaction (RT-PCR)

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Study Type : Observational
Actual Enrollment : 10735 participants
Observational Model: Cohort
Time Perspective: Retrospective
Official Title: Computed Tomography for Coronavirus Disease 19 Diagnosis
Actual Study Start Date : March 1, 2020
Actual Primary Completion Date : October 16, 2020
Actual Study Completion Date : October 16, 2020

Resource links provided by the National Library of Medicine

MedlinePlus related topics: CT Scans

Group/Cohort Intervention/treatment
Patients with suspicions of COVID-19 pneumonia
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




Primary Outcome Measures :
  1. 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.


Secondary Outcome Measures :
  1. 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

  2. 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

  3. 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

  4. Dice similarity coefficient between manual and automated segmentation of lung disease abnormalities [ Time Frame: 1 month ]


Information from the National Library of Medicine

Choosing to participate in a study is an important personal decision. Talk with your doctor and family members or friends about deciding to join a study. To learn more about this study, you or your doctor may contact the study research staff using the contacts provided below. For general information, Learn About Clinical Studies.


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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
Patients with suspicions of COVID-19 pneumonia
Criteria

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

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT04355507


Locations
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France
Cochin Hospital
Paris, France, 75014
Sponsors and Collaborators
Assistance Publique - Hôpitaux de Paris
Institut National de la Santé Et de la Recherche Médicale, France
GE Healthcare
Orange healthcare
TheraPanacea
Investigators
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Principal Investigator: Marie-Pierre REVEL, MD,PhD Assistance Publique - Hôpitaux de Paris
Publications:
Publications automatically indexed to this study by ClinicalTrials.gov Identifier (NCT Number):
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Responsible Party: Assistance Publique - Hôpitaux de Paris
ClinicalTrials.gov Identifier: NCT04355507    
Other Study ID Numbers: APHP200434
First Posted: April 21, 2020    Key Record Dates
Last Update Posted: December 21, 2020
Last Verified: April 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Assistance Publique - Hôpitaux de Paris:
Computed Tomography (CT)
COVID-19
Artificial intelligence
Deep Learning