French Imaging Database Against Coronavirus (FIDAC): A large COVID-19 multi-center chest CT database.
Diagn Interv Imaging
; 103(10): 460-463, 2022 Oct.
Article
in En
| MEDLINE
| ID: mdl-35715328
ABSTRACT
PURPOSE:
During the first wave of the COVID-19 pandemic, the French Society of Radiology and the French College of Radiology, in partnership with NEHS Digital, have set up a system to collect chest computed tomography (CT) examinations with clinical, virological and radiological metadata, from patients clinically suspected of COVID-19 pneumonia. This allowed the constitution of an anonymized multicenter database, named FIDAC (French Imaging Database Against Coronavirus). The aim of this report was to describe the content of this public database. MATERIALS ANDMETHODS:
Twenty-two French radiology centers participated to the data collection. The data collected were chest CT examinations in DICOM format associated with the following metadata patient age and sex, originating facility identifier, originating facility region, time from symptom onset to CT examination, indication for CT examination, reverse transcription-polymerase chain reaction (RT-PCR) results and normalized CT report performed by a senior radiologist. All the data were anonymized and sent through a NEHS Digital system to a centralized data center.RESULTS:
A total of 5944 patients were included from the 22 centers aggregated into 8 regions with a mean number of patients of 743 ± 603.3 [SD] per region (range 102-1577 patients). Reasons for CT examination and normalized CT reports were provided for all patients. RT-PCR results were provided in 5574 patients (93.77%) with a positive result of RT-PCR in 44.6% of patients.CONCLUSION:
The FIDAC project allowed the creation of a large database of chest CT images and metadata available, under conditions, in open access through the CERF-SFR website.Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Observational_studies
/
Risk_factors_studies
Limits:
Humans
Language:
En
Journal:
Diagn Interv Imaging
Year:
2022
Document type:
Article