Comparison of the CO-RADS and the RSNA chest CT classification system concerning sensitivity and reliability for the diagnosis of COVID-19 pneumonia.

COVID-19 Pneumonia Reproducibility of results Retrospective studies Tomography (X-ray computed)

Journal

Insights into imaging
ISSN: 1869-4101
Titre abrégé: Insights Imaging
Pays: Germany
ID NLM: 101532453

Informations de publication

Date de publication:
28 Apr 2021
Historique:
received: 28 01 2021
accepted: 09 04 2021
entrez: 29 4 2021
pubmed: 30 4 2021
medline: 30 4 2021
Statut: epublish

Résumé

The Radiological Society of North America (RSNA) recently published a chest CT classification system and Dutch Association for Radiology has announced Coronavirus disease 2019 (COVID-19) reporting and data system (CO-RADS) to provide guidelines to radiologists who interpret chest CT images of patients with suspected COVID-19 pneumonia. This study aimed to compare CO-RADS and RSNA classification with respect to their sensitivity and reliability for diagnosis of COVID-19 pneumonia. A retrospective study assessed consecutive CT chest imaging of 359 COVID-19-positive patients. Three experienced radiologists who were aware of the final diagnosis of all patients, independently categorized each patient according to CO-RADS and RSNA classification. RT-PCR test performed within one week of chest CT scan was used as a reference standard for calculating sensitivity of each system. Kappa statistics and intraclass correlation coefficient were used to assess reliability of each system. The study group included 359 patients (180 men, 179 women; mean age, 45 ± 16.9 years). Considering combination of CO-RADS 3, 4 and 5 and combination of typical and indeterminate RSNA categories as positive predictors for COVID-19 diagnosis, the overall sensitivity was the same for both classification systems (72.7%). Applying both systems in moderate and severe/critically ill patients resulted in a significant increase in sensitivity (94.7% and 97.8%, respectively). The overall inter-reviewer agreement was excellent for CO-RADS (κ = 0.801), and good for RSNA classification (κ = 0.781). CO-RADS and RSNA chest CT classification systems are comparable in diagnosis of COVID-19 pneumonia with similar sensitivity and reliability.

Sections du résumé

BACKGROUND BACKGROUND
The Radiological Society of North America (RSNA) recently published a chest CT classification system and Dutch Association for Radiology has announced Coronavirus disease 2019 (COVID-19) reporting and data system (CO-RADS) to provide guidelines to radiologists who interpret chest CT images of patients with suspected COVID-19 pneumonia. This study aimed to compare CO-RADS and RSNA classification with respect to their sensitivity and reliability for diagnosis of COVID-19 pneumonia.
RESULTS RESULTS
A retrospective study assessed consecutive CT chest imaging of 359 COVID-19-positive patients. Three experienced radiologists who were aware of the final diagnosis of all patients, independently categorized each patient according to CO-RADS and RSNA classification. RT-PCR test performed within one week of chest CT scan was used as a reference standard for calculating sensitivity of each system. Kappa statistics and intraclass correlation coefficient were used to assess reliability of each system. The study group included 359 patients (180 men, 179 women; mean age, 45 ± 16.9 years). Considering combination of CO-RADS 3, 4 and 5 and combination of typical and indeterminate RSNA categories as positive predictors for COVID-19 diagnosis, the overall sensitivity was the same for both classification systems (72.7%). Applying both systems in moderate and severe/critically ill patients resulted in a significant increase in sensitivity (94.7% and 97.8%, respectively). The overall inter-reviewer agreement was excellent for CO-RADS (κ = 0.801), and good for RSNA classification (κ = 0.781).
CONCLUSION CONCLUSIONS
CO-RADS and RSNA chest CT classification systems are comparable in diagnosis of COVID-19 pneumonia with similar sensitivity and reliability.

Identifiants

pubmed: 33913066
doi: 10.1186/s13244-021-00998-4
pii: 10.1186/s13244-021-00998-4
pmc: PMC8081002
doi:

Types de publication

Journal Article

Langues

eng

Pagination

55

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Auteurs

Mohamed Abdel-Tawab (M)

Department of Diagnostic Radiology, Faculty of Human Medicine, Assiut University, Assiut, Egypt.

Mohammad Abd Alkhalik Basha (MAA)

Department of Diagnostic Radiology, Faculty of Human Medicine, Zagazig University, Zagazig, Egypt. Mohammad_basha76@yahoo.com.

Ibrahim A I Mohamed (IAI)

Department of Diagnostic Radiology, Faculty of Human Medicine, Assiut University, Assiut, Egypt.

Hamdy M Ibrahim (HM)

Department of Diagnostic Radiology, Faculty of Human Medicine, Assiut University, Assiut, Egypt.

Mohamed M A Zaitoun (MMA)

Department of Diagnostic Radiology, Faculty of Human Medicine, Zagazig University, Zagazig, Egypt.

Saeed Bakry Elsayed (SB)

Department of Diagnostic Radiology, Faculty of Human Medicine, Zagazig University, Zagazig, Egypt.

Nader E M Mahmoud (NEM)

Department of Diagnostic Radiology, Faculty of Human Medicine, Zagazig University, Zagazig, Egypt.

Ahmed A El Sammak (AA)

Department of Diagnostic Radiology, Faculty of Human Medicine, Zagazig University, Zagazig, Egypt.

Hala Y Yousef (HY)

Department of Diagnostic Radiology, Faculty of Human Medicine, Zagazig University, Zagazig, Egypt.

Sameh Abdelaziz Aly (SA)

Department of Radio-diagnosis, Faculty of Human Medicine, Benha University, Benha, Egypt.

Hamada M Khater (HM)

Department of Radio-diagnosis, Faculty of Human Medicine, Benha University, Benha, Egypt.

Walid Mosallam (W)

Department of Radio-diagnosis, Faculty of Human Medicine, Suiz Canal University, Esmaelia, Egypt.

Waleed S Abo Shanab (WS)

Department of Radio-diagnosis, Faculty of Human Medicine, Port Said University, Port Said, Egypt.

Ali M Hendi (AM)

Department of Radiology, College of Medicine, Jazan University, Jazan, Saudi Arabia.

Sayed Hassan (S)

Department of Diagnostic Radiology, Faculty of Human Medicine, Assiut University, Assiut, Egypt.

Classifications MeSH