Interobserver Agreement in the Analysis of Different Radiological Classifications of COVID-19 on Computed Tomography

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dc.contributor.authorOzdemir, Adnan
dc.contributor.authorYilmaz, Sevda
dc.contributor.authorErol, Ozlem Ozluk
dc.contributor.authorKaygusuz, Sedat
dc.contributor.authorGoncuoglu, Alper
dc.contributor.authorErkmen, Selmin Perihan Komurcu
dc.contributor.authorKarahan, Irfan
dc.date.accessioned2025-01-21T16:41:51Z
dc.date.available2025-01-21T16:41:51Z
dc.date.issued2021
dc.departmentKırıkkale Üniversitesi
dc.description.abstractIntroduction: Computed tomography (CT) has approximately 98% sensitivity for Coronavirus disease-2019 (COVID-19). Various algorithms were designed using CT images. However, the interobserver agreement of different radiological classifications of COVID-19 is not yet known. Thus, this study aimed to investigate the interobserver agreement of different radiological classifications of COVID-19. Materials and Methods: This study included 212 patients who were positive on the polymerase chain reaction test and eligible for CT. Four radiologists examined all CT images simultaneously. They reached a consensus that CT images can provide definite findings of COVID-19. The Radiological Society of North America (RSNA) consensus statement, the British Society of Thoracic Imaging (BSTI) structured reporting statement, and COVID-19 Reporting and Data System (CO-RADS) were used. Fleiss' Kappa was used to detect interobserver agreement. Kappa values of 0.000.20 were considered as slight, 0.21-0.40 as fair, 0.41-0.60 as moderate, 0.61-0.80 as substantial, and 0.81-1.00 as near-perfect agreement, and p<0.05 was accepted as significant. Results: A total of 137 patients did not have any pathological CT findings. The most prevalent radiological findings were ground-glass opacities and consolidations. The agreements on all classifications were at near-perfect levels: RSNA, 0.86 (0.82-0.90); BSTI, 0.83 (0.79-0.87), and CO-RADS, 0.82 (0.79-0.86). The RSNA classification has the highest consistency rate, followed by BSTI and CO-RADS. However, substantial and moderate agreements were found in the subcategories of each classification. Conclusion: In this study, some subcategories had a lower agreement, despite the high consistency rates for COVID-19 radiological classification systems in the literature. Therefore, improving the items without consensus can lead to the development of better radiological diagnostic approaches.
dc.identifier.doi10.4274/mjima.galenos.2021.2021.42
dc.identifier.issn2147-673X
dc.identifier.scopus2-s2.0-85122156707
dc.identifier.scopusqualityQ4
dc.identifier.urihttps://doi.org/10.4274/mjima.galenos.2021.2021.42
dc.identifier.urihttps://hdl.handle.net/20.500.12587/24966
dc.identifier.volume10
dc.identifier.wosWOS:000757948600013
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherGalenos Publ House
dc.relation.ispartofMediterranean Journal of Infection Microbes and Antimicrobials
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241229
dc.subjectCOVID-19; diagnosis; tomography; interobserver variability
dc.titleInterobserver Agreement in the Analysis of Different Radiological Classifications of COVID-19 on Computed Tomography
dc.typeArticle

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