Evaluation of the RSNA and CORADS classifications for COVID-19 on chest computed tomography in the Brazilian population

OBJECTIVE: To determine the correlation between the two tomographic classifications for coronavirus disease (COVID-19), COVID-19 Reporting and Data System (CORADS) and Radiological Society of North America Expert Consensus Statement on Reporting Chest Computed Tomography (CT) Findings Related to COVID-19 (RSNA), in the Brazilian population and to assess the agreement between reviewers with different experience levels. METHODS: Chest CT images of patients with reverse transcriptase-polymerase chain reaction (RT-PCR)-positive COVID-19 were categorized according to the CORADS and RSNA classifications by radiologists with different levels of experience and who were initially unaware of the RT-PCR results. The inter- and intra-observer concordances for each of the classifications were calculated, as were the concordances between classifications. RESULTS: A total of 100 patients were included in this study. The RSNA classification showed an almost perfect inter-observer agreement between reviewers with similar experience levels, with a kappa coefficient of 0.892 (95% confidence interval [CI], 0.788-0.995). CORADS showed substantial agreement among reviewers with similar experience levels, with a kappa coefficient of 0.642 (95% CI, 0.491-0.793). There was inter-observer variation when comparing less experienced reviewers with more experienced reviewers, with the highest kappa coefficient of 0.396 (95% CI, 0.255-0.588). There was a significant correlation between both classifications, with a Kendall coefficient of 0.899 (p<0.001) and substantial intra-observer agreement for both classifications. CONCLUSION: The RSNA and CORADS classifications showed excellent inter-observer agreement for reviewers with the same level of experience, although the agreement between less experience reviewers and the reviewer with the most experience was only reasonable. Combined analysis of both classifications with the first RT-PCR results did not reveal any false-negative results for detecting COVID-19 in patients.

Chest computed tomography in the diagnosis of COVID-19 in patients with false negative RT-PCR / Tomografia computadorizada de tórax no diagnóstico de COVID-19 em pacientes com resultado falso-negativo na RT-PCR

ABSTRACT Objective To evaluate the role of chest computed tomography in patients with COVID-19 who presented initial negative result in reverse transcriptase-polymerase chain reaction (RT-PCR). Methods A single-center, retrospective study that evaluated 39 patients with negative RT-PCR for COVID-19, who underwent chest computed tomography and had a final clinical or serological diagnosis of COVID-19. The visual tomographic classification was evaluated according to the Consensus of the Radiological Society of North America and software developed with artificial intelligence for automatic detection of findings and chance estimation of COVID-19. Results In the visual tomographic analysis, only one of them (3%) presented computed tomography classified as negative, 69% were classified as typical and 28% as indeterminate. In the evaluation using the software, only four (about 10%) had a probability of COVID-19 <25%. Conclusion Computed tomography can play an important role in management of suspected cases of COVID-19 with initial negative results in RT-PCR, especially considering those patients outside the ideal window for sample collection for RT-PCR.

RESUMO Objetivo Avaliar o papel da tomografia computadorizada de tórax em pacientes com COVID-19 que apresentaram reação em cadeia da polimerase via transcriptase reversa (RT-PCR) inicial falsamente negativa. Métodos Estudo retrospectivo de centro único que avaliou 39 pacientes com RT-PCR negativa para COVID-19, submetidos à tomografia computadorizada de tórax e que tiveram diagnóstico final clínico ou serológico de COVID-19. A classificação tomográfica visual foi avaliada de acordo com o Consenso da Radiological Society of North America e o software desenvolvido com inteligência artificial para detecção automática de achados e estimativa de probabilidade de COVID-19. Resultados Na análise tomográfica visual, somente um deles (3%) apresentou tomografia computadorizada classificada como tendo resultado negativo, 69% foram classificados como típicos e 28% como indeterminados. Na avaliação com uso de software, somente quatro (cerca de 10%) tiveram probabilidade de COVID-19 <25%. Conclusão A tomografia computadorizada pode desempenhar papel importante no manejo de casos suspeitos de COVID-19 com RT-PCR inicialmente negativa, principalmente levando-se em consideração os pacientes que estão fora da janela ideal para coleta de amostra para RT-PCR.