Applicability of CO-RADS in an Anonymized Cohort Including Early and Advanced Stages of COVID-19 in Comparison to the Recommendations of the German Radiological Society and Radiological Society of North America

Abstract

Purpose Classifications were created to facilitate radiological evaluation of the novel coronavirus disease 2019 (COVID-19) on computed tomography (CT) images. The categorical CT assessment scheme (CO-RADS) categorizes lung parenchymal changes according to their likelihood of being caused by SARS-CoV-2 infection. This study investigates the diagnostic accuracy of diagnosing COVID-19 with CO-RADS compared to the Thoracic Imaging Section of the German Radiological Society (DRG) classification and Radiological Society of North America (RSNA) classification in an anonymized patient cohort. To mimic advanced disease stages, follow-up examinations were included as well.

Method This study includes all patients undergoing chest CT in the case of a suspected SARS-CoV-2 infection or an already confirmed infection between March 13 and November 30, 2020. During the study period, two regional lockdowns occurred due to high incidence values, increasing the pre-test probability of COVID-19. Anonymized CT images were reviewed retrospectively and in consensus by two radiologists applying CO-RADS, DRG, and RSNA classification. Afterwards, CT findings were compared to results of sequential real-time reverse transcriptase polymerase chain reaction (qPCR) test performed during hospitalization to determine statistical analysis for diagnosing COVID-19.

Results 536 CT examinations were included. CO-RADS, DRG and RSNA achieved an NPV of 96 %/94 %/95 % (CO-RADS/DRG/RSNA), PPV of 83 %/80 %/88 %, sensitivity of 86 %/76 %/80 %, and specificity of 96 %/95 %/97 %. The disease prevalence was 20 %.

Conclusion All applied classifications can reliably exclude a SARS-CoV-2 infection even in an anonymous setting. Nevertheless, pre-test probability was high in our study setting and has a great influence on the classifications. Therefore, the applicability of the individual classifications will become apparent in the future with lower prevalence and incidence of COVID-19.

Key Points: 

• CO-RADS, DRG, and RSNA classifications help to reliably detect infected patients in an anonymized setting

• Pre-test probability has a great influence on the individual classifications

• Difficulties in an anonymized study setting are severe pulmonary changes and residuals.

Citation Format

• Valentin B, Steuwe A, Wienemann T et al. Applicability of CO-RADS in an Anonymized Cohort Including Early and Advanced Stages of COVID-19 in Comparison to the Recommendations of the German Radiological Society and Radiological Society of North America. Fortschr Röntgenstr 2022; 194: 862 – 872

Zusammenfassung

Ziel Um eine einheitliche Befundung von Thorax-Computertomografien (CTs) mit Verdacht auf COVID-19 zu ermöglichen, wurden verschiedene Klassifikationen etabliert. CO-RADS klassifiziert Lungenparenchymveränderungen anhand ihrer Wahrscheinlichkeit für das Vorliegen einer SARS-CoV-2-Infektion. Diese Studie untersucht die retrospektive Anwendbarkeit der CO-RADS-Klassifikation in einer anonymisierten Kohorte im Vergleich zur DRG- und RSNA-Klassifikation. Verlaufsuntersuchungen wurden zusätzlich eingeschlossen, um ein fortgeschrittenes Krankheitsstadium zu simulieren. Als Referenzstandard dienen die Ergebnisse durchgeführter sequenzieller Reverse-Transkriptase-Polymerase-Kettenreaktionstests (qPCR).

Methoden Eingeschlossen wurden alle CT-Thorax Untersuchungen potenziell infizierter und nachweislich erkrankter Patienten zwischen dem 13. März und dem 30. November 2020. In diesem Zeitraum gab es aufgrund hoher Inzidenzwerte 2 regionale Lockdowns, wodurch eine hohe Vortestwahrscheinlichkeit vorliegt. Jede CT-Untersuchung wurde anonymisiert und anschließend nach CO-RADS-, DRG- oder RSNA-Klassifikation retrospektiv im Konsens durch 2 Radiologen (Assistenzarzt und Facharzt) ausgewertet. Die Befunde wurden mit den Ergebnissen der qPCR verglichen und eine statistische Auswertung wurde angefertigt.

Ergebnisse Insgesamt wurden 536 CT-Untersuchungen eingeschlossen. Die CO-RADS-, DRG- und RSNA-Klassifikationen erzielten einen negativ prädiktiven Wert von 96 %/94 %/95 % (CO-RADS/DRG/RSNA), einen positiv prädiktiven Wert von 83 %/80 %/88 %, eine Sensitivität von 86 %/76 %/80 % und eine Spezifität von 96 %/95 %/97 %. Die Prävalenz lag bei 20 %.

Schlussfolgerung Alle Klassifikationen konnten verlässlich eine SARS-CoV-2-Infektion ausschließen. Nichtsdestotrotz lag eine hohe Vortestwahrscheinlichkeit bei unserem Studiensetting vor, die einen großen Einfluss auf die Klassifikationen hat. Daher bleibt es zu untersuchen, ob die Klassifikationen auch in Zukunft bei niedrigerer Prävalenz und Inzidenz von COVID-19 anwendbar sind.

Kernaussagen: 

• Die CO-RADS-, DRG- und RSNA-Klassifikationen können helfen, Infizierte sicher in einer anonymisierten Kohorte zu erkennen

• Die Vortestwahrscheinlichkeit hat einen großen Einfluss auf die individuellen Klassifikationen

• Die Anonymisierung kann zu Fehlinterpretationen bei einem gravierenden Lungenbefall oder Residuen führen

Key words

SARS-CoV-2 - pneumonia, viral - Multidetector Computed Tomography - Real-Time PCR - SARS-Associated Coronavirus

Publication History

Received: 28 April 2021

Accepted: 03 January 2022

Article published online:
24 February 2022

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