Accuracy of Chest CT for Differentiating COVID-19 from COVID-19 Mimics

Abstract

Purpose To determine the performance of radiologists with different levels of expertise regarding the differentiation of COVID-19 from other atypical pneumonias. Chest CT to identify patients suffering from COVID-19 has been reported to be limited by its low specificity for distinguishing COVID-19 from other atypical pneumonias (“COVID-19 mimics”). Meanwhile, the understanding of the morphologic patterns of COVID-19 has improved and they appear to be fairly specific.

Materials and Methods Between 02/2020 and 04/2020, 60 patients with COVID-19 pneumonia underwent chest CT in our department. Cases were matched with a comparable control group of 60 patients of similar age, sex, and comorbidities, who underwent chest CT prior to 01/2020 for atypical pneumonia caused by other pathogens. Included were other viral, fungal, and bacterial pathogens. All 120 cases were blinded to patient history and were reviewed independently by two radiologists and two radiology residents. Readers rated the probability of COVID-19 pneumonia according to the COV-RADS classification system. Results were analyzed using Clopper-Pearson 95 % confidence intervals, Youden’s Index for test quality criteria, and Fleiss‘ kappa statistics.

Results Overall, readers were able to correctly identify the presence of COVID-19 pneumonia in 219/240 (sensitivity: 91 %; 95 %-CI; 86.9 %–94.5 %), and to correctly attribute CT findings to COVID-19 mimics in 159/240 ratings (specificity: 66.3 %; 59.9 %–72.2 %), yielding an overall diagnostic accuracy of 78.8 % (378/480; 74.8 %–82.3 %). Individual reader accuracy ranged from 74.2 % (89/120) to 84.2 % (101/120) and did not correlate significantly with reader expertise. Youden’s Index was 0.57. Between-reader agreement was moderate (κ = 0.53).

Conclusion In this enriched cohort, radiologists were able to distinguish COVID-19 from “COVID-19 mimics” with moderate diagnostic accuracy. Accuracy did not correlate with reader expertise.

Key Points: 

• In a scenario of direct comparison (no negative findings), CT allows the differentiation of COVID-19 from other atypical pneumonias (“COVID mimics”) with moderate accuracy.

• Reader expertise did not significantly influence these results.

• Despite similar patterns and distributions of pulmonary findings, radiologists were able to estimate the probability of COVID-19 pneumonia using the COV-RADS classification in a standardized manner in the larger proportion of cases.

Citation Format

• Sähn M, Yüksel C, Keil S et al. Accuracy of Chest CT for Differentiating COVID-19 from COVID-19 Mimics. Fortschr Röntgenstr 2021; 193: 1081 – 1091

Zusammenfassung

Ziel Bestimmung der Leseleistung von Radiologen mit unterschiedlichen Fachkenntnissen hinsichtlich der Unterscheidung von COVID-19 von anderen atypischen Pneumonien. Als Limitierung der Thorax-CT in der Identifizierung von Patienten mit COVID-19 wird eine geringe Spezifität in der Unterscheidung von COVID-19 von anderen atypischen Pneumonien („COVID-19-Mimics“) beschrieben. Inzwischen hat sich das Verständnis der morphologischen Muster von COVID-19 verbessert und scheint relativ spezifisch zu sein.

Material und Methoden Im Zeitraum von Februar bis April 2020 wurden 60 Patienten mit COVID-19-Pneumonie mittels Thorax-CT in unserem Hause untersucht. Die Fälle wurden einer vergleichbaren Kontrollgruppe mit ähnlicher Geschlechterverteilung, Alter und Vorerkrankungen gegenübergestellt, die eine CT-Thorax bei atypischer Pneumonie vor Januar 2020 erhielt. Eingeschlossen wurden andere virale, Pilz- und atypische bakterielle Erreger. Alle 120 Fälle wurden verblindet von 2 radiologischen Fachärzten und 2 Assistenzärzten hinsichtlich der Wahrscheinlichkeit einer COVID-19-Pneumonie anhand des COV-RADS-Score beurteilt. Die Ergebnisse wurden mittels Clopper-Pearson-95 %-Konfidenzintervallen, Youden-Index für die Testgütekriterien und Fleiss’ Kappa ausgewertet.

Ergebnisse Insgesamt erkannten die Radiologen das Vorliegen einer COVID-19-Pneumonie in 219/240 Wertungen (Sensitivität: 91 %; 95 %-KI 86,9–94,5 %) und das eines „COVID-19-Mimics“ in 159/240 Wertungen (Spezifität: 66,3 %; 95 %-KI 59,9 %–72,2 %). Dies entspricht einer diagnostischen Genauigkeit von 78,8 % (378/480 Wertungen; 74,8–82,3 %). Individuelle diagnostische Genauigkeiten reichten von 74,2 % (89/120) bis 84,2 % (101/120) und korrelierten nicht signifikant mit der Berufserfahrung. Der Youden-Index betrug 0,57. Die Übereinstimmung der Radiologen war moderat (κ = 0,53).

Zusammenfassung In dieser mit atypischen Pneumonien angereicherten Kohorte konnten die Radiologen anhand der CT-Untersuchung COVID-19-Pneumonien von „COVID-Mimics“ mit moderater diagnostischer Genauigkeit unterscheiden. Hierbei zeigte die Berufserfahrung der Radiologen keinen direkten Einfluss auf die Ergebnisse.

Kernaussagen:

• Eine Unterscheidung zwischen COVID-19- und anderen atypischen Pneumonien („COVID-Mimics“) in der CT ist im Szenario des direkten Vergleichs (keine Negativbefunde) mit moderater diagnostischer Genauigkeit möglich.

• Die Berufserfahrung hatte keinen direkten Einfluss auf die Ergebnisse.

• Trotz der ähnlichen Verteilung von Infiltraten konnten die Radiologen anhand der COV-RADS-Klassifikation die Wahrscheinlichkeiten für das Vorliegen einer COVID-Pneumonie reliabel und standardisiert im größeren Anteil der Fälle einschätzen.

Key words

COVID-19 - diagnostic accuracy - CT - atypical pneumonia

Publication History

Received: 29 September 2020

Accepted: 19 January 2021

Article published online:
26 March 2021

• References

• 1 Huang C, Wang Y, Li X. et al Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. Lancet 2020; 395: 497-506
• 2 Bernheim A, Mei X, Huang M. et al. Chest CT Findings in Coronavirus Disease-19 (COVID-19): Relationship to Duration of Infection. Radiology 2020;
• 3 Ai T, Yang Z, Hou H. et al. Correlation of Chest CT and RT-PCR Testing in Coronavirus Disease 2019 (COVID-19) in China: A Report of 1014 Cases. Radiology 2020;
• 4 Cheng Z, Lu Y, Cao Q. et al. Clinical Features and Chest CT Manifestations of Coronavirus Disease 2019 (COVID-19) in a Single-Center Study in Shanghai, China. Am J Roentgenol 2020; 1-6
• 5 Salehi S, Abedi A, Balakrishnan S. et al. Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients. Am J Roentgenol 2020; 1-7
• 6 Han R, Huang L, Jiang H. et al. Early Clinical and CT Manifestations of Coronavirus Disease 2019 (COVID-19) Pneumonia. Am J Roentgenol 2020; 1-6
• 7 Ye Z, Zhang Y, Wang Y. et al. Chest CT manifestations of new coronavirus disease 2019 (COVID-19): a pictorial review. Eur Radiol 2020;
• 8 Zhou S, Wang Y, Zhu T. et al. CT Features of Coronavirus Disease 2019 (COVID-19) Pneumonia in 62 Patients in Wuhan, China. Am J Roentgenol 2020; 1-8
• 9 Wang K, Kang S, Tian R. et al. Imaging manifestations and diagnostic value of chest CT of coronavirus disease 2019 (COVID-19) in the Xiaogan area. Clin Radiol 2020;
• 10 Carotti M, Salaffi F, Sarzi-Puttini P. et al. Chest CT features of coronavirus disease 2019 (COVID-19) pneumonia: key points for radiologists. Radiol Med 2020;
• 11 Sun Z, Zhang N, Li Y. et al A systematic review of chest imaging findings in COVID-19. Quant Imaging Med Surg 2020; 10: 1058-1079
• 12 Li L, Qin L, Xu Z. et al. Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT. Radiology 2020;
• 13 Fang Y, Zhang H, Xie J. et al. Sensitivity of Chest CT for COVID-19: Comparison to RT-PCR. Radiology 2020;
• 14 Herpe G, Lederlin M, Naudin M. et al. Efficacy of Chest CT for COVID-19 Pneumonia in France. Radiology 2020;
• 15 American College of Radiology. ACR Recommendations for the use of Chest Radiography and Computed Tomography (CT) for Suspected COVID-19 Infection. In 2020 May 11.
• 16 Hosseiny M, Kooraki S, Gholamrezanezhad A. et al Radiology Perspective of Coronavirus Disease 2019 (COVID-19): Lessons From Severe Acute Respiratory Syndrome and Middle East Respiratory Syndrome. Am J Roentgenol 2020; 214: 1078-1082
• 17 Franquet T. Imaging of pulmonary viral pneumonia. Radiology 2011; 260: 18-39
• 18 Ellis SJ, Cleverley JR, Muller NL. Drug-induced lung disease: high-resolution CT findings. Am J Roentgenol 2000; 175: 1019-1024
• 19 Islam N, Salameh JP, Leeflang MM. et al Thoracic imaging tests for the diagnosis of COVID-19. Cochrane Database Syst Rev 2020; 11: Cd013639
• 20 Bai HX, Hsieh B, Xiong Z. et al. Performance of radiologists in differentiating COVID-19 from viral pneumonia on chest CT. Radiology 2020;
• 21 Luo N, Zhang H, Zhou Y. et al. Utility of chest CT in diagnosis of COVID-19 pneumonia. Diagn Interv Radiol 2020;
• 22 Charlson ME, Pompei P, Ales KL. et al A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 1987; 40: 373-383
• 23 Chung M, Bernheim A, Mei X. et al CT Imaging Features of 2019 Novel Coronavirus (2019-nCoV). Radiology 2020; 295: 202-207
• 24 Song F, Shi N, Shan F. et al Emerging 2019 Novel Coronavirus (2019-nCoV) Pneumonia. Radiology 2020; 295: 210-217
• 25 Pan F, Ye T, Sun P. et al. Time Course of Lung Changes On Chest CT During Recovery From 2019 Novel Coronavirus (COVID-19) Pneumonia. Radiology 2020;
• 26 Schulze-Hagen M, Hübel C, Meier-Schroers M. et al Low-dose chest CT for the diagnosis of COVID-19 – a systematic, prospective comparison with PCR. Dtsch Arztebl Int 2020; 117: 389-395
• 27 Gezer NS, Ergan B, Barış MM. et al COVID-19 S: A new proposal for diagnosis and structured reporting of COVID-19 on computed tomography imaging. Diagn Interv Radiol 2020; 26: 315-322
• 28 Schalekamp S, Bleeker-Rovers CP, Beenen LFM. et al. Chest CT in the Emergency Department for Diagnosis of COVID-19 Pneumonia: Dutch Experience. Radiology 2020;
• 29 Kovács A, Palásti P, Veréb D. et al. The sensitivity and specificity of chest CT in the diagnosis of COVID-19. Eur Radiol 2020; 1-6
• 30 Wang Y, Dong C, Hu Y. et al Temporal Changes of CT Findings in 90 Patients with COVID-19 Pneumonia: A Longitudinal Study. Radiology 2020; 296: e55-e64