CT Findings in Patients with COVID-19-Compatible Symptoms but Initially Negative qPCR Test.

PURPOSE: To assess whether it is possible to reliably detect patients with strong suspicion of COVID-19 despite initially negative quantitative polymerase-chain-reaction (qPCR) tests by means of computed tomography (CT). MATERIALS AND METHODS: 437 patients with suspected COVID-19 but initially negative qPCR and subsequent chest CT between March 13 and November 30, 2020 were included in this retrospective study. CT findings were compared to results of successive qPCR tests (minimum of 3 qPCR tests if CT suggested infection) to determine the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of CT for diagnosing COVID-19. RESULTS: COVID-19 was diagnosed correctly with a sensitivity of 100 % [95 % confidence interval (CI): 65-100] and a specificity of 88 % [95 % CI: 84-90]. A PPV of 12 % [95 % CI: 6-22] and an NPV of 100 % [95 % CI: 99-100] were determined. CONCLUSION: CT is able to detect COVID-19 before qPCR in initially negative patients in this special study setting. Similar CT findings in COVID-19 and other atypical pneumonias can lead to high numbers of false-positive patients, reducing the specificity of CT. KEY POINTS: · Low-dose chest CT is able to diagnose COVID-19 in symptomatic patients even in cases of an initially negative quantitative PCR result and therefore is a fast support method to detect COVID-19, especially in early disease.. · Low-dose chest CT can reliably exclude COVID-19 in a pandemic setting.. · CT does not always ensure a reliable differentiation from other viral diseases.. CITATION FORMAT: · Valentin B, Steuwe A, Wienemann T, et al. CT Findings in Patients with COVID-19-Compatible Symptoms but Initially Negative qPCR Test. Fortschr Röntgenstr 2022; 194: 1110 - 1118.

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.

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.

Dose-optimised chest computed tomography for diagnosis of Coronavirus Disease 2019 (COVID-19) - Evaluation of image quality and diagnostic impact.

OBJECTIVES: The detection of Coronavirus Disease 2019 (COVID-19) by reverse transcription polymerase chain reaction (RT-PCR) has varying sensitivity. Computed tomography (CT) of the chest can verify infection in patients with clinical symptoms and a negative test result, accelerating treatment and actions to prevent further contagion. However, CT employs ionising radiation. The purpose of this study was to evaluate protocol settings, associated radiation exposure, image quality and diagnostic performance of a low-dose CT protocol in a university hospital setting. MATERIALS AND METHODS: Chest CT examinations were performed on a single scanner (Somatom Definition Edge, Siemens Healthineers, Germany) in 105 symptomatic patients (60 male, 45 female). Images were evaluated with regard to protocol parameters, image quality, radiation exposure and diagnostic accuracy. Serial RT-PCR served as the standard of reference. Based on this reference standard sensitivity, specificity, positive and negative predictive values of CT with 95% confidence interval were calculated. RESULTS: The mean effective dose was 1.3 ± 0.4 mSv (0.7-2.9 mSv) for the patient cohort (mean age 66.6 ± 16.7 years (19-94 years), mean body mass index (BMI) 26.6 ± 5.3 kg m-2 (16-46 kg/m2)). A sensitivity of 100 [95% CI: 82-100]%, a specificity of 78 [95% CI: 68-86]%, a positive predictive value of 50 [95% CI: 33-67]% and a negative predictive value of 100 [95% CI: 95-100]% were obtained. No COVID-19 diagnoses were missed by CT. Image noise did not strongly correlate with BMI or patient diameter and was rated as average. CONCLUSIONS: We presented a robust imaging procedure with a chest CT protocol for confident diagnosis of COVID-19. Even for an overweight patient cohort, an associated radiation exposure of only 1.3 ± 0.4 mSv was achieved with sufficient diagnostic quality to exclude COVID-19.