Use of the COVID-19 Reporting and Data System (CO-RADS) classification and chest computed tomography involvement score (CT-IS) in COVID-19 pneumonia

Özel, Mehmet; Aslan, Aydın; Araç, Songül

doi:10.1007/s11547-021-01335-x

Cited by 34 publications

(24 citation statements)

References 21 publications

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“…Studies evaluating the diagnostic performance of CORADS report a consistent evaluation system with high positive predictions. 8 , 22 , 23 , 24 , 25 In the literature, there is no study comparing the findings of…”

Section: Discussionmentioning

confidence: 99%

Radiological Imaging of Viral Pneumonia Cases Identified Before the COVİD-19 Pandemic Period and COVİD-19 Pneumonia Cases Comparison of Characteristics

Cömert

Cingöz

Mese

et al. 2022

Preprint

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BackgroundThoracic CT imaging is widely used as a diagnostic method in the diagnosis of COVID-19 pneumonia. Radiological differential diagnosis and isolation of other viral agents causing pneumonia in patients gained importance, especially during the pandemic period.AimsWe aimed to investigate whether there is a difference between the CT imaging findings characteristically defined in COVID-19 pneumonia and the findings detected in pneumonia due to other viral agents, and which finding may be more effective in the diagnosis.Study DesignThe study included 249 adult patients with pneumonia found in thorax CT examination and positive COVID-19 RT-PCR test and 94 patients diagnosed with non-COVID pneumonia (viral PCR positive, no bacterial/fungal agents were detected in other cultures) from the last 5 years before the pandemic. It was retrospectively analyzed using the PACS System. CT findings were evaluated by two radiologists with 5 and 20 years of experience who did not know to which group the patient belonged, and it was decided by consensus.MethodsDemographic data (age, gender, known chronic disease) and CT imaging findings (percentage of involvement, number of lesions, distribution preference, dominant pattern, ground-glass opacity distribution pattern, nodule, tree in bud sign, interstitial changes, crazy paving sign, reversed halo sign, vacuolar sign, halo sign, vascular enlargement, linear opacities, traction bronchiectasis, peribronchial wall thickness, air trapping, pleural retraction, pleural effusion, pericardial effusion, cavitation, mediastinal/hilar lymphadenopathy, dominant lesion size, consolidation, subpleural curvilinear opacities, air bronchogram, pleural thickening) of the patients were evaluated. CT findings were also evaluated with the RSNA consensus guideline and the CORADS scoring system. Data were divided into two main groups as non-COVID-19 and COVID-19 pneumonia and compared statistically with chi-square tests and multiple regression analysis of independent variables.ResultsTwo main groups; RSNA and CORADS classification, percentage of involvement, number of lesions, distribution preference, dominant pattern, nodule, tree in bud, interstitial changes, crazy paving, reverse halo vascular enlargement, peribronchial wall thickness, air trapping, pleural retraction, pleural/pericardial effusion, cavitation and mediastinal/hilar lymphadenopathy were compared, significant differences were found between the groups (p < 0.01). Multiple linear regression analysis of independent variables found a significant effect of reverse halo sign (β = 0.097, p <0.05) and pleural effusion (β = 10.631, p <0.05) on COVID-19 pneumonia.ConclusionPresence of reverse halo and absence of pleural effusion was found to be efficient in the diagnosis of COVID-19 pneumonia.

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Section: Discussionmentioning

confidence: 99%

Radiological Imaging of Viral Pneumonia Cases Identified Before the COVİD-19 Pandemic Period and COVİD-19 Pneumonia Cases Comparison of Characteristics

Cömert

Cingöz

Mese

et al. 2022

Preprint

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“…Their identification and the volumetric quantification may allow an easier classification in terms of gravity, extent and progression of the disease. Moreover, this may provide a high-impact tool to enhance awareness of the severity of COVID-19 pneumonia [ 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 ].…”

Section: Introductionmentioning

confidence: 99%

Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center

Granata

Fusco

Villanacci

et al. 2022

JPM

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Purpose: To analyze the vaccine effect by comparing five groups: unvaccinated patients with Alpha variant, unvaccinated patients with Delta variant, vaccinated patients with Delta variant, unvaccinated patients with Omicron variant, and vaccinated patients with Omicron variant, assessing the “gravity” of COVID-19 pulmonary involvement, based on CT findings in critically ill patients admitted to Intensive Care Unit (ICU). Methods: Patients were selected by ICU database considering the period from December 2021 to 23 March 2022, according to the following inclusion criteria: patients with proven Omicron variant COVID-19 infection with known COVID-19 vaccination with at least two doses and with chest Computed Tomography (CT) study during ICU hospitalization. Wee also evaluated the ICU database considering the period from March 2020 to December 2021, to select unvaccinated consecutive patients with Alpha variant, subjected to CT study, consecutive unvaccinated and vaccinated patients with Delta variant, subjected to CT study, and, consecutive unvaccinated patients with Omicron variant, subjected to CT study. CT images were evaluated qualitatively using a severity score scale of 5 levels (none involvement, mild: ≤25% of involvement, moderate: 26–50% of involvement, severe: 51–75% of involvement, and critical involvement: 76–100%) and quantitatively, using the Philips IntelliSpace Portal clinical application CT COPD computer tool. For each patient the lung volumetry was performed identifying the percentage value of aerated residual lung volume. Non-parametric tests for continuous and categorical variables were performed to assess statistically significant differences among groups. Results: The patient study group was composed of 13 vaccinated patients affected by the Omicron variant (Omicron V). As control groups we identified: 20 unvaccinated patients with Alpha variant (Alpha NV); 20 unvaccinated patients with Delta variant (Delta NV); 18 vaccinated patients with Delta variant (Delta V); and 20 unvaccinated patients affected by the Omicron variant (Omicron NV). No differences between the groups under examination were found (p value > 0.05 at Chi square test) in terms of risk factors (age, cardiovascular diseases, diabetes, immunosuppression, chronic kidney, cardiac, pulmonary, neurologic, and liver disease, etc.). A different median value of aerated residual lung volume was observed in the Delta variant groups: median value of aerated residual lung volume was 46.70% in unvaccinated patients compared to 67.10% in vaccinated patients. In addition, in patients with Delta variant every other extracted volume by automatic tool showed a statistically significant difference between vaccinated and unvaccinated group. Statistically significant differences were observed for each extracted volume by automatic tool between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant of COVID-19. Good statistically significant correlations among volumes extracted by automatic tool for each lung lobe and overall radiological severity score were obtained (ICC range 0.71–0.86). GGO was the main sign of COVID-19 lesions on CT images found in 87 of the 91 (95.6%) patients. No statistically significant differences were observed in CT findings (ground glass opacities (GGO), consolidation or crazy paving sign) among patient groups. Conclusion: In our study, we showed that in critically ill patients no difference were observed in terms of severity of disease or exitus, between unvaccinated and vaccinated patients. The only statistically significant differences were observed, with regard to the severity of COVID-19 pulmonary parenchymal involvement, between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant, and between unvaccinated patients with Delta variant and vaccinated patients with Delta variant.

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“…Another retrospective analysis of the practical use of CO-RADS in the emergency department of patients with possible COVID-19 infection also showed a great sensitivity and specificity of CO-RADS at 83.8% and 78.6%, respectively, with an AUC of 0.890 and provided a better risk classification by 65.8% in patients affected and by 82.1% in patients not affected by COVID-19 [26]. A study made by Özel et al in Turkey to assess CO-RADS model agrees with Dutch Radiology society findings by discovering this reporting scheme being incredibly effective in detecting COVID-19 pneumonia, especially, CO-RADS 5, which suggests a very high probability for infection and statistically significant correlation with RT-PCR results [32]. A comparison analysis of CO-RADS scale and severity scoring system (CT-SS), suggested by Yang at al.…”

Section: Discussionmentioning

confidence: 62%

Clinical Application of Covid-19 Reporting and Data System in Computed Tomography of Bilateral Pneumonia Diagnostic

Kulnickaitė¹,

Dobrovaitė²,

Grigaitė³

et al. 2021

Health Sciences

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Background: the 2019 coronavirus disease pandemic (COVID-19) has spread at an astonishing speed across the world, causing major morbidity and mortality. Computed tomography (CT) examination plays an important role in crisis areas in the diagnosis of COVID-19. COVID-19 Reporting and Data System (CO-RADS) has a five-point scale of suspicion for COVID-19 pneumonia in chest CT picture which standardizes the evaluation scheme and simplifies reporting. Aim: to summarise and present the role of COVID-19 Reporting and Data System in computed tomography of bilateral pneumonia diagnostic. Materials and methods: recently published studies were reviewed to evaluate COVID-19 Reporting and Data System scale as effective tool to detect COVID-19 pneumonia on chest CT scans. Databases from the subscription list of Lithuanian University of Health Sciences were selected: Medline (PubMed), SpringerLink and ScienceDirect. Results: chest CT features, as bilateral involvement, subpleural or peripherally distributed GGO, consolidation, reticulation, crazy paving pattern, air bronchogram signs, intralobular septal thickening, pulmonary vascular enlargement, are considered to be characteristic manifestations of COVID-19 infection. Studies show that Dutch Radiological Society presented CO-RADS scale sensitivity and specificity may vary from 61-88% and 66,4-98%, respectively. Conclusion: chest CT scan has a high sensitivity for COVID-19 diagnosis and could reduce false negative results obtained from RT-PCR tests. Furthermore, a standardized reporting system could increase clarification, minimize reporting variability and help radiologists recognize the results they observe, especially, for less experienced specialists.

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Use of the COVID-19 Reporting and Data System (CO-RADS) classification and chest computed tomography involvement score (CT-IS) in COVID-19 pneumonia

Cited by 34 publications

References 21 publications

Radiological Imaging of Viral Pneumonia Cases Identified Before the COVİD-19 Pandemic Period and COVİD-19 Pneumonia Cases Comparison of Characteristics

Radiological Imaging of Viral Pneumonia Cases Identified Before the COVİD-19 Pandemic Period and COVİD-19 Pneumonia Cases Comparison of Characteristics

Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center

Clinical Application of Covid-19 Reporting and Data System in Computed Tomography of Bilateral Pneumonia Diagnostic

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