Validation of imaging reporting and data system of coronavirus disease 2019 lexicons CO-RADS and COVID-RADS with radiologists’ preference: a multicentric study

Atta, Haisam; Hasan, Hosam A.; El-Morshedy, Reham M.; Gabr, Adel; Abbas, Wael A.; El-Barody, Mohamed M.

doi:10.1186/s43055-021-00485-2

Cited by 4 publications

(3 citation statements)

References 29 publications

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“…This suggests that pulmonologists and house officers can also reliably apply the CO-RADS, although there is more variability compared to specialized radiologists. These results are consistent with those of previous studies [ 3 , 5 , 13 , 15 , 16 , 21 , 22 , 23 , 24 , 25 , 26 ]. Fonseca et al [ 3 ] emphasized the substantial inter-observer agreement among the three readers for CO-RADS classifications, even three months after the initial case analysis, and without any additional training (κ = 0.642).…”

Section: Discussionsupporting

confidence: 94%

“…Nair et al [ 24 ] reported an overall moderate inter-observer agreement for CO-RADS categories among the six readers (κ = 0.548). Atta et al [ 25 ] reported an overall substantial agreement among three readers (κ = 0.78). Sushentsev et al [ 26 ] demonstrated moderate inter-observer agreement among the three readers for the CO-RADS, with a κ value of 0.51.…”

Section: Discussionmentioning

confidence: 99%

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Inter-observer Variability in the Analysis of CO-RADS Classification for COVID-19 Patients

Almalki,

Basha,

Metwally

et al. 2023

TropicalMed

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During the early stages of the pandemic, computed tomography (CT) of the chest, along with serological and clinical data, was frequently utilized in diagnosing COVID-19, particularly in regions facing challenges such as shortages of PCR kits. In these circumstances, CT scans played a crucial role in diagnosing COVID-19 and guiding patient management. The COVID-19 Reporting and Data System (CO-RADS) was established as a standardized reporting system for cases of COVID-19 pneumonia. Its implementation necessitates a high level of agreement among observers to prevent any potential confusion. This study aimed to assess the inter-observer agreement between physicians from different specialties with variable levels of experience in their CO-RADS scoring of CT chests for confirmed COVID-19 patients, and to assess the feasibility of applying this reporting system to those having little experience with it. All chest CT images of patients with positive RT-PCR tests for COVID-19 were retrospectively reviewed by seven observers. The observers were divided into three groups according to their type of specialty (three radiologists, three house officers, and one pulmonologist). The observers assessed each image and categorized the patients into five CO-RADS groups. A total of 630 participants were included in this study. The inter-observer agreement was almost perfect among the radiologists, substantial among a pulmonologist and the house officers, and moderate-to-substantial among the radiologists, the pulmonologist, and the house officers. There was substantial to almost perfect inter-observer agreement when reporting using the CO-RADS among observers with different experience levels. Although the inter-observer variability among the radiologists was high, it decreased compared to the pulmonologist and house officers. Radiologists, house officers, and pulmonologists applying the CO-RADS can accurately and promptly identify typical CT imaging features of lung involvement in COVID-19.

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Section: Discussionsupporting

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Inter-observer Variability in the Analysis of CO-RADS Classification for COVID-19 Patients

Almalki,

Basha,

Metwally

et al. 2023

TropicalMed

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“…For the overall CT categories, the interobserver agreement was reported in 18 studies: 12 studies reported diagnostic accuracy and the interobserver agreement together [ 9 , 20 , 22 , 25 , 28 , 36 – 39 , 45 , 51 , 54 ] and six studies solely reported the interobserver agreement [ 59 , 61 – 65 ]. They reported κ values ranged from 0.43 to 0.90.…”

Section: Resultsmentioning

confidence: 99%

Diagnostic performance of standardized typical CT findings for COVID-19: a systematic review and meta-analysis

2023

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Objective To meta-analyze diagnostic performance measures of standardized typical CT findings for COVID-19 and examine these measures by region and national income. Methods MEDLINE and Embase were searched from January 2020 to April 2022 for diagnostic studies using the Radiological Society of North America (RSNA) classification or the COVID-19 Reporting and Data System (CO-RADS) for COVID-19. Patient and study characteristics were extracted. We pooled the diagnostic performance of typical CT findings in the RSNA and CO-RADS systems and interobserver agreement. Meta-regression was performed to examine the effect of potential explanatory factors on the diagnostic performance of the typical CT findings. Results We included 42 diagnostic performance studies with 6777 PCR-positive and 9955 PCR-negative patients from 18 developing and 24 developed countries covering the Americas, Europe, Asia, and Africa. The pooled sensitivity was 70% (95% confidence interval [CI]: 65%, 74%; I2 = 92%), and the pooled specificity was 90% (95% CI 86%, 93%; I2 = 94%) for the typical CT findings of COVID-19. The sensitivity and specificity of the typical CT findings did not differ significantly by national income and the region of the study (p > 0.1, respectively). The pooled interobserver agreement from 19 studies was 0.72 (95% CI 0.63, 0.81; I2 = 99%) for the typical CT findings and 0.67 (95% CI 0.61, 0.74; I2 = 99%) for the overall CT classifications. Conclusion The standardized typical CT findings for COVID-19 provided moderate sensitivity and high specificity globally, regardless of region and national income, and were highly reproducible between radiologists. Critical relevance statement Standardized typical CT findings for COVID-19 provided a reproducible high diagnostic accuracy globally. Key points Standardized typical CT findings for COVID-19 provide high sensitivity and specificity. Typical CT findings show high diagnosability regardless of region or income. The interobserver agreement for typical findings of COVID-19 is substantial. Graphical abstract

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Diagnostic accuracy and inter-observer agreement with the CO-RADS lexicon for CT chest reporting in COVID-19

et al. 2021

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Purpose To measure the diagnostic accuracy and inter-observer agreement with the use of COVID-19 Reporting and Data System (CO-RADS) for detection of COVID-19 on CT chest imaging. Methods This retrospective study included 164 consecutive patients with clinical suspicion of COVID-19 in whom a CT chest examination was performed at a single institution between April 2020 and July 2020. Of them, 101 patients was RT-PCR positive for COVID-19. Six readers with varying radiological experience (two each of chest radiologists, general radiologists, and radiologists in training) independently assigned a CO-RADS assessment category for each CT chest study. The Fleiss’ K was used to quantify inter-observer agreement. The inter-observer agreement was also assessed based on the duration of onset of symptoms to CT scan. ROC curve analysis was used to determine the diagnostic accuracy of CO-RADS. The area under curve was calculated to determine the reader accuracy for detection of COVID-19 lung involvement with RT-PCR as reference standards. The data sets were plotted in ROC space, and Youden’s J statistic was calculated to determine the threshold cut-off CO-RADS category for COVID-19 positivity. Results There was overall moderate inter-observer agreement between all readers (Fleiss’ K 0.54 [95% CI 0.54, 0.54]), with substantial agreement among chest radiologists (Fleiss’ K 0.68 [95% CI 0.67, 0.68]), general radiologists (Fleiss’ K 0.61 [95% CI 0.61, 0.61]), and moderate agreement among radiologists-in-training (Fleiss’ K 0.56 [95% CI 0.56, 0.56]). There was overall moderate inter-observer agreement in early disease (stages 1 and 2), with cumulative Fleiss’ K 0.45 [95% CI 0.45, 0.45]). The overall AUC for CO-RADS lexicon scheme to accurately diagnose COVID-19 yielded 0.92 (95% CI 0.91, 0.94) with strong concordance within and between groups, of chests radiologists with AUC of 0.91 (95% CI 0.88, 0.94), general radiologists with AUC 0.96 (95% CI 0.94, 0.98), and radiologists in training with AUC of 0.90 (95% CI 0.87, 0.94). For detecting COVID-19, ROC curve analysis yielded CO-RADS > 3 as the cut-off threshold with sensitivity 90% (95% CI 0.88, 0.93), and specificity of 87% (95% CI 0.83, 0.91). Conclusion Readers across different levels of experience could accurately identify COVID-19 positive patients using the CO-RADS lexicon with moderate inter-observer agreement and high diagnostic accuracy.

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Validation of imaging reporting and data system of coronavirus disease 2019 lexicons CO-RADS and COVID-RADS with radiologists’ preference: a multicentric study

Cited by 4 publications

References 29 publications

Inter-observer Variability in the Analysis of CO-RADS Classification for COVID-19 Patients

Inter-observer Variability in the Analysis of CO-RADS Classification for COVID-19 Patients

Diagnostic performance of standardized typical CT findings for COVID-19: a systematic review and meta-analysis

Diagnostic accuracy and inter-observer agreement with the CO-RADS lexicon for CT chest reporting in COVID-19

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