COVID-19 Reporting And Data System Research Articles

Evaluation of the RSNA and CORADS classifications for COVID-19 on chest computed tomography in the Brazilian population

OBJECTIVE:To determine the correlation between the two tomographic classifications for coronavirus disease (COVID-19), COVID-19 Reporting and Data System (CORADS) and Radiological Society of North America Expert Consensus Statement on Reporting Chest Computed Tomography (CT) Findings Related to COVID-19 (RSNA), in the Brazilian population and to assess the agreement between reviewers with different experience levels.METHODS:Chest CT images of patients with reverse transcriptase-polymerase chain reaction (RT-PCR)-positive COVID-19 were categorized according to the CORADS and RSNA classifications by radiologists with different levels of experience and who were initially unaware of the RT-PCR results. The inter- and intra-observer concordances for each of the classifications were calculated, as were the concordances between classifications.RESULTS:A total of 100 patients were included in this study. The RSNA classification showed an almost perfect inter-observer agreement between reviewers with similar experience levels, with a kappa coefficient of 0.892 (95% confidence interval [CI], 0.788-0.995). CORADS showed substantial agreement among reviewers with similar experience levels, with a kappa coefficient of 0.642 (95% CI, 0.491-0.793). There was inter-observer variation when comparing less experienced reviewers with more experienced reviewers, with the highest kappa coefficient of 0.396 (95% CI, 0.255-0.588). There was a significant correlation between both classifications, with a Kendall coefficient of 0.899 (p<0.001) and substantial intra-observer agreement for both classifications.CONCLUSION:The RSNA and CORADS classifications showed excellent inter-observer agreement for reviewers with the same level of experience, although the agreement between less experience reviewers and the reviewer with the most experience was only reasonable. Combined analysis of both classifications with the first RT-PCR results did not reveal any false-negative results for detecting COVID-19 in patients.

Diagnosis and Management of Coronavirus Disease 2019 (COVID-19): Patients with Presented with Abdominal Pain, Fever and Shortness of Breath in Pandemic.

The aim of this study was to evaluate the admission chest and abdominal computed tomography (CT) findings of patients who presented to the emergency department with complaints of abdominal pain, fever, and shortness of breath and who had RT-PCR-confirmed COVID-19 infection. Seventy-five patients with RT-PCR-confirmed (in laboratory) COVID-19 infection who underwent chest and abdominal CT were included in the study. The radiological scales [the COVID-19 Reporting and Data System (CO-RADS) and severity score] of the chest and abdominal findings were examined on CT images. Forty-one (54.7%) patients were male and 34 (45.3%) were female. The mean age of the patients was 63.03 (range 24-89) years. The most frequently calculated CO-RADS score was found to be 5 (n= 53, 70.7%). Bilateral (72.0%) and multibolar (74.7%) involvement, peripheral (72.0%) and posterior (60.0%) distribution, and ground-glass opacity (66.7%) pattern were the most common pulmonary findings. A positive correlation was observed between CO-RADS and total severty score (p< 0.001). All patients were hospitalized. One (1.3%) patient was surgically treated because of acute appendicitis. Nine (12.0%) patients were admitted to the intensive care unit. Six (8.0%) patients died in the intensive care unit. Patients presenting to the emergency department with both abdominal and respiratory complaints during the pandemic should be evaluated for COVID-19. Patients can be diagnosed early with the data collected from CT without waiting for the PCR result. Hospital staff can take the necessary protective measures against virus transmission early, minimizing the in-hospital transmission of the virus.

Chest CT in COVID-19 at the ED: Validation of the COVID-19 Reporting and Data System (CO-RADS) and CT Severity Score: A Prospective, Multicenter, Observational Study

BackgroundCT is thought to play a key role in coronavirus disease 2019 (COVID-19) diagnostic workup. The possibility of comparing data across different settings depends on the systematic and reproducible manner in which the scans are analyzed and reported. The COVID-19 Reporting and Data System (CO-RADS) and the corresponding CT severity score (CTSS) introduced by the Radiological Society of the Netherlands (NVvR) attempt to do so. However, this system has not been externally validated.Research QuestionWe aimed to prospectively validate the CO-RADS as a COVID-19 diagnostic tool at the ED and to evaluate whether the CTSS is associated with prognosis.Study Design and MethodsWe conducted a prospective, observational study in two tertiary centers in The Netherlands, between March 19 and May 28, 2020. We consecutively included 741 adult patients at the ED with suspected COVID-19, who received a chest CT and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) PCR (PCR). Diagnostic accuracy measures were calculated for CO-RADS, using PCR as reference. Logistic regression was performed for CTSS in relation to hospital admission, ICU admission, and 30-day mortality.ResultsSeven hundred forty-one patients were included. We found an area under the curve (AUC) of 0.91 (CI, 0.89-0.94) for CO-RADS using PCR as reference. The optimal CO-RADS cutoff was 4, with a sensitivity of 89.4% (CI, 84.7-93.0) and specificity of 87.2% (CI, 83.9-89.9). We found a significant association between CTSS and hospital admission, ICU admission, and 30-day mortality; adjusted ORs per point increase in CTSS were 1.19 (CI, 1.09-1.28), 1.23 (1.15-1.32), 1.14 (1.07-1.22), respectively. Intraclass correlation coefficients for CO-RADS and CTSS were 0.94 (0.91-0.96) and 0.82 (0.70-0.90).InterpretationOur findings support the use of CO-RADS and CTSS in triage, diagnosis, and management decisions for patients presenting with possible COVID-19 at the ED.

Chest CT in the Emergency Department for Diagnosis of COVID-19 Pneumonia: Dutch Experience.

BackgroundClinicians need rapid and reliable diagnosis of coronavirus disease 2019 (COVID-19) for proper risk stratification, isolation strategies, and treatment decisions.PurposeTo assess the real-life performance of radiologist emergency department chest CT interpretation for diagnosing COVID-19 during the acute phase of the pandemic, using the COVID-19 reporting and data system (CO-RADS).Materials and MethodsThis retrospective multicenter study included consecutive patients who presented to emergency departments in six medical centers between March and April 2020 with moderate to severe upper respiratory symptoms suspicious for COVID-19. As part of clinical practice, chest CT was obtained for primary workup and scored using the 5-point CO-RADS scheme for suspicion of COVID-19. CT was compared with SARS-CoV-2 RT-PCR, and a clinical reference standard established by a multidisciplinary group of clinicians based on RT-PCR, COVID-19 contact history, oxygen therapy, timing of RT-PCR testing and likely alternative diagnosis. Performance of CT was estimated using area under the receiver operating characteristics curve (AUC) analysis and diagnostic odds ratios (OR) against both reference standards. Subgroup analysis was performed based on symptom duration grouped presentations of < 48 hours, 48 hours through 7 days, and > 7 days.ResultsA total of 1070 patients (median age 66, IQR 54-75, 626 men) were included, of whom 536/1070 (50%) had a positive RT-PCR, 137/1070 (13%) patients were considered to have a possible or probable COVID- 19 based on the clinical reference standard. Chest CT yielded an AUC of 0.87 (95%CI 0.84-0.89) compared with RT-PCR and 0.87 (95%CI 0.85-0.89) compared with the clinical reference standard. A CO-RADS score ≥4 yielded an OR of 25.9 (95%CI 18.7-35.9) for a COVID-19 diagnosis by RT-PCR, and an OR of 30.6 (95%CI 21.1-44.4) by the clinical reference standard. For symptom duration of less than 48 hours, the AUC fell to 0.71 (95%CI 0.62-0.80; P<.001).ConclusionChest CT analysis using the COVID-19 reporting and data system (CO-RADS) enables rapid and reliable diagnosis of COVID-19, particularly when symptom duration is greater than 48 hours.See also the editorial by Elicker.

Comparison of Chest CT Grading Systems in Coronavirus Disease 2019 (COVID-19) Pneumonia.

PurposeTo compare the performance and interobserver agreement of the COVID-19 Reporting and Data System (CO-RADS), the COVID-19 imaging reporting and data system (COVID-RADS), the RSNA expert consensus statement, and the British Society of Thoracic Imaging (BSTI) guidance statement.Materials and MethodsIn this case-control study, total of 100 symptomatic patients suspected of having COVID-19 were included: 50 patients with COVID-19 (59±17 years, 38 men) and 50 patients without COVID-19 (65±24 years, 30 men). Eight radiologists independently scored chest CT images of the cohort according to each reporting system. The area under the receiver operating characteristic curves (AUC) and interobserver agreements were calculated and statistically compared across the systems.ResultsA total of 800 observations were made for each system. The level of suspicion of COVID-19 correlated with the RT-PCR positive rate except for the “negative for pneumonia” classifications in all the systems (Spearman’s coefficient: ρ=1.0, P=<.001 for all the systems). Average AUCs were as follows: CO-RADS, 0.84 (95% confidence interval, 0.83–0.85): COVID-RADS, 0.80 (0.78–0.81): the RSNA statement, 0.81 (0.79–0.82): and the BSTI statement, 0.84 (0.812-0.86). Average Cohen’s kappa across observers was 0.62 (95% confidence interval, 0.58–0.66), 0.63 (0.58–0.68), 0.63 (0.57–0.69), and 0.61 (0.58-0.64) for CO-RADS, COVID-RADS, the RSNA statement and the BSTI statement, respectively. CO-RADS and the BSTI statement outperformed COVID-RADS and the RSNA statement in diagnostic performance (P=.<.05 for all the comparison).ConclusionsCO-RADS, COVID-RADS, the RSNA statement and the BSTI statement provided reasonable performances and interobserver agreements in reporting CT findings of COVID-19.

Clinical application of the COVID-19 Reporting and Data System (CO-RADS) in patients with suspected SARS-CoV-2 infection: observational study in an emergency department

• CO-RADS improves the risk classification by 65.8% in patients affected by COVID-19. • CO-RADS combined with RT-PCR presents an AUC of 0.98 for patients with COVID-19. • CO-RADS improves the risk classification by 82.1% in patients not affected by COVID-19.

Predictors of Severity in Covid-19 Patients in Casablanca, Morocco.

BackgroundMorocco was affected, as were other countries, by the coronavirus disease 2019 (COVID-19) pandemic. Many risk factors of COVID-19 severity have been described, but data on infected patients in North Africa are limited. We aimed to explore the predictive factors of disease severity in COVID-19 patients in a tertiary hospital in Casablanca.MethodsIn this single-center, retrospective, observational study, we included all adult patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, admitted to Sheikh Khalifa International University Hospital in Casablanca between March 18 and May 20, 2020. Patients were separated into two groups: Non-severe patients were those with mild or moderate forms of COVID-19, and severe patients were those admitted to the intensive care unit (ICU) who had one of the following signs-respiratory rate > 30 breaths/min; oxygen saturation < 93% on room air; acute respiratory distress syndrome (ARDS); or required mechanical ventilation. Demographic, clinical, laboratory data, and outcomes were reviewed. We used univariable and multivariable logistic regression to explore predictive factors of severity.ResultsWe reported 134 patients with confirmed SARS-CoV-2 infection. The median age was 53 years (interquartile range [IQR], 36-64), and 73 (54.5%) were men. Eighty-nine non-severe patients (66.4%) were admitted to single bedrooms, and 45 (33.6%) were placed in the ICU. The median time from illness onset to hospital admission was seven days (IQR, 3.0-7.2). Ninety-nine patients (74%) were admitted directly to the hospital, and 35 (26%) were transferred from other structures. Also, 68 patients (65.4%) were infected in clusters. Of the 134 patients, 61 (45.5%) had comorbidities, such as hypertension (n = 36; 26.9%), diabetes (n = 19; 14.2%), and coronary heart disease (n = 16; 11.9%). The most frequent symptoms were fever (n = 61; 45.5%), dry cough (n = 59; 44%), and dyspnea (n = 39; 29%). A total of 127 patients received hydroxychloroquine and azithromycin (95%). Eleven critical cases received lopinavir/ritonavir (8.2%). Five patients received tocilizumab (3.7%). We reported 13 ARDS cases in ICU patients (29%), eight with acute kidney injury (17.8%), and four thromboembolic events (8.8%). Fourteen ICU patients (31.1%) died at 28 days. In univariable analysis, older men with one or more comorbidities, infection in a cluster, chest scan with the COVID-19 Reporting and Data System (CO-RADS) 5, lymphopenia, high rates of ferritin, C-reactive protein (CRP), D-dimer, and lactate dehydrogenase were associated with severe forms of COVID-19. Multivariable logistic regression model founded increasing odds of severity associated with older age (odds ratio [OR] 1.05, 95% confidence interval [CI] 1.01-1.09, P = .0039), men (OR 3.19, CI 1.06-9.60, P = .016), one or more comorbidities (OR 4.36, CI 1.32-14.45, P = .016), CRP > 10 mg/L (OR 5.47, CI 1.57-19.10, P = .008), and lymphopenia lower than 0.8 x109/L (OR 6.65, CI 1.43-30.92, P = .016).ConclusionsClinicians should consider older male patients with comorbidities, lymphopenia, and a high CRP rate as factors to predict severe forms of COVID-19 earlier. The higher severity of infected patients in clusters must be confirmed by epidemiological and genetic studies.

Evaluation of the Usefulness of CO-RADS for Chest CT in Patients Suspected of Having COVID-19.

The purpose of this study was to use the Coronavirus Disease 2019 (COVID-19) Reporting and Data System (CO-RADS) to evaluate the chest computed tomography (CT) images of patients suspected of having COVID-19, and to investigate its diagnostic performance and interobserver agreement. The Dutch Radiological Society developed CO-RADS as a diagnostic indicator for assessing suspicion of lung involvement of COVID-19 on a scale of 1 (very low) to 5 (very high). We investigated retrospectively 154 adult patients with clinically suspected COVID-19, between April and June 2020, who underwent chest CT and reverse transcription-polymerase chain reaction (RT-PCR). The patients’ average age was 61.3 years (range, 21–93), 101 were male, and 76 were RT-PCR positive. Using CO-RADS, four radiologists evaluated the chest CT images. Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were calculated. Interobserver agreement was calculated using the intraclass correlation coefficient (ICC) by comparing the individual reader’s score to the median of the remaining three radiologists. The average sensitivity was 87.8% (range, 80.2–93.4%), specificity was 66.4% (range, 51.3–84.5%), and AUC was 0.859 (range, 0.847–0.881); there was no significant difference between the readers (p > 0.200). In 325 (52.8%) of 616 observations, there was absolute agreement among observers. The average ICC of readers was 0.840 (range, 0.800–0.874; p < 0.001). CO-RADS is a categorical taxonomic evaluation scheme for COVID-19 pneumonia, using chest CT images, that provides outstanding performance and from substantial to almost perfect interobserver agreement for predicting COVID-19.

Automated Assessment of COVID-19 Reporting and Data System and Chest CT Severity Scores in Patients Suspected of Having COVID-19 Using Artificial Intelligence.

BackgroundThe COVID-19 pandemic has spread across the globe with alarming speed, morbidity and mortality. Immediate triage of suspected patients with chest infections caused by COVID-19 using chest CT may be of assistance when results from definitive viral testing are delayed.PurposeTo develop and validate an artificial intelligence (AI) system to score the likelihood and extent of pulmonary COVID-19 on chest CT scans using the CO-RADS and CT severity scoring systems.Materials and MethodsCORADS-AI consists of three deep learning algorithms that automatically segment the five pulmonary lobes, assign a CO-RADS score for the suspicion of COVID-19 and assign a CT severity score for the degree of parenchymal involvement per lobe. This study retrospectively included patients who received an unenhanced chest CT scan due to clinical suspicion of COVID-19 at two medical centers. The system was trained, validated, and tested with data from one of the centers. Data from the second center served as an external test set. Diagnostic performance and agreement with scores assigned by eight independent observers were measured using receiver operating characteristic (ROC) analysis, linearly-weighted kappa and classification accuracy.Results105 patients (62 ± 16 years, 61 men) and 262 patients (64 ± 16 years, 154 men) were evaluated in the internal and the external test set, respectively. The system discriminated between COVID-19 positive and negative patients with areas under the ROC curve of 0.95 (95% CI: 0.91-0.98) and 0.88 (95% CI: 0.84-0.93). Agreement with the eight human observers was moderate to substantial with a mean linearly-weighted kappa of 0.60 ± 0.01 for CO-RADS scores and 0.54 ± 0.01 for CT severity scores.ConclusionCORADS-AI correctly identified COVID-19 positive patients with high diagnostic performance from chest CT exams, assigned standardized CO-RADS and CT severity scores in good agreement with eight independent observers and generalized well to external data.

FDG-PET/CT imaging during the Covid-19 emergency: a southern Italian perspective.

PurposeTo assess the impact of the Covid-19 pandemic on FDG-PET/CT work volume and to evaluate the occurrence of abnormal imaging findings suspicious or potentially diagnostic for interstitial pneumonia by Covid-19 infection in south Italy.MethodsWe retrospectively reviewed the number and the findings of FDG-PET/CT studies acquired between February and April 2020 during the Covid-19 pandemic at the University of Napoli Federico II. The number and the findings of FDG-PET/CT studies acquired in the corresponding period of 2019 were also assessed for direct comparison.ResultsThe number of FDG-PET/CT studies performed during the pandemic (n = 299) and in the corresponding period of 2019 (n = 335) were comparable. The percentage of abnormal FDG-PET/CT findings, suspicious for interstitial pneumonia by Covid-19 infection, was significantly higher during the pandemic (9%) compared with that found in the corresponding period of 2019 (4%) (χ2 5.45, P = 0.02). No significant differences were observed in the distribution of Covid-19 reporting and data system (CO-RADS) classification and in the maximum standardized uptake value between the pandemic (2.6 ± 2.2) and the corresponding period of 2019 (3.2 ± 1.4). Of note, patients with abnormal imaging findings during the pandemic time had clinical data and/or laboratory tests negative for Covid-19 infection.ConclusionDespite the restrictive medical measures for the emergency, the number of FDG-PET/CT studies was unchanged during the pandemic compared with the previous year. Our findings also indicate that Covid-19 infection was contained in our series of patients from southern Italy.

CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19-Definition and Evaluation.

PurposeTo introduce the COVID-19 Reporting and Data System (CO-RADS) for standardized assessment of pulmonary involvement of COVID-19 on non-enhanced chest CT and report its initial interobserver agreement and performance.MethodsThe Dutch Radiological Society (NVvR) developed CO-RADS based on other efforts for standardization, such as Lung-RADS or BI-RADS. CO-RADS assesses the suspicion for pulmonary involvement of COVID-19 on a scale from 1 (very low) to 5 (very high). The system is meant to be used in patients presenting with moderate to severe symptoms of COVID-19. The system was evaluated using 105 chest CTs of patients admitted to the hospital with clinical suspicion of COVID-19 in whom RT-PCR was performed (62 +/- 16 years, 61 men, 53 with positive RT-PCR). Eight observers assessed the scans using CO-RADS. Fleiss’ kappa was calculated, and scores of individual observers were compared to the median of the remaining seven observers. The resulting area under the receiver operating characteristics curve (AUC) was compared to results from RT-PCR and clinical diagnosis of COVID-19.ResultsThere was absolute agreement among observers in 573 (68.2%) of 840 observations. Fleiss’ kappa was 0.47 (95% confidence interval (CI) 0.45-0.47), with the highest kappa for CO-RADS categories 1 (0.58, 95% CI 0.54-0.62) and 5 (0.68, 95% CI 0.65-0.72). The average AUC was 0.91 (95% CI 0.85-0.97) for predicting RT-PCR outcome and 0.95 (95% CI 0.91-0.99) for clinical diagnosis. The false negative rate for CO-RADS 1 was 9/161 (5.6%, 95% CI 1.0-10%), and the false positive rate for CO-RADS 5 was 1/286 (0.3%, 95% CI 0-1.0%).ConclusionsCO-RADS is a categorical assessment scheme for pulmonary involvement of COVID-19 on non-enhanced chest CT providing very good performance for predicting COVID-19 in patients with moderate to severe symptoms and has a substantial interobserver agreement, especially for categories 1 and 5.