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

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.

Metabolic characterization of structural lung changes in patients with findings suggestive of incidental COVID-19 pneumonia on 18F-FDG PET/CT. Pathophysiological insights from multimodal images obtained during the pandemic

PURPOSE

To evaluate the metabolic uptake of different tomographic signs observed in patients with incidental structural findings suggestive of COVID-19 pneumonia through 18F-FDG PET/CT.

MATERIALS AND METHODS

We retrospectively analyzed 596 PET/CT studies performed from February 21, 2020 to April 17, 2020. After excluding 37 scans (non-18F-FDG PET tracers and brain studies), we analyzed the metabolic activity of several structural changes integrated in the CO-RADS score using the SUVmax of multimodal studies with 18F-FDG.

RESULTS

Forty-three patients with 18F-FDG PET/CT findings suggestive of COVID-19 pneumonia were included (mean age: 68±12.3 years, 22 male). SUVmax values were higher in patients with CO-RADS categories 5-6 than in those with lower CO-RADS categories (6.1±3.0 vs. 3.6±2.1, p=0.004). In patients with CO-RADS 5-6, ground-glass opacities, bilaterality and consolidations exhibited higher SUVmax values (p-values of 0.01, 0.02 and 0.01, respectively). Patchy distribution and crazy paving pattern were also associated with higher SUVmax (p-values of 0.002 and 0.01). After multivariate analysis, SUVmax was significantly associated with a positive structural diagnosis of COVID-19 pneumonia (odds ratio=0.63, 95% confidence interval=0.41-0.90; p=0.02). The ROC curve of the regression model intended to confirm or rule out the structural diagnosis of COVID-19 pneumonia showed an AUC of 0.77 (standard error=0.072, p=0.003).

CONCLUSIONS

In those patients referred for standard oncologic and non-oncologic indications (43/559; 7.7%) during pandemic, imaging with 18F-FDG PET/CT is a useful tool during incidental detection of COVID-19 pneumonia. Several CT findings characteristic of COVID-19 pneumonia, specifically those included in diagnostic CO-RADS scores (5-6), were associated with higher SUVmax values.

Effects of Patient-Based Imaging Artifacts On CT Diagnosis of COVID-19 and Its Severity

PURPOSE

To investigate the effects of patient-induced artifacts on the diagnostic performance of the COVID-19 Reporting and Data System (CO-RADS) and the computed tomography chest severity score (CT-SS).

METHODS

A single-center retrospective analysis of patients aged 18 years and older who were admitted to the authors' hospital with laboratory-confirmed COVID-19 and underwent chest CT between July and November 2021 was conducted. Patients' chest CT scans were examined by 3 radiologists for CT-SS and CO-RADS classifications. Patient-based artifacts, including metal artifacts, incomplete projection artifacts, motion artifacts, and insufficient inspiration, were identified by 3 readers who were unaware of each other. For statistical analysis, interreader agreement was investigated using Fleiss kappa () agreement analysis.

RESULTS

The study population included 549 patients with a median age of 66 years (IQR, 55-75 years), 321 (58.5%) of whom were men. According to the overall CO-RADS classification, the highest interreader agreement was in patients without CT artifacts ( = 0.924), while the lowest interreader agreement was in patients with motion artifacts ( = 0.613). For the CO-RADS 1 and 2 patient groups, insufficient inspiration decreased the interreader agreement most ( = 0.712 and = 0.250, respectively). For the CO-RADS 3, 4, and 5 patient groups, motion artifacts reduced the interreader agreement most ( = 0.464, = 0.453, and = 0.705, respectively). For total CT-SS, the highest kappa value was in patients without artifacts ( = 0.574), while the lowest kappa value was in patients with motion artifacts ( = 0.374).

DISCUSSION

The CT technologist can avoid patient-induced artifacts by placing patients carefully on the CT table, giving patients necessary instructions before CT acquisition, and selecting optimal scanning parameters. The authors are not aware of another study in the literature investigating the effects of patient-based artifacts on interreader agreement of CO-RADS classification and CT-SS for COVID-19.

CONCLUSION

CT artifacts degrade image quality and might lead to interreader disagreement of CO-RADS classification and CT-SS for patients with COVID-19.

Immune checkpoint inhibitor-related pneumonitis and COVID-19: a case-matched comparison of CT findings

OBJECTIVES

To compare the radiological findings of immune checkpoint inhibitor-related pneumonitis (IRP) and COVID-19 pneumonia, evaluating the potential of the CO-RADS score to differentiate between them.

METHODS

Two readers blindly reviewed chest CTs from age- and sex-matched groups of 33 patients with IRP and 33 patients with COVID-19 pneumonia. Each examiner evaluated the presence of 13 CT features, semiquantitatively scored lung involvement, and assigned a CO-RADS score. Inter-reader reliability in the assessment of CT features and CO-RADS categories was evaluated with Cohen's κ. Distribution differences between groups were evaluated with the χ², Fisher's, and Mann-Whitney U tests.

RESULTS

Substantial or higher inter-reader reliability was found in CO-RADS assignments (κ = 0.664) and in the evaluation of CT features (κ ≥ 0.638), among which the sole feature found to significantly differentiate IRP from COVID-19 pneumonia was unilateral presentation (p < 0.001). Lung involvement semiquantitative scores and CO-RADS scores were significantly higher (p < 0.001) in COVID patients (median involvement score 4, IQR 4-6; median CO-RADS score 5, IQR 4-5) than in IRP patients (median involvement score 2.5, IQR 2-4; median CO-RADS score 3, IQR 3-4) but exploratory analysis of CO-RADS specificity revealed comparatively low values, ranging between 51.5% (Reader 1) and 54.6% (Reader 2).

CONCLUSIONS

CT features of IRP and COVID-19 pneumonia frequently overlap, save for the extent of lung involvement and bilaterality. In the current SARS-CoV-2 pandemic, the low specificity of the CO-RADS score for the differential diagnosis of COVID-19 pneumonia and IRP may prompt to reconsider the role of imaging in IRP work-up.

Peripheral blood cell anomalies in COVID-19 patients in the United Arab Emirates: A single-centered study

Introduction

In this study, we aimed at exploring the morphologic and quantitative abnormalities in the peripheral blood counts of coronavirus disease 2019 (COVID-19) patients.

Methods

A cohort of 131 COVID-19 patients was recruited at University Hospital Sharjah (UHS), UAE. Their peripheral blood smears were examined for morphological evaluation. Also, their clinical laboratory investigations and radiological findings were retrieved from the medical records. Our cohort consisted of 63 males and 68 females with an age of 63.6 ± 18.6 years.

Results

The presence of atypical lymphocytes was observed in around 80% of the recruited COVID-19 patients. Further, monocytes with toxic cytoplasmic vacuoles were identified in 55% of the cases. Neutrophil-associated changes, including pseudo-Pelger-Huët, bands, and long nuclear endoplasm, were reported in around 25-35% of the patients. RBCs associated changes such as microcytic and hypochromic RBCs, as well as targetoid, dacrocytes, ovalocytes, echinocytes/burr cells, and schistocytes, were described. According to disease severity, RBCs chromicity was found to be significantly different between stable and critical patients. COVID-19 patients with CO-RADS 5 showed a similar change in RBCs as well as a decrease in the neutrophils with hypogranular cytoplasm.

Conclusion

Peripheral blood smear assessment in COVID-19 patients could provide information about the disease state and pulmonary involvement.

Clinical, laboratory and high-resolution computed tomography (HRCT) thorax profile of reverse transcription polymerase chain reaction (RT-PCR) negative COVID-19 suspects with moderate to severe disease

BACKGROUND

Diagnostic dilemma arises when patients with clinical suspicion of COVID-19 disease having moderate-to-severe respiratory symptoms yield negative result for COVID-19 in reverse transcription polymerase chain reaction (RT-PCR). This study evaluated the clinical, laboratory and HRCT thorax findings among RT-PCR-negative COVID-19 suspects with moderate-to-severe disease.

MATERIALS AND METHODS

A hospital-based retrospective observational study was conducted between July 2021 to December 2021, among 60 moderate and severe symptomatic COVID-19 suspects admitted in the severe acute respiratory illness (SARI) ward and intensive care unit (ICU), who were negative for COVID-19 in RT-PCR. Data were abstracted from the medical records section of the hospital using a predesigned data abstraction form and presented by descriptive statistics.

RESULTS

Mean age of study participants was 55.5 years (SD 14.1 years), and majority were males (n = 43, 71.7%). Common presenting symptoms were fever (n = 60, 100%), dyspnea (n = 57, 95%), and cough (n = 54, 90%). The common laboratory findings were rise of C-reactive protein (n = 60, 100%), NLR (n = 49, 81.7%), d-dimer (n = 47, 78.3%), ferritin (n = 46, 76.7%), and LDH (n = 40, 66.7%). HRCT scan of thorax revealed ground glass opacities with or without consolidations located bilaterally with diffuse or peripheral distribution, interlobar septal thickening (n = 43, 74.1%), vascular thickening (n = 35, ≥58.3%), and sub-pleural lines (n = 32, 53.3%). Median CT-SS value was 15 (IQR 11-19), and majority (n = 56, 93.3%) belonged to CO-RADS ≥4.

CONCLUSION

Diagnosis of COVID-19 can be presumed in RT-PCR-negative suspected COVID-19 patients with moderate-to-severe disease, with marked rise of inflammatory markers and HRCT revealing typical findings of COVID-19 pneumonia.

Investigation of the relationship of CO-RADS and CT patterns with laboratory parameters in COVID-19 patients and a new perspective on the total CT scoring system

Background

It is important to determine the correlation of the CO-RADS classification and computed tomography (CT) patterns of the lung with laboratory data. To investigate the relationship of CO-RADS categories and CT patterns with laboratory data in patients with a positive RT-PCR test. We also developed a structured total CT scoring system and investigated its correlation with the total CT scoring system.

Method

The CT examinations of the patients were evaluated in terms of the CO-RADS classification, pattern groups and total CT score. Structured total CT score values were obtained by including the total CT score values and pattern values in a regression analysis. The CT data were compared according to the laboratory data.

Results

A total of 198 patients were evaluated. There were significant differences between the CO-RADS groups in terms of age, ICU transfer, oxygen saturation, creatinine, LDH, D-dimer, high-sensitivity cardiac troponin-T (hs-TnT), CRP, structured total CT score values, and total CT score values. A significant difference was also observed between the CT pattern groups and oxygen saturation, creatinine and CRP values. When the structured total CT score values and total CT score values were compared they were observed to be correlated.

Conclusions

Creatinine can be considered as an important marker for the CO-RADS and pattern classifications in lung involvement. LDH can be considered as an important marker of parenchymal involvement, especially bilateral and diffuse involvement. The structured total CT scoring system is a new system that can be used as an alternative.

Diagnostic accuracy of CO-RADS in patients with suspected Coronavirus Disease-2019: A single center experience

INTRODUCTION

COVID-19 Reporting and Data System (CO-RADS) is a tool for standardizing the reports of patients with suspected or confirmed Sars-CoV-2 infection. We performed a study of the performance of the CO-RADS in a triage scenario of patients in Brazil.

METHODS

Data from 426 Computed Tomography (CT) scans from March 2020 through December 2020 were assessed in an ambidirectional, both retrospective and prospective, for the assessment in one of the six categories of the CO-RADS. We assessed sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), positive likelihood ratio (PLR), negative likelihood ratio (NLR) Youden's index, Positive and Negative Clinical Utility Index (UC + and UC- respectively) and diagnostic odds ratio (DOR). We also plotted Receiver Operating Characteristics (ROC) curve with Area Under the Curve (AUC) for CO-RADS of >4 (4 + 5).

RESULTS

For CO-RADS classification > 4 (4 + 5) considered positive, the AUC obtained was of 0.89 (95% CI of 0.02), sensitivity of 78% (95% CI of 0.3), specificity of 91% (95% CI of 0.3), PPV of 0.92 (95% CI of 0.02), NPV of 0.41 (95% CI of 0.03), PLR of 0.85 (95% CI of 0.2), and NLR of 0.23 (95% CI of 0.02).

CONCLUSION

CO-RADS demonstrated overall good diagnostic performance in stratifying patients with suspected Sars-CoV-2 infection, even those without confirmed laboratorial diagnosis, therefore being useful in a triage scenario with lack of resources.

Chest Computed Tomography Is an Efficient Method for Initial Diagnosis of COVID-19: An Observational Study

Coronavirus disease (COVID-19) is an infectious disease that can lead to pneumonia, pulmonary oedema, acute respiratory distress syndrome, multiple organ and system dysfunction, and death. This study aimed to verify the efficacy of chest computed tomography (CT) for the initial diagnosis of COVID-19. This observational, retrospective, cross-sectional study included 259 individuals who underwent clinical evaluation, blood collection, chest CT, and a reverse transcription polymerase chain reaction (RT-PCR) diagnostic test for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during their course of treatment at a reference hospital in Belém, Pará, Brazil between April and June 2020. Inclusion criteria were flu-like symptoms in adults of both sexes. Individuals with an inconclusive COVID-19 molecular test or who had artifacts in the chest CT images were excluded. Parametric data were analyzed using Student-t-test and non-parametric data were analyzed using average test and Fisher exact test. Participants were divided into two groups: Group 1 (COVID-19 positive), n = 211 (124 males, 87 females), 51.8 ± 17.9 years old and Group 2 (COVID-19 negative), n = 48 (22 males, 26 females), 47.6 ± 18.6 years old. Most frequent symptoms were cough [Group 1 n = 199 (94%)/Group 2 n = 46 (95%)], fever [Group 1 n = 154 (72%)/Group 2 n = 28 (58%)], myalgia [Group 1 n = 172 (81%)/Group 2 n = 38 (79%)], dyspnoea [Group 1 n = 169 (80%) / Group 2 n = 37 (77%)], headache [Group 1 n = 163 (77%)/Group 2 n = 32 (66%)], and anosmia [Group 1 n = 154 (73%)/Group 2 n = 29 (60%)]. Group 1 had a higher proportion of ground-glass opacity [Group 1 n = 175 (83%)/Group 2 n = 24 (50%), 0.00], vascular enhancement sign [Group 1 n = 128 (60%)/Group 2 n = 15 (31%), 0.00], septal thickening [Group 1 n = 99 (47%)/Group 2 n = 13 (27%), 0.01], crazy-paving pattern [Group 1 n = 98 (46%) / Group 2 n = 13 (27%), 0.01], consolidations [Group 1 n = 92 (43%)/Group 2 n = 8 (16%), 0.00], and CO-RADS 4 and 5 [Group 1 n = 163 (77.25%)/Group 2 n = 24 (50%), 0.00] categories in chest CT. Chest CT, when available, was found to be an efficient method for the initial diagnosis and better management of individuals with COVID-19.

Electrocardiographic QRS axis shift, rotation and COVİD-19

Background

In patients with coronavirus disease-2019 (COVID-19), severe dyspnea is the most dramatic complication. Severe respiratory difficulties may include electrocardiographic frontal QRS axis rightward shift (Rws) and clockwise rotation (Cwr).

Aim

This study investigated the predictability of advanced lung tomography findings with QRS axis shift and rotation.

Patients and Methods

This was a retrospective analysis of 160 patients. Patients were divided into the following two groups: normal (n = 80) and low (n = 80) oxygen saturation. These groups were further divided into four groups according to the rightward and leftward axis shift (Lws) on the electrocardiographic follow-up findings. These groups were compared in terms of electrocardiographic rotation (Cwr, counterclockwise rotation, or normal transition), tomographic stage (CO-RADS5(advanced)/CO-RADS1-4), electrocardiographic intervals, and laboratory findings.

Results

In patients with low oxygen saturation, the amount of QRS axis shift, Cwr, and tomographic stage were significantly higher in the Rws group than in the Lws group. There were no differences in the above parameters between the Rws and Lws groups in patients with normal oxygen saturation. Logistic regression analysis revealed that the presence of Cwr and Rws independently increased the risk of CO-RADS5 by 18.9 and 4.6 fold, respectively, in patients with low oxygen saturation.

Conclusion

In COVID-19 patients who have dyspnea with low oxygen saturation, electrocardiographically clockwise rotation with a rightward axis shift demonstrated good sensitivity (80% [0.657-0.943]) and specificity (80% [0.552->1]) for predicting advanced lung tomographic findings.

ClinicalTrialsgov Identifier

NCT04698083.

Radiological Findings Increased the Successful of COVID-19 Diagnosis in Hospitalized Patients Suspected of Respiratory Viral Infection but with a Negative First SARS-CoV-2 RT-PCR Result

SARS-CoV-2 is the etiological agent of COVID-19 and may evolve from asymptomatic disease to fatal outcomes. Real-time reverse-transcription polymerase chain reaction (RT-PCR) screening is the gold standard to diagnose severe accurate respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, but this test is not 100% accurate, as false negatives can occur. We aimed to evaluate the potential false-negative results in hospitalized patients suspected of viral respiratory disease but with a negative previous SARS-CoV-2 RT-PCR and analyze variables that may increase the success of COVID-19 diagnosis in this group of patients. A total of 55 hospitalized patients suspected of viral respiratory disease but with a previous negative RT-PCR result for SARS-CoV-2 were included. All the participants had clinical findings related to COVID-19 and underwent a second SARS-CoV-2 RT-PCR. Chest-computed axial tomography (CT) was used as an auxiliary tool for COVID-19 diagnosis. After the second test, 36 patients (65.5%) were positive for SARS-CoV-2 (COVID-19 group), and 19 patients (34.5%) were negative (controls). There were differences between the groups in the platelet count and the levels of D-dimer, procalcitonin, and glucose (p < 0.05). Chest CT scans categorized as COVID-19 Reporting and Data System 5 (CO-RADS 5) were more frequent in the COVID-19 group than in the control group (91.7% vs. 52.6%; p = 0.003). CO-RADS 5 remained an independent predictor of COVID-19 diagnosis in a second SARS-CoV-2 screening (p = 0.013; odds ratio = 7.0, 95% confidence interval 1.5−32.7). In conclusion, chest CT classified as CO-RADS 5 was an independent predictor of a positive second SARS-CoV-2 RT-PCR, increasing the odds of COVID-19 diagnosis by seven times. Based on our results, in hospitalized patients with a chest CT classified as CO-RADS 5, a second SARS-CoV-2 RT-PCR test should be mandatory when the first one is negative. This approach could increase SARS-CoV-2 detection up to 65% and could allow for isolation and treatment, thus improving the patient outcome and avoiding further contagion.

The Role of CO-RADS Scoring System in the Diagnosis of COVID-19 Infection and its Correlation with Clinical Signs

BACKGROUND

Computed tomography (CT) evaluation systematics has become necessary to eliminate the difference of opinion among radiologists in evaluating COVID-19 CT findings.

INTRODUCTION

The objectives of this study were to evaluate the efficiency of CO-RADS scoring system in our patients with COVID-19 as well as to examine its correlation with clinical and laboratory findings.

METHODS

The CO-RADS category of all patients included in the study was determined by a radiologist who did not know the rtRT-PCR test result of the patients, according to the Covid-19 reporting and data system of Mathias Prokop et al. Results: A total of 1338 patients were included. CT findings were positive in 66.3%, with a mean CO-RADS score of 3,4 ± 1,7. 444 (33.1%) of the patients were in the CO-RADS 1-2, 894 (66.9%) were in the CO-RADS 3-5 group. There were positive correlations between CO-RADS score and age, CMI, hypertension, diabetes mellitus, chronic pulmonary diseases presence of symptoms, symptom duration, presence of cough, shortness of breath, malaise, CRP, and LDH, while CORADS score was negatively correlated with lymphocyte count. The results of the ROC analysis suggested that those with age ≥40 years, symptom duration >2 days, CMI score >1 and/or comorbid conditions were more likely to have a CO-RADS score of 3-5.

CONCLUSION

The CO-RADS classification system is a CT findings assessment system that can be used to diagnose COVID-19 in patients with symptoms of cough, shortness of breath, myalgia and fatigue for more than two days.

The role of chest imaging in the diagnosis, management, and monitoring of coronavirus disease 2019 (COVID-19)

Coronavirus disease 2019 (COVID-19) pandemic has posed a major public health crisis all over the world. The role of chest imaging, especially computed tomography (CT), has evolved during the pandemic paralleling the accumulation of scientific evidence. In the early stage of the pandemic, the performance of chest imaging for COVID-19 has widely been debated especially in the context of comparison to real-time reverse transcription polymerase chain reaction. Current evidence is against the use of chest imaging for routine screening of COVID-19 contrary to the initial expectations. It still has an integral role to play, however, in its work up and staging, especially when assessing complications or disease progression. Chest CT is gold standard imaging modality for COVID-19 pneumonia; in some situations, chest X-ray or ultrasound may be an effective alternative. The most important role of radiologists in this context is to be able to identify those patients at greatest risk of imminent clinical decompensation by learning to stratify cases of COVID-19 on the basis of radiologic imaging in the most efficient and timely fashion possible. The present availability of multiple and more refined CT grading systems and classification is now making this task easier and thereby contributing to the recent improvements achieved in COVID-19 treatment and outcomes. In this article, evidence of chest imaging regarding diagnosis, management and monitoring of COVID-19 will be chronologically reviewed.

Quantitative Comparisons of Deep-learning-based and Atlas-based Auto-segmentation of the Intermediate Risk Clinical Target Volume for Nasopharyngeal Carcinoma

BACKGROUND

Manual segment target volumes were time-consuming and inter-observer variability couldn't be avoided. With the development of computer science, auto-segmentation had the potential to solve this problem.

OBJECTIVE

To evaluate the accuracy and stability of Atlas-based and deep-learning-based auto-segmentation of the intermediate risk clinical target volume, composed of CTV2 and CTVnd, for nasopharyngeal carcinoma quantitatively.

METHODS AND MATERIALS

A cascade-deep-residual neural network was constructed to automatically segment CTV2 and CTVnd by deep learning method. Meanwhile, a commercially available software was used to automatically segment the same regions by Atlas-based method. The datasets included contrast computed tomography scans from 102 patients. For each patient, the two regions were manually delineated by one experienced physician. The similarity between the two auto-segmentation methods was quantitatively evaluated by Dice similarity coefficient, the 95th Hausdorff distance, volume overlap error and relative volume difference, respectively. Statistical analyses were performed using the ranked Wilcoxon test.

RESULTS

The average Dice similarity coefficient (±standard deviation) given by the deep-learning-based and Atlas-based auto-segmentation were 0.84(±0.03) and 0.74(±0.04) for CTV2, 0.79(±0.02) and 0.68(±0.03) for CTVnd, respectively. For the 95th Hausdorff distance, the corresponding values were 6.30±3.55mm and 9.34±3.39mm for CTV2, 7.09±2.27mm and 14.33±3.98mm for CTVnd. Besides, volume overlap error and relative volume difference could also predict the same situations. Statistical analyses showed significant difference between the two auto-segmentation methods (p<0.01).

CONCLUSIONS

Compared with the Atlas-based segmentation approach, the deep-learning-based segmentation method performed better both in accuracy and stability for meaningful anatomical areas other than organs at risk.

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

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.

Chest CT Diagnosis of COVID-19: Accuracy using CO-RADS and CT-Involvement Scoring

Objectives:

Both Reporting and Data System (CO-RADS) and CT-involvement scores (CTIS) have been proposed for evaluation of COVID-19 on chest CT. The purpose of this single-center, retrospective study was to evaluate both scoring systems to diagnose COVID-19 infection in a high-prevalence area.

Materials and Methods:

Chest CT datasets (n = 200) and available reverse transcription polymerase chain reaction (RT-PCR) on nasopharyngeal swab were included. CT scans were assigned to four ‘imaging groups’ after scoring for both CO-RADS and CTIS. Diagnostic accuracy of chest CT was calculated respectively using RT-PCR and clinical diagnosis as gold standards: False-negatives and false-positives of chest CT regarding RT-PCR were studied in more depth using the medical files.

Results:

The ‘imaging group’ including CO-RADS 4/5 scores reached the highest diagnostic values for COVID-19 considering either the initial RT-PCR or the final clinical diagnosis as the standard of reference: accuracies of 172/200 (86%) to 181/200 (90.5%), sensitivities of 60/80 (88.2%) to 70/79 (88.6%), specificities of 112/132 (84.9%) to 111/121 (91.7%), negative predictive values (NPV) of 112/120 (93.3%) to 111/120 (92.5%), respectively. False-negative CTs regarding RT-PCR were mainly explained by imaging very early in the disease course (5 out of 8 cases) or COVID-19 infection with no/minor respiratory symptoms (3 out of 8 cases).

Conclusion:

Assessing chest CT using CO-RADS is a valuable diagnostic approach for COVID-19 infection in a high-prevalence area, with a higher accuracy than CTIS.

Tocilizumab improves survival in severe COVID-19 pneumonia with persistent hypoxia: a retrospective cohort study with follow-up from Mumbai, India

BACKGROUND

Cytokine storm triggered by Severe Coronavirus Disease 2019 (COVID-19) is associated with high mortality. With high Interleukin -6 (IL-6) levels reported in COVID-19 related deaths in China, IL-6 is considered to be the key player in COVID-19 cytokine storm. Tocilizumab, a monoclonal antibody against IL-6 receptor, is used on compassionate grounds for treatment of COVID-19 cytokine storm. The aim of this study was to assess effect of tocilizumab on mortality due to COVID-19 cytokine storm.

METHOD

This retrospective, observational study included patients of severe COVID-19 pneumonia with persistent hypoxia (defined as saturation 94% or less on supplemental Oxygen of 15 L per minute through non-rebreathing mask or PaO2/FiO2 ratio of less than 200) who were admitted to a tertiary care center in Mumbai, India, between 31st March to 5th July 2020. In addition to standard care, single Inj. Tocilizumab 400 mg was given intravenously to 151 consecutive COVID-19 patients with persistent hypoxia, from 13th May to 5th July 2020. These 151 patients were retrospectively analysed and compared with historic controls, ie consecutive COVID-19 patients with persistent hypoxia, defined as stated above (N = 118, from our first COVID-19 admission on 31st March to 12th May 2020 i.e., till tocilizumab was available in hospital). Univariate and multivariate Cox regression analysis was performed for identifying predictors of survival. Statistical analysis was performed using IBM SPSS version 26.

RESULTS

Out of 269 (151 in tocilizumab group and 118 historic controls) patients studied from 31st March to 5th July 2020, median survival in the tocilizumab group was significantly longer than in the control group; 18 days (95% CI, 11.3 to 24.7) versus 9 days (95% CI, 5.7 to 12.3); log rank p 0.007. On multivariate Cox regression analysis, independent predictors of survival were use of tocilizumab (HR 0.621, 95% CI 0.427-0.903, P 0.013) and higher oxygen saturation.

CONCLUSION

Tocilizumab may improve survival in severe COVID-19 pneumonia with persistent hypoxia. Randomised controlled trials on use of tocilizumab as rescue therapy in patients of severe COVID-19 pneumonia with hypoxia (PaO2/FiO2 less than 200) due to hyperinflammatory state, are warranted.

Diagnostic and surgical challenges in disseminated tuberculosis presenting as acute abdomen during COVID-19 pandemic

Due to the lack of much insight into the SARS-CoV-2 and overlapping symptoms and signs with other respiratory infections, diagnosis and management is often challenging in chest ailments particularly tuberculosis.

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.