Severe multivessel coronary vasospasm in a patient with coronavirus disease 2019.

We present the case of a 65-year-old male with multivessel coronary spasm presumably related to coronavirus disease 2019 (COVID-19). Acetylcholine coronary angiogram and cardiac magnetic resonance imaging were used for the diagnosis. As the precise pathophysiology of myocardial injury by COVID-19 remains unclear, the multimodality approach may contribute to the accurate diagnosis. Learning objective: Myocardial involvement by severe acute respiratory syndrome coronavirus 2 infection is related to various pathologies. It is important to evaluate the degrees of cardiac damage and make a diagnosis by multimodality imaging especially with cardiac magnetic resonance.

Comparison of CO-RADS Scores Based on Visual and Artificial Intelligence Assessments in a Non-Endemic Area.

In this study, we first developed an artificial intelligence (AI)-based algorithm for classifying chest computed tomography (CT) images using the coronavirus disease 2019 Reporting and Data System (CO-RADS). Subsequently, we evaluated its accuracy by comparing the calculated scores with those assigned by radiologists with varying levels of experience. This study included patients with suspected SARS-CoV-2 infection who underwent chest CT imaging between February and October 2020 in Japan, a non-endemic area. For each chest CT, the CO-RADS scores, determined by consensus among three experienced chest radiologists, were used as the gold standard. Images from 412 patients were used to train the model, whereas images from 83 patients were tested to obtain AI-based CO-RADS scores for each image. Six independent raters (one medical student, two residents, and three board-certified radiologists) evaluated the test images. Intraclass correlation coefficients (ICC) and weighted kappa values were calculated to determine the inter-rater agreement with the gold standard. The mean ICC and weighted kappa were 0.754 and 0.752 for the medical student and residents (taken together), 0.851 and 0.850 for the diagnostic radiologists, and 0.913 and 0.912 for AI, respectively. The CO-RADS scores calculated using our AI-based algorithm were comparable to those assigned by radiologists, indicating the accuracy and high reproducibility of our model. Our study findings would enable accurate reading, particularly in areas where radiologists are unavailable, and contribute to improvements in patient management and workflow.

Cardiac involvement in coronavirus disease 2019 assessed by cardiac magnetic resonance imaging: a meta-analysis.

In this systematic review and meta-analysis, we sought to evaluate the prevalence of cardiac involvement in patients with COVID-19 using cardiac magnetic resonance imaging. A literature review was performed to investigate the left ventricular (LV) and right ventricular (RV) ejection fraction (EF), the prevalence of LV late gadolinium enhancement (LGE), pericardial enhancement, abnormality on T1 mapping, and T2 mapping/T2-weighted imaging (T2WI), and myocarditis (defined by modified Lake Louis criteria). Pooled mean differences (MD) between COVID-19 patients and controls for LVEF and RVEF were estimated using random-effects models. We included data from 10.462 patients with COVID-19, comprising 1.010 non-athletes and 9.452 athletes from 29 eligible studies. The meta-analysis showed a significant difference between COVID-19 patients and controls in terms of LVEF [MD = - 2.84, 95% confidence interval (CI) - 5.11 to - 0.56, p < 0.001] and RVEF (MD = - 2.69%, 95% CI - 4.41 to - 1.27, p < 0.001). However, in athletes, no significant difference was identified in LVEF (MD = - 0.74%, 95% CI - 2.41 to - 0.93, p = 0.39) or RVEF (MD = - 1.88%, 95% CI - 5.21 to 1.46, p = 0.27). In non-athletes, the prevalence of LV LGE abnormalities, pericardial enhancement, T1 mapping, T2 mapping/T2WI, myocarditis were 27.5% (95%CI 17.4-37.6%), 11.9% (95%CI 4.1-19.6%), 39.5% (95%CI 16.2-62.8%), 38.1% (95%CI 19.0-57.1%) and 17.6% (95%CI 6.3-28.9%), respectively. In athletes, these values were 10.8% (95%CI 2.3-19.4%), 35.4% (95%CI - 3.2 to 73.9%), 5.7% (95%CI - 2.9 to 14.2%), 1.9% (95%CI 1.1-2.7%), 0.9% (0.3-1.6%), respectively. Both LVEF and RVEF were significantly impaired in COVID-19 patients compared to controls, but not in athletes. In addition, the prevalence of myocardial involvement is not negligible in patients with COVID-19.

Correction to: Ultra-high-resolution computed tomography can demonstrate alveolar collapse in novel coronavirus (COVID-19) pneumonia.

The correct caption is given below.

Imaging of COVID-19: An update of current evidences.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been reported as a global emergency. As respiratory dysfunction is a major clinical presentation of COVID-19, chest computed tomography (CT) plays a central role in the diagnosis and management of patients with COVID-19. Recent advances in imaging approaches using artificial intelligence have been essential as a quantification and diagnostic tool to differentiate COVID-19 from other respiratory infectious diseases. Furthermore, cardiovascular involvement in patients with COVID-19 is not negligible and may result in rapid worsening of the disease and sudden death. Cardiac magnetic resonance imaging can accurately depict myocardial involvement in SARS-CoV-2 infection. This review summarizes the role of the radiology department in the management and the diagnosis of COVID-19, with a special emphasis on ultra-high-resolution CT findings, cardiovascular complications and the potential of artificial intelligence.

Real-world evaluation of a computed tomography-first triage strategy for suspected Coronavirus disease 2019 in outpatients in Japan: An observational cohort study.

ABSTRACT: The Coronavirus disease 2019 pandemic continues to spread worldwide. Because of the absence of reliable rapid diagnostic systems, patients with symptoms of Coronavirus disease 2019 are treated as suspected of the disease. Use of computed tomography findings in Coronavirus disease 2019 are expected to be a reasonable method for triaging patients, and computed tomography-first triage strategies have been proposed. However, clinical evaluation of a computed tomography-first triage protocol is lacking.The aim of this study is to investigate the real-world efficacy and limitations of a computed tomography-first triage strategy in patients with suspected Coronavirus disease 2019.This was a single-center cohort study evaluating outpatients with fever who received medical examination at Yokohama City University Hospital, prospectively registered between 9 February and 5 May 2020. We treated according to the computed tomography-first triage protocol. The primary outcome was efficacy of the computed tomography-first triage protocol for patients with fever in an outpatient clinic. Efficacy of the computed tomography-first triage protocol for outpatients with fever was evaluated using sensitivity, specificity, positive predictive value, and negative predictive value. We conducted additional analyses of the isolation time of feverish outpatients and final diagnoses.In total, 108 consecutive outpatients with fever were examined at our hospital. Using the computed tomography-first triage protocol, 48 (44.9%) patients were classified as suspected Coronavirus disease 2019. Nine patients (18.8%) in this group were positive for severe acute respiratory syndrome coronavirus 2 using polymerase chain reaction; no patients in the group considered less likely to have Coronavirus disease 2019 tested positive for the virus. The protocol significantly shortened the duration of isolation for the not-suspected versus the suspected group (70.5 vs 1037.0 minutes, P < .001).Our computed tomography-first triage protocol was acceptable for screening patients with suspected Coronavirus disease 2019. This protocol will be helpful for appropriate triage, especially in areas where polymerase chain reaction is inadequate.

Pulmonary vascular enlargement and lesion extent on computed tomography are correlated with COVID-19 disease severity.

PURPOSE: To assess the relationships among pulmonary vascular enlargement, computed tomography (CT) findings quantified with software, and coronavirus disease (COVID-19) severity. MATERIALS AND METHODS: Ultra-high-resolution (UHR) CT images of 87 patients (50 males, 37 females; median age, 63 years) with COVID-19 confirmed using real-time polymerase chain reaction were analyzed. The maximum subsegmental vascular diameter was measured on CT. Total CT lung volume (CTLV total) and lesion extent (ratio of lesion volume to CTLV total) of ground-glass opacities, reticulation, and consolidation were measured using software. Maximum pulmonary vascular diameter and lesion extent were analyzed using Spearman's correlation analysis. Logistic regression analysis was performed on CT results to predict disease severity. We also assessed changes in these measures on follow-up scans in 16 patients. RESULTS: All 23 patients with severe and critical illness had vascular enlargement (> 4 mm). Pulmonary vascular enlargement (odds ratio 3.05, p = 0.018) and CT lesion extent (odds ratio 1.07, p = 0.002) were independent predictors of disease severity after adjustment for age and comorbidities. On follow-up CT, vascular diameter and CT lesion volume decreased (p = 0.001, p = 0.002; respectively), but CTLV total did not change significantly. CONCLUSION: Subsegmental vascular enlargement is a notable finding to predict acute COVID-19 disease severity.

Ultra-high-resolution computed tomography can demonstrate alveolar collapse in novel coronavirus (COVID-19) pneumonia.

PURPOSE: To review the chest computed tomography (CT) findings on the ultra-high-resolution CT (U-HRCT) in patients with the Novel coronavirus disease 2019 (COVID-19). MATERIALS AND METHODS: In February 2020, six consecutive patients with COVID-19 pneumonia (median age, 69 years) underwent U-HR CT imaging. U-HR-CT has a larger matrix size of 1024 × 1024 thinner slice thickness of 0.25 mm and can demonstrate terminal bronchioles in the normal lungs; as a result, Reid's secondary lobules and their abnormalities can be identified. The distribution and hallmarks (ground-glass opacity, consolidation with or without architectural distortion, linear opacity, crazy paving) of the lung opacities on U-HRCT were visually evaluated on a 1 K monitor by two experienced reviewers. The CT lung volume was measured, and the ratio of the measured lung volume to the predicted total lung capacity (predTLC) based on sex, age and height was calculated. RESULTS: All cases showed crazy paving pattern in U-HRCT. In these lesions, the secondary lobules were smaller than those in the un-affected lungs. CT lung volume decreased in two cases comparing predTLC. CONCLUSION: U-HRCT can evaluate not only the distribution and hallmarks of COVID-19 pneumonia but also visualize local lung volume loss.

COVID-19 pneumonia: infection control protocol inside computed tomography suites.

A novel coronavirus (severe acute respiratory syndrome coronavirus 2) causes a cluster of pneumonia cases in Wuhan, China. It spread rapidly and globally. CT imaging is helpful for the evaluation of the novel coronavirus disease 2019 (COVID-19) pneumonia. Infection control inside the CT suites is also important to prevent hospital-related transmission of COVID-19. We present our experience with infection control protocol for COVID-19 inside the CT suites.