COVID-19 Chest CT Research Articles

Chest CT surprising improvement in COVID-19 patient

A surprising improvement in COVID-19 chest CT follow up gives hope regarding future clinical evolution of COVID-19 patients.

Association between clinical, laboratory findings and chest CT in COVID-19 in a secondary hospital in Jakarta, Indonesia.

A new emerging infectious disease caused by SARS-CoV-2 has caused a global pandemic. Early diagnosis is essential to prevent and halt the spread of the disease, patient management and isolation. In this study, we aimed to reveal correlations between clinical and laboratory findings with chest CT. This in an observational case series single center study in a secondary hospital in Jakarta, Indonesia. Patients were included if they had typical symptoms and positive RT-PCR for SARS-CoV-2. Forty-two patients with positive RT-PCR were included in this study. Typical CT findings were present in 33 (78.6%). We found a positive correlation between patients in whom the imaging was performed after the 4th day of symptoms and chest CT findings (r=0.365 p<0.05). In receiver operating characteristic analysis of this parameter, the area under curve (AUC) was 0.678, and the sensitivity and specificity were 0.96 and 0.44, respectively. Early diagnosis of COVID-19 is essential to promptly isolate and treat suspected patients. Utilization of chest CT to help diagnosis in this pandemic era needs to be considered by healthcare facilities especially if RT-PCR is limited.

Association of AI quantified COVID-19 chest CT and patient outcome.

PurposeSeverity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-consuming task, while qualitative evaluation may be fast but highly subjective. This study aims to develop artificial intelligence (AI)-based methods to quantify disease severity and predict COVID-19 patient outcome.MethodsWe develop an AI-based framework that employs deep neural networks to efficiently segment lung lobes and pulmonary opacities. The volume ratio of pulmonary opacities inside each lung lobe gives the severity scores of the lobes, which are then used to predict ICU admission and mortality with three different machine learning methods. The developed methods were evaluated on datasets from two hospitals (site A: Firoozgar Hospital, Iran, 105 patients; site B: Massachusetts General Hospital, USA, 88 patients).ResultsAI-based severity scores are strongly associated with those evaluated by radiologists (Spearman’s rank correlation 0.837, p<0.001). Using AI-based scores produced significantly higher (p<0.05) area under the ROC curve (AUC) values. The developed AI method achieved the best performance of AUC = 0.813 (95% CI [0.729, 0.886]) in predicting ICU admission and AUC = 0.741 (95% CI [0.640, 0.837]) in mortality estimation on the two datasets.ConclusionsAccurate severity scores can be obtained using the developed AI methods over chest CT images. The computed severity scores achieved better performance than radiologists in predicting COVID-19 patient outcome by consistently quantifying image features. Such developed techniques of severity assessment may be extended to other lung diseases beyond the current pandemic.

Image Analysis of COVID-19 Chest CT Images Based on Machine Learning

Novel coronavirus pneumonia has found in late 2019, and it has spread around the world. COVID-19 pandemic has changed the way of our lives tremendously. WHO reported 170 million people infected, and the Ministry of Health,Labour and Welfare of Japan has reported 0.75 million people infections in Japan on June 7 th . Medical image diagnosis using X-ray photograph or X-ray CT are practical tools for COVID-19 infections since they enable us to take images of entire of the lung. Especially, chest CT images have significant advantages to observe the inside the lung on axial slices and are easy to diagnose ground-glass opacity regions. This paper shows image analysis techniques for COVID-19 cases by introducing our laboratory’s work on COVID-19 CT image analysis. Furthermore, we discuss image analysis, network image database and computation facilities for the COVID-19 pandemic. References (11)

Single, unilateral halo sign in COVID-19 pneumonia

•Radiologists should know the atypical CT findings of COVID-19 pneumonia as well as the typical ones•A single focal halo-sign lesion has never been described before as manifestation of COVID-19 pneumonia•CT halo-sign could represent a consequence of thrombotic damage of pulmonary microcirculation•In the setting of asymptomatic patients, unilateral single halo-sign should alert to the possibility of COVID-19 pneumonia We read with great interest the article by McLaren et al., recently published in Clinical Imaging.[1]McLaren T.A. Gruden J.F. Green D.B. The Bullseye sign: a variant of the reverse halo sign in COVID-19 pneumonia.Clin Imaging. 2020; 68: 191-196https://doi.org/10.1016/j.clinimag.2020.07.024Abstract Full Text Full Text PDF PubMed Scopus (15) Google Scholar We agree on the importance of describing atypical CT findings in COVID-19 to better and more comprehensively understand this newly emerged disease and on the effort of correlating pathologic alterations (as diffuse alveolar damage, organizing pneumonia, microthrombi and hemorrhagic infarcts) with CT findings. We think CT could be a useful tool to enlighten its pathophysiology through the study of the abnormalities that we can observe with High Resolution Chest CT (HRCT) at a very high anatomical detail. McLaren et al. described the bullseye sign, a variant of the reverse halo sign, as an atypical manifestation of COVID-19 pneumonia. Similarly, Poerio et al.[2]Poerio A. Sartoni M. Lazzari G. et al.Halo, reversed halo, or both? Atypical computed tomography manifestations of coronavirus disease (COVID-19) pneumonia: the “double halo sign”.Korean J Radiol. 2020; 21: 1161-1164https://doi.org/10.3348/kjr.2020.0687Crossref PubMed Scopus (7) Google Scholar described a combination of multiple and bilateral halo sign, reverse halo sign and double halo sign as main CT findings in a 55 year-old woman with COVID-19. Both authors explained these atypical findings as manifestations of organizing pneumonia (reverse halo sign) and extensive thrombotic damage of pulmonary microcirculation (halo sign and double halo sign). At our center, a reference hospital for COVID-19 in Northern Italy, we collected a large series of chest X-ray and CT in symptomatic patients with suspicion and/or confirmation of SARS-CoV-2 infection. Among these, we believe it could be useful to describe the case of a 42 year-old male nurse presented with a 2 days history of arthromyalgia and an isolated episode of fever up to 38 °C (100.4 °F). At admission, laboratory studies showed leucopenia and elevated C-reactive protein (15.2 mg/L); a positive rapid antigen test confirmed SARS-CoV-2 infection. Chest x-ray showed no alterations, whereas a chest CT showed, as single finding, a rounded consolidation surrounded by ground-glass opacity (halo sign) in the left lower lobe (Fig. 1). The patient was then discharged to quarantine at home. Asymptomatic or paucisymptomatic COVID-19 patients might have normal chest CT[3]Kwee T.C. Kwee R.M. Chest CT in COVID-19: what the radiologist needs to know.RadioGraphics. 2020; 40: 1848-1865https://doi.org/10.1148/rg.2020200159Crossref PubMed Scopus (160) Google Scholar; in case of lung abnormalities, CT findings are frequently unilateral (58.6%).[4]Heng M. Xiong R. He R. et al.CT imaging and clinical course of asymptomatic cases with COVID-19 pneumonia at admission in Wuhan, China.J Infect. 2020; 81: e33-e39https://doi.org/10.1016/j.jinf.2020.04.004Abstract Full Text Full Text PDF Scopus (169) Google Scholar Moreover, Shi et al. reported that the predominant pattern in early stages is characterized by unilateral, although multifocal GGO.5.Shi H. Han X. Jiang N. et al.Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: a descriptive study.Lancet Infect Dis. 2020; 20: 425e34https://doi.org/10.1016/S1473-3099(20)30086-4Abstract Full Text Full Text PDF Scopus (2238) Google Scholar To the best of our knowledge, a single focal halo-sign lesion has never been described before as manifestation of COVID-19 pneumonia. Halo sign has rather been observed, in the context of more typical CT findings, as multiple and bilateral lesions, sometimes associated with reverse CT halo sign.[6]Shaghaghi S. Daskareh M. Irannejad M. et al.Target-shaped combined halo and reversed-halo sign, an atypical chest CT finding in COVID-19.Clin Imaging. 2020; 69: 72-74https://doi.org/10.1016/j.clinimag.2020.06.038Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar Halo-sign is non-specific and histopathologically usually represents alveolar edema and hemorrhage.[7]Pinto P.S. The CT halo sign.Radiology. 2004; 230: 109-110Crossref PubMed Scopus (72) Google Scholar Although different from the bullseye sign, the lesion we presented showed connection to the vascular system too, as suggested from a pulmonary vein branch reaching it. Thus, it is likely that in SARS-Cov-2 infection halo sign represent a consequence of thrombotic damage of pulmonary microcirculation. In conclusion, we believe that radiologists should know the atypical CT findings of COVID-19 pneumonia as well as the typical ones. In the setting of asymptomatic patients and in case of short time between symptom onset and hospital admission, the presence of unilateral single halo-sign should alert to the possibility of COVID-19 pneumonia. No conflict of interest or industry support for the two authors.

CT findings of 795 COVID-19 positive cases: a multicenter study in Egypt

BackgroundCorona Virus Disease 2019 (COVID-19) outbreak was officially announced as a global pandemic by the WHO on March 11th 2020. Thorough understanding of CT imaging features of COVID-19 is essential for effective patient management; rationalizing the need for relevant research. The aim of this study was to analyze the chest CT findings of patients with real-time polymerase chain reaction (RT-PCR) proved COVID-19 admitted to four Egyptian hospitals. The recently published RSNA expert consensus statement on reporting COVID-19 chest CT findings was taken into consideration.ResultsNormal CT “negative for COVID-19” was reported in 26.1% of our RT-PCR proved COVID-19 cases. In descending order of prevalence, imaging findings of the positive CT studies (73.9%) included GGO (69%), consolidation (49.7%), crazy paving (15.4%), and peri-lobular fibrosis (40.6%). These showed a dominantly bilateral (68.2%), peripheral (72.4%), and patchy (64.7%) distribution. Remarkably, thymic hyperplasia was identified in 14.3% of studies. According to the RSNA consensus, CT findings were classified as typical in 68.9%, indeterminate in 3.6%, and atypical in 1.4% of the evaluated CT studies.ConclusionAlthough COVID-19 cannot be entirely excluded by chest CT, it can be distinguished in more than two-thirds of cases; making CT a widely available, non-invasive, and rapid diagnostic tool.

Temporal variations in the diagnostic performance of chest CT for Covid-19 depending on disease prevalence: Experience from North-Eastern France

Rationale and ObjectiveThe purpose of this work was to analyze temporal variations in the diagnostic performance of chest CT for Covid-19 throughout the first wave, depending on disease prevalence variations between the ascending, peak and descending phases of the epidemic in North-Eastern France. Materials and MethodsFrom March 6th to April 22nd 2020, all consecutive adult patients referred to the “Covid-19 clinic” of our Emergency Department with the availability of chest CT and of at least one RT-PCR result were retrospectively included in the present study. Chest CT was considered positive when typical Covid-19 lesions were observed (bilateral and predominantly peripheral and sub-pleural ground glass opacities and/or alveolar consolidations). RT-PCR results were considered as the reference standard. Ascending, peak and descending phases were determined based on the number of CT scans performed daily. CT diagnostic performance were calculated and variations between phases were tested for equivalence or difference using Bayesian methods. Results2194 consecutive chest CT were analyzed. Overall CT diagnostic performance was Se = 84.2 [82.0 ; 86.3], Sp = 86.6 [84.5 ; 88.5], PPV = 86.1 [84.0 ; 88.1], NPV = 84.7 [82.6 ; 86.7] and accuracy = 85.4 [83.9 ; 86.8], with no significant differences between chest and non-chest radiologists. Variations between the ascending (11 days, 281 chest CT, disease prevalence 37.0 %), the peak (18 days, 1167 chest CT, disease prevalence 64 %) and the descending phases (19 days, 746 chest CT, disease prevalence 32.2 %) were highest for PPV and NPV with a probability of difference >99.9 %, and smallest for accuracy and specificity with a probability of equivalence >98.8 %. ConclusionIn a homogenous cohort of 2194 consecutive chest CT performed over a 7-week epidemic wave, we observed significant variations of CT predictive values whereas CT specificity appeared marginally affected.

CT and clinical assessment in asymptomatic and pre-symptomatic patients with early SARS-CoV-2 in outbreak settings.

ObjectivesThe early infection dynamics of patients with SARS-CoV-2 are not well understood. We aimed to investigate and characterize associations between clinical, laboratory, and imaging features of asymptomatic and pre-symptomatic patients with SARS-CoV-2.MethodsSeventy-four patients with RT-PCR-proven SARS-CoV-2 infection were asymptomatic at presentation. All were retrospectively identified from 825 patients with chest CT scans and positive RT-PCR following exposure or travel risks in outbreak settings in Japan and China. CTs were obtained for every patient within a day of admission and were reviewed for infiltrate subtypes and percent with assistance from a deep learning tool. Correlations of clinical, laboratory, and imaging features were analyzed and comparisons were performed using univariate and multivariate logistic regression.ResultsForty-eight of 74 (65%) initially asymptomatic patients had CT infiltrates that pre-dated symptom onset by 3.8 days. The most common CT infiltrates were ground glass opacities (45/48; 94%) and consolidation (22/48; 46%). Patient body temperature (p < 0.01), CRP (p < 0.01), and KL-6 (p = 0.02) were associated with the presence of CT infiltrates. Infiltrate volume (p = 0.01), percent lung involvement (p = 0.01), and consolidation (p = 0.043) were associated with subsequent development of symptoms.ConclusionsCOVID-19 CT infiltrates pre-dated symptoms in two-thirds of patients. Body temperature elevation and laboratory evaluations may identify asymptomatic patients with SARS-CoV-2 CT infiltrates at presentation, and the characteristics of CT infiltrates could help identify asymptomatic SARS-CoV-2 patients who subsequently develop symptoms. The role of chest CT in COVID-19 may be illuminated by a better understanding of CT infiltrates in patients with early disease or SARS-CoV-2 exposure.Key Points• Forty-eight of 74 (65%) pre-selected asymptomatic patients with SARS-CoV-2 had abnormal chest CT findings.• CT infiltrates pre-dated symptom onset by 3.8 days (range 1–5).• KL-6, CRP, and elevated body temperature identified patients with CT infiltrates. Higher infiltrate volume, percent lung involvement, and pulmonary consolidation identified patients who developed symptoms.

A head-to-head comparison of the intra- and interobserver agreement of COVID-RADS and CO-RADS grading systems in a population with high estimated prevalence of COVID-19.

Objective:To evaluate the inter- and intraobserver agreement of COVID-RADS and CO-RADS reporting systems among differently experienced radiologists in a population with high estimated prevalence of COVID-19.Methods and materials:Chest CT scans of patients with clinically–epidemiologically diagnosed COVID-19 were retrieved from an open-source MosMedData data set, randomised, and independently assigned COVID-RADS and CO-RADS grades by an abdominal radiology fellow, thoracic imaging fellow and a consultant cardiothoracic radiologist. The inter- and intraobserver agreement of the two systems were assessed using the Fleiss’ and Cohen’s κ coefficients, respectively.Results:A total of 200 studies were included in the analysis. Both systems demonstrated moderate interobserver agreement, with κ values of 0.51 [95% confidence interval (CI): 0.46–0.56] and 0.55 (95% CI: 0.50–0.59) for COVID-RADS and CO-RADS, respectively. When COVID-RADS and CO-RADS grades were dichotomised at cut-off values of 2B and 4 to evaluate the agreement between grades representing different levels of clinical suspicion for COVID-19, the interobserver agreement became substantial with κ values of 0.74 (95% CI: 0.66–0.82) for COVID-RADS and 0.73 (95% CI: 0.65–0.81) for CO-RADS. The median intraobserver agreement was considerably higher for CO-RADS reaching 0.81 (95% CI: 0.43–0.76) compared with 0.60 (95% CI: 0.43–0.76) of COVID-RADS.Conclusions:COVID-RADS and CO-RADS showed comparable interobserver agreement, which was moderate when grades were compared head-to-head and substantial when grades were dichotomised to better reflect the underlying levels of suspicion for COVID-19. The median intraobserver agreement of CO-RADS was, however, considerably higher compared with COVID-RADS.Advances in knowledge:This paper provides a comprehensive review of the newly introduced COVID-19 chest CT reporting systems, which will help radiologists of all sub-specialties and experience levels make an informed decision on which system to use in their own practice.

Prognostic Value and Reproducibility of AI-assisted Analysis of Lung Involvement in COVID-19 on Low-Dose Submillisievert Chest CT: Sample Size Implications for Clinical Trials.

PurposeTo compare the prognostic value and reproducibility of visual versus AI-assisted analysis of lung involvement on submillisievert low-dose chest CT in COVID-19 patients.Materials and MethodsThis was a HIPAA-compliant, institutional review board-approved retrospective study. From March 15 to June 1, 2020, 250 RT-PCR confirmed COVID-19 patients were studied with low-dose chest CT at admission. Visual and AI-assisted analysis of lung involvement was performed by using a semi-quantitative CT score and a quantitative percentage of lung involvement. Adverse outcome was defined as intensive care unit (ICU) admission or death. Cox regression analysis, Kaplan-Meier curves, and cross-validated receiver operating characteristic curve with area under the curve (AUROC) analysis was performed to compare model performance. Intraclass correlation coefficients (ICCs) and Bland- Altman analysis was used to assess intra- and interreader reproducibility.ResultsAdverse outcome occurred in 39 patients (11 deaths, 28 ICU admissions). AUC values from AI-assisted analysis were significantly higher than those from visual analysis for both semi-quantitative CT scores and percentages of lung involvement (all P<0.001). Intrareader and interreader agreement rates were significantly higher for AI-assisted analysis than visual analysis (all ICC ≥0.960 versus ≥0.885). AI-assisted variability for quantitative percentage of lung involvement was 17.2% (coefficient of variation) versus 34.7% for visual analysis. The sample size to detect a 5% change in lung involvement with 90% power and an α error of 0.05 was 250 patients with AI-assisted analysis and 1014 patients with visual analysis.ConclusionAI-assisted analysis of lung involvement on submillisievert low-dose chest CT outperformed conventional visual analysis in predicting outcome in COVID-19 patients while reducing CT variability. Lung involvement on chest CT could be used as a reliable metric in future clinical trials.

Chest CT for rapid triage of patients in multiple emergency departments during COVID-19 epidemic: experience report from a large French university hospital.

ObjectivesTo assess the diagnostic performances of chest CT for triage of patients in multiple emergency departments during COVID-19 epidemic, in comparison with reverse transcription polymerase chain reaction (RT-PCR) test.MethodFrom March 3 to April 4, 2020, 694 consecutive patients from three emergency departments of a large university hospital, for which a hospitalization was planned whatever the reasons, i.e., COVID- or non-COVID-related, underwent a chest CT and one or several RT-PCR tests. Chest CTs were rated as “Surely COVID+,” “Possible COVID+,” or “COVID−” by experienced radiologists. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated using the final RT-PCR test as standard of reference. The delays for CT reports and RT-PCR results were recorded and compared.ResultsAmong the 694 patients, 287 were positive on the final RT-PCR exam. Concerning the 694 chest CT, 308 were rated as “Surely COVID+”, 34 as “Possible COVID+,” and 352 as “COVID−.” When considering only the “Surely COVID+” CT as positive, accuracy, sensitivity, specificity, PPV, and NPV reached 88.9%, 90.2%, 88%, 84.1%, and 92.7%, respectively, with respect to final RT-PCR test. The mean delay for CT reports was three times shorter than for RT-PCR results (187 ± 148 min versus 573 ± 327 min, p < 0.0001).ConclusionDuring COVID-19 epidemic phase, chest CT is a rapid and most probably an adequately reliable tool to refer patients requiring hospitalization to the COVID+ or COVID− hospital units, when response times for virological tests are too long.Key Points• In a large university hospital in Lyon, France, the accuracy, sensitivity, specificity, PPV, and NPV of chest CT for COVID-19 reached 88.9%, 90.2%, 88%, 84.1%, and 92.7%, respectively, using RT-PCR as standard of reference.• The mean delay for CT reports was three times shorter than for RT-PCR results (187 ± 148 min versus 573 ± 327 min, p < 0.0001).• Due to high accuracy of chest CT for COVID-19 and shorter time for CT reports than RT-PCR results, chest CT can be used to orient patients suspected to be positive towards the COVID+ unit to decrease congestion in the emergency departments.

Structured reporting of chest CT in COVID-19 pneumonia: a consensus proposal

ObjectivesThe need of a standardized reporting scheme and language, in imaging of COVID-19 pneumonia, has been welcomed by major scientific societies. The aim of the study was to build the reporting scheme of chest CT in COVID-19 pneumonia.MethodsA team of experts, of the Italian Society of Medical and Interventional Radiology (SIRM), has been recruited to compose a consensus panel. They used a modified Delphi process to build a reporting scheme and expressed a level of agreement for each section of the report. To measure the internal consistency of the panelist ratings for each section of the report, a quality analysis based on the average inter-item correlation was performed with Cronbach’s alpha (Cα) correlation coefficient.ResultsThe overall mean score of the experts and the sum of score were 3.1 (std.dev. ± 0.11) and 122 in the second round, and improved to 3.75 (std.dev. ± 0.40) and 154 in the third round. The Cronbach’s alpha (Cα) correlation coefficient was 0.741 (acceptable) in the second round and improved to 0.789 in the third round. The final report was built in the management of radiology report template (MRRT) and includes n = 4 items in the procedure information, n = 5 items in the clinical information, n = 16 in the findings, and n = 3 in the impression, with overall 28 items.ConclusionsThe proposed structured report could be of help both for expert radiologists and for the less experienced who are faced with the management of these patients. The structured report is conceived as a guideline, to recommend the key items/findings of chest CT in COVID-19 pneumonia.

Chest CT for triage during COVID-19 on the emergency department: myth or truth?

PurposeWe aimed to investigate the diagnostic performance of chest CT compared with first RT-PCR results in adult patients suspected of COVID-19 infection in an ED setting. We also constructed a predictive machine learning model based on chest CT and additional data to improve the diagnostic accuracy of chest CT.MethodsThis study’s cohort consisted of 319 patients who underwent chest CT and RT-PCR testing at the ED. Patient characteristics, demographics, symptoms, vital signs, laboratory tests, and chest CT results (CO-RADS) were collected. With first RT-PCR as reference standard, the diagnostic performance of chest CT using the CO-RADS score was assessed. Additionally, a predictive machine learning model was constructed using logistic regression.ResultsChest CT, with first RT-PCR as a reference, had a sensitivity, specificity, PPV, and NPV of 90.2%, 88.2%, 84.5%, and 92.7%, respectively. The prediction model with CO-RADS, ferritin, leucocyte count, CK, days of complaints, and diarrhea as predictors had a sensitivity, specificity, PPV, and NPV of 89.3%, 93.4%, 90.8%, and 92.3%, respectively.ConclusionChest CT, using the CO-RADS scoring system, is a sensitive and specific method that can aid in the diagnosis of COVID-19, especially if RT-PCR tests are scarce during an outbreak. Combining a predictive machine learning model could further improve the accuracy of diagnostic chest CT for COVID-19. Further candidate predictors should be analyzed to improve our model. However, RT-PCR should remain the primary standard of testing as up to 9% of RT-PCR positive patients are not diagnosed by chest CT or our machine learning model.Electronic supplementary materialThe online version of this article (10.1007/s10140-020-01821-1) contains supplementary material, which is available to authorized users.

Typical and Atypical Manifestations and Characteristics of the covid-19 Chest CT Scanner : Retrospective study of 28 patients

Typical and Atypical Manifestations and Characteristics of the covid-19 Chest CT Scanner : Retrospective study of 28 patients

Classification of the COVID-19 infected patients using DenseNet201 based deep transfer learning

Deep learning models are widely used in the automatic analysis of radiological images. These techniques can train the weights of networks on large datasets as well as fine tuning the weights of pre-trained networks on small datasets. Due to the small COVID-19 dataset available, the pre-trained neural networks can be used for diagnosis of coronavirus. However, these techniques applied on chest CT image is very limited till now. Hence, the main aim of this paper to use the pre-trained deep learning architectures as an automated tool to detection and diagnosis of COVID-19 in chest CT. A DenseNet201 based deep transfer learning (DTL) is proposed to classify the patients as COVID infected or not i.e. COVID-19 (+) or COVID (−). The proposed model is utilized to extract features by using its own learned weights on the ImageNet dataset along with a convolutional neural structure. Extensive experiments are performed to evaluate the performance of the propose DTL model on COVID-19 chest CT scan images. Comparative analyses reveal that the proposed DTL based COVID-19 classification model outperforms the competitive approaches. Communicated by Ramaswamy H. Sarma

Systematic Review and Meta-Analysis on the Value of Chest CT in the Diagnosis of Coronavirus Disease (COVID-19): Sol Scientiae, Illustra Nos.

OBJECTIVE. The purpose of this article is to systematically review and meta-analyze the diagnostic accuracy of chest CT in detecting coronavirus disease (COVID-19). MATERIALS AND METHODS. MEDLINE was systematically searched for publications on the diagnostic performance of chest CT in detecting COVID-19. Methodologic quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Meta-analysis was performed using a bivariate random-effects model. RESULTS. Six studies were included, comprising 1431 patients. All six studies included patients at high risk of COVID-19, and five studies explicitly reported that they included only symptomatic patients. Mean prevalence of COVID-19 was 47.9% (range, 27.6-85.4%). High or potential risk of bias was present throughout all QUADAS-2 domains in all six studies. Sensitivity ranged from 92.9% to 97.0%, and specificity ranged from 25.0% to 71.9%, with pooled estimates of 94.6% (95% CI, 91.9-96.4%) and 46.0% (95% CI, 31.9-60.7%), respectively. The included studies were statistically homogeneous in their estimates of sensitivity (p = 0.578) and statistically heterogeneous in their estimates of specificity (p < 0.001). CONCLUSION. Diagnostic accuracy studies on chest CT in COVID-19 suffer from methodologic quality issues. Chest CT appears to have a relatively high sensitivity in symptomatic patients at high risk of COVID-19, but it cannot exclude COVID-19. Specificity is poor. These data, along with other local factors such as COVID-19 prevalence, available real-time reverse transcriptase-polymerase chain reaction tests, staff, hospital, and CT scanning capacity, can be useful to healthcare professionals and policy makers to decide on the utility of chest CT for COVID-19 detection in the hospital setting.

Diagnostic Power of Chest CT for COVID-19: To Screen or Not to Screen

Background: Chest CT is increasingly used for COVID-19 screening in healthcare systems with limited SARS-CoV-2 PCR capacity. Its diagnostic value was supported by studies with methodological concerns and its use is controversial. Here we investigated its potential to diagnose COVID-19 in symptomatic patients and to screen asymptomatic patients in a prospective study with minimal selection bias. Methods: From March 19, 2020 to April 20, 2020 we performed parallel SARS-CoV-2 PCR and CT with categorization of COVID-19 suspicion by CO-RADS, in 859 patients with COVID-19 symptoms and 1138 controls admitted to the hospital for COVID-19 unrelated medical urgencies. CT-CORADS was categorized on a 5-point scale from 1 (very low suspicion) to 5 (very high suspicion). AUC under ROC curve were calculated in symptomatic versus asymptomatic patients to predict positive SARS-CoV-2 positive PCR and likelihood ratios for each CO-RADS score were used for rational selection of diagnostic thresholds. Findings: CT-CORADS had significant (PInterpretation: CT-CORADS has meaningful diagnostic power in symptomatic patients, supporting its application for time-sensitive triage. Sensitivity in asymptomatic patients is insufficient to justify its use as screening approach. Incidental detection of CO-RADS ≥ 3 in asymptomatic patients should trigger reflex testing for respiratory pathogens.Funding Statement: This work was supported by a donation from board members of Fagron (Nazareth, Belgium), a healthcare company, to RADar, the teaching and education initiative of AZ Delta General Hospital, to be used as unconditional research grant for data collection, collaborative collaboration and open access publication.Declaration of Interests: The authors declare no conflict of interest.Ethics Approval Statement: The study was approved by the AZ Delta Institutional Review Board with a waiver of informed consent from study participants considering the study is based on secondary analysis of existing data (Clinical Trial Number: B1172020000008).

Clinical Features and Temporal Changes of RT-PCR and Chest CT in COVID-19 Pediatric Patients: A Diagnostic Strategy Study

Clinical Features and Temporal Changes of RT-PCR and Chest CT in COVID-19 Pediatric Patients: A Diagnostic Strategy Study