Diagnostic Performance of CO-RADS and the RSNA Classification System                     in Evaluating COVID-19 at Chest CT: A Meta-Analysis

Abstract Early diagnosis and isolation of cases are particularly crucial for coronavirus disease 2019 (COVID-19) in global pandemic. The aim of this study is to determine the diagnostic performance of chest computed tomography (CT) and imaging features for diagnosing COVID-19. Diagnostic accuracy studies of CT and RT-PCR in patients with clinically suspected COVID-19, which were published up to April 25th, 2020 from MEDLINE, EMBASE, and the Cochrane Library. Twelve studies (n=2,204) were included. The pooled sensitivity, specificity, likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) of chest CT for detecting COVID-19 were 94.5% (95% confidence interval (CI) 89.5 to 97.2%) and 41.8% (95% CI 24.2 to 61.6%), 1.6 (95% CI: 1.6-2.3), 0.13 (95% CI: 0.06-0.31), and 12.4 (95% CI: 4.0-38.5), respectively. Initial RT-PCR revealed a better diagnostic performance. Peripheral lesions, bilateral involvement, multiple lesions, and ground-glass opacities (GGO), revealed to be with better diagnostic value than other CT manifestations. Using chest CT for COVID-19 diagnosis has a high sensitivity and a relatively low specificity. Bilateral multiple peripheral lesions and GGO revealed to be with better diagnostic value. For areas with high prevalence, chest CT could be a good screening test to preliminary screen patients with COVID-19 quickly.

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The diagnostic performance of chest computed tomography in the detection of rib fractures in children investigated for suspected physical abuse: a systematic review and meta-analysis

European Radiology ◽

10.1007/s00330-021-07775-3 ◽

2021 ◽

Author(s):

Nasser M Alzahrani ◽

Annmarie Jeanes ◽

Michael Paddock ◽

Farag Shuweihdi ◽

Amaka C. Offiah

Keyword(s):

Physical Abuse ◽

Diagnostic Performance ◽

Meta Analysis ◽

High Specificity ◽

Chest Ct ◽

Rib Fractures ◽

Chest Radiographs ◽

Skeletal Survey ◽

Fractures In Children ◽

Low Sensitivity

Abstract Objectives To assess the diagnostic performance of chest CT in the detection of rib fractures in children investigated for suspected physical abuse (SPA). Methods Medline, Web of Science and Cochrane databases were searched from January 1980 to April 2020. The QUADAS-2 tool was used to assess the quality of the eligible English-only studies following which a formal narrative synthesis was constructed. Studies reporting true-positive, false-positive, true-negative, and false-negative results were included in the meta-analysis. Overall sensitivity and specificity of chest CT for rib fracture detection were calculated, irrespective of fracture location, and were pooled using a univariate random-effects meta-analysis. The diagnostic accuracy of specific locations along the rib arc (anterior, lateral or posterior) was assessed separately. Results Of 242 identified studies, 4 met the inclusion criteria. Of these, 2 were included in the meta-analysis. Chest CT identified 142 rib fractures compared to 79 detected by initial skeletal survey chest radiographs in live children with SPA. Post-mortem CT (PMCT) has low sensitivity (34%) but high specificity (99%) in the detection of rib fractures when compared to the autopsy reference standard. PMCT has low sensitivity (45%, 21% and 42%) but high specificity (99%, 97% and 99%) at anterior, lateral and posterior rib locations, respectively. Conclusions Chest CT detects more rib fractures than initial skeletal survey chest radiographs in live children with SPA. PMCT has low sensitivity but high specificity for detecting rib fractures in children investigated for SPA. Key Points • PMCT has low sensitivity (34%) but high specificity (99%) in the detection of rib fractures; extrapolation to CT in live children is difficult. • No studies have compared chest CT with the current accepted practice of initial and follow-up skeletal survey chest radiographs in the detection of rib fractures in live children investigated for SPA.

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Faculty Opinions recommendation of Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.3594987.3438054 ◽

2010 ◽

Author(s):

Sandra Davidge ◽

Stephane Bourque

Keyword(s):

Systematic Review ◽

Body Mass Index ◽

Body Mass ◽

Diagnostic Performance ◽

Meta Analysis ◽

Body Adiposity

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COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

10.2196/preprints.19407 ◽

2020 ◽

Author(s):

Jinseok Lee

Keyword(s):

Deep Learning ◽

Diagnostic Performance ◽

Ct Images ◽

Chest Ct ◽

University Hospital ◽

Detection Accuracy ◽

Ct Image ◽

Test Dataset ◽

Learning Framework ◽

Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

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Diagnostic performance of different sampling approaches for SARS-CoV-2 RT-PCR testing: a systematic review and meta-analysis

The Lancet Infectious Diseases ◽

10.1016/s1473-3099(21)00146-8 ◽

2021 ◽

Author(s):

Nicole Ngai Yung Tsang ◽

Hau Chi So ◽

Ka Yan Ng ◽

Benjamin J Cowling ◽

Gabriel M Leung ◽

...

Keyword(s):

Systematic Review ◽

Diagnostic Performance ◽

Meta Analysis ◽

Rt Pcr

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A meta-analysis of accuracy and sensitivity of chest CT and RT-PCR in COVID-19 diagnosis

Scientific Reports ◽

10.1038/s41598-020-80061-2 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Fatemeh Khatami ◽

Mohammad Saatchi ◽

Seyed Saeed Tamehri Zadeh ◽

Zahra Sadat Aghamir ◽

Alireza Namazi Shabestari ◽

...

Keyword(s):

Polymerase Chain Reaction ◽

Reverse Transcriptase ◽

Ct Scan ◽

Predictive Value ◽

Meta Analysis ◽

Chest Ct ◽

Rt Pcr ◽

Chain Reaction ◽

Polymerase Chain ◽

Chest Ct Scan

AbstractNowadays there is an ongoing acute respiratory outbreak caused by the novel highly contagious coronavirus (COVID-19). The diagnostic protocol is based on quantitative reverse-transcription polymerase chain reaction (RT-PCR) and chests CT scan, with uncertain accuracy. This meta-analysis study determines the diagnostic value of an initial chest CT scan in patients with COVID-19 infection in comparison with RT-PCR. Three main databases; PubMed (MEDLINE), Scopus, and EMBASE were systematically searched for all published literature from January 1st, 2019, to the 21st May 2020 with the keywords "COVID19 virus", "2019 novel coronavirus", "Wuhan coronavirus", "2019-nCoV", "X-Ray Computed Tomography", "Polymerase Chain Reaction", "Reverse Transcriptase PCR", and "PCR Reverse Transcriptase". All relevant case-series, cross-sectional, and cohort studies were selected. Data extraction and analysis were performed using STATA v.14.0SE (College Station, TX, USA) and RevMan 5. Among 1022 articles, 60 studies were eligible for totalizing 5744 patients. The overall sensitivity, specificity, positive predictive value, and negative predictive value of chest CT scan compared to RT-PCR were 87% (95% CI 85–90%), 46% (95% CI 29–63%), 69% (95% CI 56–72%), and 89% (95% CI 82–96%), respectively. It is important to rely on the repeated RT-PCR three times to give 99% accuracy, especially in negative samples. Regarding the overall diagnostic sensitivity of 87% for chest CT, the RT-PCR testing is essential and should be repeated to escape misdiagnosis.

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Diagnostic performance of chest CT in differentiating COVID-19 from other causes of ground-glass opacities

Egyptian Journal of Radiology and Nuclear Medicine ◽

10.1186/s43055-020-00398-6 ◽

2021 ◽

Vol 52 (1) ◽

Author(s):

Ali H. Elmokadem ◽

Dalia Bayoumi ◽

Sherif A. Abo-Hedibah ◽

Ahmed El-Morsy

Keyword(s):

Diagnostic Performance ◽

Pulmonary Nodules ◽

Ground Glass Opacity ◽

Chest Ct ◽

Ground Glass ◽

Rt Pcr ◽

Laboratory Findings ◽

Halo Sign ◽

Traction Bronchiectasis ◽

Chest Ct Scan

Abstract Background To evaluate the diagnostic performance of chest CT in differentiating coronavirus disease 2019 (COVID-19) and non-COVID-19 causes of ground-glass opacities (GGO). Results A total of 80 patients (49 males and 31 females, 46.48 ± 16.09 years) confirmed with COVID-19 by RT-PCR and who underwent chest CT scan within 2 weeks of symptoms, and 100 patients (55 males and 45 females, 48.94 ± 18.97 years) presented with GGO on chest CT were enrolled in the study. Three radiologists reviewed all CT chest exams after removal of all identifying data from the images. They expressed the result as positive or negative for COVID-19 and recorded the other pulmonary CT features with mention of laterality, lobar affection, and distribution pattern. The clinical data and laboratory findings were recorded. Chest CT offered diagnostic accuracy ranging from 59 to 77.2% in differentiating COVID-19- from non-COVID-19-associated GGO with sensitivity from 76.25 to 90% and specificity from 45 to 67%. The specificity was lower when differentiating COVID-19 from non-COVID-19 viral pneumonias (30.5–61.1%) and higher (53.1–70.3%) after exclusion of viral pneumonia from the non-COVID-19 group. Patients with COVID-19 were more likely to have lesions in lower lobes (p = 0.005), peripheral distribution (p < 0.001), isolated ground-glass opacity (p = 0.043), subpleural bands (p = 0.048), reverse halo sign (p = 0.005), and vascular thickening (p = 0.013) but less likely to have pulmonary nodules (p < 0.001), traction bronchiectasis (p = 0.005), pleural effusion (p < 0.001), and lymphadenopathy (p < 0.001). Conclusions Chest CT offered reasonable sensitivity when differentiating COVID-19- from non-COVID-19-associated GGO with low specificity when differentiating COVID-19 from other viral pneumonias and moderate specificity when differentiating COVID-19 from other causes of GGO.

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