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Nam BD, Hong H, Yoon SH. Diagnostic performance of standardized typical CT findings for COVID-19: a systematic review and meta-analysis. Insights Imaging 2023; 14:96. [PMID: 37222857 DOI: 10.1186/s13244-023-01429-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Accepted: 04/14/2023] [Indexed: 05/25/2023] Open
Abstract
OBJECTIVE To meta-analyze diagnostic performance measures of standardized typical CT findings for COVID-19 and examine these measures by region and national income. METHODS MEDLINE and Embase were searched from January 2020 to April 2022 for diagnostic studies using the Radiological Society of North America (RSNA) classification or the COVID-19 Reporting and Data System (CO-RADS) for COVID-19. Patient and study characteristics were extracted. We pooled the diagnostic performance of typical CT findings in the RSNA and CO-RADS systems and interobserver agreement. Meta-regression was performed to examine the effect of potential explanatory factors on the diagnostic performance of the typical CT findings. RESULTS We included 42 diagnostic performance studies with 6777 PCR-positive and 9955 PCR-negative patients from 18 developing and 24 developed countries covering the Americas, Europe, Asia, and Africa. The pooled sensitivity was 70% (95% confidence interval [CI]: 65%, 74%; I2 = 92%), and the pooled specificity was 90% (95% CI 86%, 93%; I2 = 94%) for the typical CT findings of COVID-19. The sensitivity and specificity of the typical CT findings did not differ significantly by national income and the region of the study (p > 0.1, respectively). The pooled interobserver agreement from 19 studies was 0.72 (95% CI 0.63, 0.81; I2 = 99%) for the typical CT findings and 0.67 (95% CI 0.61, 0.74; I2 = 99%) for the overall CT classifications. CONCLUSION The standardized typical CT findings for COVID-19 provided moderate sensitivity and high specificity globally, regardless of region and national income, and were highly reproducible between radiologists. CRITICAL RELEVANCE STATEMENT Standardized typical CT findings for COVID-19 provided a reproducible high diagnostic accuracy globally. KEY POINTS Standardized typical CT findings for COVID-19 provide high sensitivity and specificity. Typical CT findings show high diagnosability regardless of region or income. The interobserver agreement for typical findings of COVID-19 is substantial.
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Affiliation(s)
- Bo Da Nam
- Department of Radiology, Soonchunhyang University College of Medicine, Soonchunhyang University Seoul Hospital, Seoul, Republic of Korea
| | - Hyunsook Hong
- Medical Research Collaborating Center, Seoul National University Hospital, Seoul, Republic of Korea
| | - Soon Ho Yoon
- Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
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Tiwari S, Chanak P, Singh SK. A Review of the Machine Learning Algorithms for Covid-19 Case Analysis. IEEE TRANSACTIONS ON ARTIFICIAL INTELLIGENCE 2023; 4:44-59. [PMID: 36908643 PMCID: PMC9983698 DOI: 10.1109/tai.2022.3142241] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 12/25/2021] [Indexed: 11/09/2022]
Abstract
The purpose of this article is to see how machine learning (ML) algorithms and applications are used in the COVID-19 inquiry and for other purposes. The available traditional methods for COVID-19 international epidemic prediction, researchers and authorities have given more attention to simple statistical and epidemiological methodologies. The inadequacy and absence of medical testing for diagnosing and identifying a solution is one of the key challenges in preventing the spread of COVID-19. A few statistical-based improvements are being strengthened to answer this challenge, resulting in a partial resolution up to a certain level. ML have advocated a wide range of intelligence-based approaches, frameworks, and equipment to cope with the issues of the medical industry. The application of inventive structure, such as ML and other in handling COVID-19 relevant outbreak difficulties, has been investigated in this article. The major goal of this article is to 1) Examining the impact of the data type and data nature, as well as obstacles in data processing for COVID-19. 2) Better grasp the importance of intelligent approaches like ML for the COVID-19 pandemic. 3) The development of improved ML algorithms and types of ML for COVID-19 prognosis. 4) Examining the effectiveness and influence of various strategies in COVID-19 pandemic. 5) To target on certain potential issues in COVID-19 diagnosis in order to motivate academics to innovate and expand their knowledge and research into additional COVID-19-affected industries.
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Affiliation(s)
- Shrikant Tiwari
- Department of Computer Science and EngineeringIndian Institute of Technology (BHU) Varanasi 221005 India
| | - Prasenjit Chanak
- Department of Computer Science and EngineeringIndian Institute of Technology (BHU) Varanasi 221005 India
| | - Sanjay Kumar Singh
- Department of Computer Science and EngineeringIndian Institute of Technology (BHU) Varanasi 221005 India
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Kwekha-Rashid AS, Abduljabbar HN, Alhayani B. Coronavirus disease (COVID-19) cases analysis using machine-learning applications. APPLIED NANOSCIENCE 2023; 13:2013-2025. [PMID: 34036034 PMCID: PMC8138510 DOI: 10.1007/s13204-021-01868-7] [Citation(s) in RCA: 74] [Impact Index Per Article: 74.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2021] [Accepted: 05/04/2021] [Indexed: 12/23/2022]
Abstract
Today world thinks about coronavirus disease that which means all even this pandemic disease is not unique. The purpose of this study is to detect the role of machine-learning applications and algorithms in investigating and various purposes that deals with COVID-19. Review of the studies that had been published during 2020 and were related to this topic by seeking in Science Direct, Springer, Hindawi, and MDPI using COVID-19, machine learning, supervised learning, and unsupervised learning as keywords. The total articles obtained were 16,306 overall but after limitation; only 14 researches of these articles were included in this study. Our findings show that machine learning can produce an important role in COVID-19 investigations, prediction, and discrimination. In conclusion, machine learning can be involved in the health provider programs and plans to assess and triage the COVID-19 cases. Supervised learning showed better results than other Unsupervised learning algorithms by having 92.9% testing accuracy. In the future recurrent supervised learning can be utilized for superior accuracy.
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Affiliation(s)
- Ameer Sardar Kwekha-Rashid
- Business Information Technology, College of Administration and Economics, University of Sulaimani, Sulaimaniya, Iraq
| | - Heamn N. Abduljabbar
- College of Education, Physics Department, Salahaddin University, Shaqlawa, Iraq ,Department of radiology and imagingFaculty of Medicine and Health Sciences, Universiti Putra Malaysia UPM, Seri Kembangan, Malaysia
| | - Bilal Alhayani
- Electronics and Communication Department, Yildiz Technical University, Istanbul, Turkey
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Cremades-Martínez P, Parker LA, Chilet-Rosell E, Lumbreras B. Evaluation of Diagnostic Strategies for Identifying SARS-CoV-2 Infection in Clinical Practice: a Systematic Review and Compliance with the Standards for Reporting Diagnostic Accuracy Studies Guideline (STARD). Microbiol Spectr 2022; 10:e0030022. [PMID: 35699441 PMCID: PMC9430610 DOI: 10.1128/spectrum.00300-22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 05/12/2022] [Indexed: 11/21/2022] Open
Abstract
We aimed to review strategies for identifying SARS-CoV-2 infection before the availability of molecular test results, and to assess the reporting quality of the studies identified through the application of the STARD guideline. We screened 3,821 articles published until 30 April 2021, of which 23 met the inclusion criteria: including at least two diagnostic variables, being designed for use in clinical practice or in a public health context and providing diagnostic accuracy rates. Data extraction and application of STARD criteria were performed independently by two researchers and discrepancies were discussed with a third author. Most of the studies (16, 69.6%) included symptomatic patients with suspected infection, six studies (26.1%) included patients already diagnosed and one study (4.3%) included individuals with close contact to a COVID-positive patient. The main variables considered in the studies, which included symptomatic patients, were imaging and demographic characteristics, symptoms, and lymphocyte count. The values for area under the receiver operating characteristic curve (AUC)ranged from 53-97.4. Seven studies (30.4%) validated the diagnostic model in an independent sample. The average number of STARD criteria fulfilled was 17.6 (maximum, 27 and minimum, 5). High diagnostic accuracy values are shown when more than one diagnostic variable is considered, mainly imaging and demographic characteristics, symptoms, and lymphocyte count. This could offer the potential to identify individuals with SARS-CoV-2 infection with high accuracy when molecular testing is not available. However, external validation for developed models and evaluations in populations as similar as possible to those in which they will be applied is urgently needed. IMPORTANCE According to this review, the inclusion of more than one diagnostic test in the diagnostic process for COVID-19 infection shows high diagnostic accuracy values. Imaging characteristics, patients' symptoms, demographic characteristics, and lymphocyte count were the variables most frequently included in the diagnostic models. However, developed models should be externally validated before reaching conclusions on their utility in practice. In addition, it is important to bear in mind that the test should be evaluated in populations as similar as possible to those in which it will be applied in practice.
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Affiliation(s)
| | - Lucy A. Parker
- Public Health, History of Medicine and Gynecology Department, Miguel Hernandez University, Alicante, Spain
- CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Elisa Chilet-Rosell
- Public Health, History of Medicine and Gynecology Department, Miguel Hernandez University, Alicante, Spain
- CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain
| | - Blanca Lumbreras
- Public Health, History of Medicine and Gynecology Department, Miguel Hernandez University, Alicante, Spain
- CIBER Epidemiology and Public Health (CIBERESP), Madrid, Spain
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Update on the limited sensitivity of computed tomography relative to RT-PCR for COVID-19: a systematic review. Pol J Radiol 2022; 87:e381-e391. [PMID: 35979154 PMCID: PMC9373863 DOI: 10.5114/pjr.2022.118238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Accepted: 10/15/2021] [Indexed: 01/08/2023] Open
Abstract
Purpose The global and ongoing COVID-19 outbreak has compelled the need for timely and reliable methods of detection for SARS-CoV-2 infection. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely accepted as a reference standard for COVID-19 diagnosis, several early studies have suggested the superior sensitivity of computed tomography (CT) in identifying SARS-CoV-2 infection. In a previous systematic review, we stratified studies based on risk for bias to evaluate the true sensitivity of CT for detecting SARS-CoV-2 infection. This study revisits our prior analysis, incorporating more current data to assess the sensitivity of CT for COVID-19. Material and methods The PubMed and Google Scholar databases were searched for relevant articles published between 1 January 2020, and 25 April 2021. Exclusion criteria included lack of specification regarding whether the study cohort was adult or paediatric, whether patients were symptomatic or asymptomatic, and not identifying the source of RT-PCR specimens. Ultimately, 62 studies were included for systematic review and were subsequently stratified by risk for bias using the QUADAS-2 quality assessment tool. Sensitivity data were extracted for random effects meta-analyses. Results The average sensitivity for COVID-19 reported by the high-risk-of-bias studies was 68% [CI: 58, 80; range: 38-96%] for RT-PCR and 91% [CI: 87, 96; range: 47-100%] for CT. The average sensitivity reported by the low-risk-of-bias studies was 84% [CI: 0.75, 0.94; range: 70-97%] for RT-PCR and 78% [CI: 71, 0.86; range: 44-92%] for CT. Conclusions On average, the high-risk-of bias studies underestimated the sensitivity of RT-PCR and overestimated the sensitivity of CT for COVID-19. Given the incorporation of recently published low-risk-of-bias articles, the sensitivities according to low-risk-of-bias studies for both RT-PCR and CT were higher than previously reported.
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Ferritin, blood urea nitrogen, and high chest CT score determines ICU admission in COVID-19 positive UAE patients: A single center retrospective study. PLoS One 2022; 17:e0269185. [PMID: 35852999 PMCID: PMC9295942 DOI: 10.1371/journal.pone.0269185] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 05/17/2022] [Indexed: 11/29/2022] Open
Abstract
Coronavirus Disease (COVID-19) was declared a pandemic by WHO in March 2020. Since then, additional novel coronavirus variants have emerged challenging the current healthcare system worldwide. There is an increased need for hospital care, especially intensive care unit (ICU), for the patients severely affected by the disease. Most of the studies analyzed COVID-19 infected patients in the hospitals and established the positive correlation between clinical parameters such as high levels of D-dimer, C-reactive protein, and ferritin to the severity of infection. However, little is known about the course of the ICU admission. The retrospective study carried out at University Hospital Sharjah, UAE presented here reports an integrated analysis of the biochemical and radiological factors among the newly admitted COVID-19 patients to decide on their ICU admission. The descriptive statistical analysis revealed that patients with clinical presentations such as acute respiratory distress syndrome (ARDS) (p<0.0001) at the time of admission needed intensive care. The ROC plot indicated that radiological factors including high chest CT scores (>CO-RADS 4) in combination with biochemical parameters such as higher levels of blood urea nitrogen (>6.7 mg/dL;66% sensitivity and 75.8% specificity) and ferritin (>290 μg/mL, 71.4% sensitivity and 77.8% specificity) may predict ICU admission with 94.2% accuracy among COVID-19 patients. Collectively, these findings would benefit the hospitals to predict the ICU admission amongst COVID-19 infected patients.
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Diagnostic Performance of Antigen Rapid Diagnostic Tests, Chest Computed Tomography, and Lung Point-of-Care-Ultrasonography for SARS-CoV-2 Compared with RT-PCR Testing: A Systematic Review and Network Meta-Analysis. Diagnostics (Basel) 2022; 12:diagnostics12061302. [PMID: 35741112 PMCID: PMC9222155 DOI: 10.3390/diagnostics12061302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Revised: 05/04/2022] [Accepted: 05/20/2022] [Indexed: 12/10/2022] Open
Abstract
(1) Background: The comparative performance of various diagnostic methods for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection remains unclear. This study aimed to investigate the comparison of the 3 index test performances of rapid antigen diagnostic tests (RDTs), chest computed tomography (CT), and lung point-of-care-ultrasonography (US) with reverse transcription-polymerase chain reaction (RT-PCR), the reference standard, to provide more evidence-based data on the appropriate use of these index tests. (2) Methods: We retrieved data from electronic literature searches of PubMed, Cochrane Library, and EMBASE from 1 January 2020, to 1 April 2021. Diagnostic performance was examined using bivariate random-effects diagnostic test accuracy (DTA) and Bayesian network meta-analysis (NMA) models. (3) Results: Of the 3992 studies identified in our search, 118 including 69,445 participants met our selection criteria. Among these, 69 RDT, 38 CT, and 15 US studies in the pairwise meta-analysis were included for DTA with NMA. CT and US had high sensitivity of 0.852 (95% credible interval (CrI), 0.791–0.914) and 0.879 (95% CrI, 0.784–0.973), respectively. RDT had high specificity, 0.978 (95% CrI, 0.960–0.996). In accuracy assessment, RDT and CT had a relatively higher than US. However, there was no significant difference in accuracy between the 3 index tests. (4) Conclusions: This meta-analysis suggests that, compared with the reference standard RT-PCR, the 3 index tests (RDTs, chest CT, and lung US) had similar and complementary performances for diagnosis of SARS-CoV-2 infection. To manage and control COVID-19 effectively, future large-scale prospective studies could be used to obtain an optimal timely diagnostic process that identifies the condition of the patient accurately.
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Ebrahimzadeh S, Islam N, Dawit H, Salameh JP, Kazi S, Fabiano N, Treanor L, Absi M, Ahmad F, Rooprai P, Al Khalil A, Harper K, Kamra N, Leeflang MM, Hooft L, van der Pol CB, Prager R, Hare SS, Dennie C, Spijker R, Deeks JJ, Dinnes J, Jenniskens K, Korevaar DA, Cohen JF, Van den Bruel A, Takwoingi Y, van de Wijgert J, Wang J, Pena E, Sabongui S, McInnes MD. Thoracic imaging tests for the diagnosis of COVID-19. Cochrane Database Syst Rev 2022; 5:CD013639. [PMID: 35575286 PMCID: PMC9109458 DOI: 10.1002/14651858.cd013639.pub5] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
BACKGROUND Our March 2021 edition of this review showed thoracic imaging computed tomography (CT) to be sensitive and moderately specific in diagnosing COVID-19 pneumonia. This new edition is an update of the review. OBJECTIVES Our objectives were to evaluate the diagnostic accuracy of thoracic imaging in people with suspected COVID-19; assess the rate of positive imaging in people who had an initial reverse transcriptase polymerase chain reaction (RT-PCR) negative result and a positive RT-PCR result on follow-up; and evaluate the accuracy of thoracic imaging for screening COVID-19 in asymptomatic individuals. The secondary objective was to assess threshold effects of index test positivity on accuracy. SEARCH METHODS We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 17 February 2021. We did not apply any language restrictions. SELECTION CRITERIA We included diagnostic accuracy studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19. Studies had to assess chest CT, chest X-ray, or ultrasound of the lungs for the diagnosis of COVID-19, use a reference standard that included RT-PCR, and report estimates of test accuracy or provide data from which we could compute estimates. We excluded studies that used imaging as part of the reference standard and studies that excluded participants with normal index test results. DATA COLLECTION AND ANALYSIS The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using QUADAS-2. We presented sensitivity and specificity per study on paired forest plots, and summarized pooled estimates in tables. We used a bivariate meta-analysis model where appropriate. MAIN RESULTS We included 98 studies in this review. Of these, 94 were included for evaluating the diagnostic accuracy of thoracic imaging in the evaluation of people with suspected COVID-19. Eight studies were included for assessing the rate of positive imaging in individuals with initial RT-PCR negative results and positive RT-PCR results on follow-up, and 10 studies were included for evaluating the accuracy of thoracic imaging for imagining asymptomatic individuals. For all 98 included studies, risk of bias was high or unclear in 52 (53%) studies with respect to participant selection, in 64 (65%) studies with respect to reference standard, in 46 (47%) studies with respect to index test, and in 48 (49%) studies with respect to flow and timing. Concerns about the applicability of the evidence to: participants were high or unclear in eight (8%) studies; index test were high or unclear in seven (7%) studies; and reference standard were high or unclear in seven (7%) studies. Imaging in people with suspected COVID-19 We included 94 studies. Eighty-seven studies evaluated one imaging modality, and seven studies evaluated two imaging modalities. All studies used RT-PCR alone or in combination with other criteria (for example, clinical signs and symptoms, positive contacts) as the reference standard for the diagnosis of COVID-19. For chest CT (69 studies, 28285 participants, 14,342 (51%) cases), sensitivities ranged from 45% to 100%, and specificities from 10% to 99%. The pooled sensitivity of chest CT was 86.9% (95% confidence interval (CI) 83.6 to 89.6), and pooled specificity was 78.3% (95% CI 73.7 to 82.3). Definition for index test positivity was a source of heterogeneity for sensitivity, but not specificity. Reference standard was not a source of heterogeneity. For chest X-ray (17 studies, 8529 participants, 5303 (62%) cases), the sensitivity ranged from 44% to 94% and specificity from 24 to 93%. The pooled sensitivity of chest X-ray was 73.1% (95% CI 64. to -80.5), and pooled specificity was 73.3% (95% CI 61.9 to 82.2). Definition for index test positivity was not found to be a source of heterogeneity. Definition for index test positivity and reference standard were not found to be sources of heterogeneity. For ultrasound of the lungs (15 studies, 2410 participants, 1158 (48%) cases), the sensitivity ranged from 73% to 94% and the specificity ranged from 21% to 98%. The pooled sensitivity of ultrasound was 88.9% (95% CI 84.9 to 92.0), and the pooled specificity was 72.2% (95% CI 58.8 to 82.5). Definition for index test positivity and reference standard were not found to be sources of heterogeneity. Indirect comparisons of modalities evaluated across all 94 studies indicated that chest CT and ultrasound gave higher sensitivity estimates than X-ray (P = 0.0003 and P = 0.001, respectively). Chest CT and ultrasound gave similar sensitivities (P=0.42). All modalities had similar specificities (CT versus X-ray P = 0.36; CT versus ultrasound P = 0.32; X-ray versus ultrasound P = 0.89). Imaging in PCR-negative people who subsequently became positive For rate of positive imaging in individuals with initial RT-PCR negative results, we included 8 studies (7 CT, 1 ultrasound) with a total of 198 participants suspected of having COVID-19, all of whom had a final diagnosis of COVID-19. Most studies (7/8) evaluated CT. Of 177 participants with initially negative RT-PCR who had positive RT-PCR results on follow-up testing, 75.8% (95% CI 45.3 to 92.2) had positive CT findings. Imaging in asymptomatic PCR-positive people For imaging asymptomatic individuals, we included 10 studies (7 CT, 1 X-ray, 2 ultrasound) with a total of 3548 asymptomatic participants, of whom 364 (10%) had a final diagnosis of COVID-19. For chest CT (7 studies, 3134 participants, 315 (10%) cases), the pooled sensitivity was 55.7% (95% CI 35.4 to 74.3) and the pooled specificity was 91.1% (95% CI 82.6 to 95.7). AUTHORS' CONCLUSIONS Chest CT and ultrasound of the lungs are sensitive and moderately specific in diagnosing COVID-19. Chest X-ray is moderately sensitive and moderately specific in diagnosing COVID-19. Thus, chest CT and ultrasound may have more utility for ruling out COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. The uncertainty resulting from high or unclear risk of bias and the heterogeneity of included studies limit our ability to confidently draw conclusions based on our results.
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Affiliation(s)
- Sanam Ebrahimzadeh
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | - Nayaar Islam
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Haben Dawit
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | | | - Sakib Kazi
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | | | - Lee Treanor
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Marissa Absi
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Faraz Ahmad
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Paul Rooprai
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Ahmed Al Khalil
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Kelly Harper
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Neil Kamra
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Mariska Mg Leeflang
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Lotty Hooft
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht , Netherlands
| | | | - Ross Prager
- Department of Medicine, University of Ottawa, Ottawa, Canada
| | - Samanjit S Hare
- Department of Radiology, Royal Free London NHS Trust, London , UK
| | - Carole Dennie
- Department of Radiology, University of Ottawa, Ottawa, Canada
- Department of Medical Imaging, The Ottawa Hospital, Ottawa, Canada
| | - René Spijker
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht , Netherlands
- Medical Library, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Amsterdam, Netherlands
| | - Jonathan J Deeks
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Jacqueline Dinnes
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Kevin Jenniskens
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Daniël A Korevaar
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Jérémie F Cohen
- Obstetrical, Perinatal and Pediatric Epidemiology Research Team (EPOPé), Centre of Research in Epidemiology and Statistics (CRESS), UMR1153, Université de Paris, Paris, France
| | | | - Yemisi Takwoingi
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
| | - Janneke van de Wijgert
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Institute of Infection, Veterinary, and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Junfeng Wang
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Elena Pena
- Department of Radiology, University of Ottawa, Ottawa, Canada
- Department of Medical Imaging, The Ottawa Hospital, Ottawa, Canada
| | | | - Matthew Df McInnes
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
- Department of Radiology, University of Ottawa, Ottawa, Canada
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Liu G, Chen Y, Runa A, Liu J. Diagnostic performance of CO-RADS for COVID-19: a systematic review and meta-analysis. Eur Radiol 2022; 32:4414-4426. [PMID: 35348865 PMCID: PMC8961267 DOI: 10.1007/s00330-022-08576-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 12/12/2021] [Accepted: 01/08/2022] [Indexed: 12/13/2022]
Abstract
Objectives To investigate the diagnostic performance of the coronavirus disease 2019 (COVID-19) Reporting and Data System (CO-RADS) for detecting COVID-19. Methods We searched PubMed, EMBASE, MEDLINE, Web of Science, Cochrane Library, and Scopus database until September 21, 2021. Statistical analysis included data pooling, forest plot construction, heterogeneity testing, meta-regression, and subgroup analyses. Results We included 24 studies with 8382 patients. The pooled sensitivity and specificity and the area under the curve (AUC) of CO-RADS ≥ 3 for detecting COVID-19 were 0.89 (95% confidence interval (CI) 0.85–0.93), 0.68 (95% CI 0.60–0.75), and 0.87 (95% CI 0.84–0.90), respectively. The pooled sensitivity and specificity and AUC of CO-RADS ≥ 4 were 0.83 (95% CI 0.79–0.87), 0.84 (95% CI 0.78–0.88), and 0.90 (95% CI 0.87–0.92), respectively. Cochran’s Q test (p < 0.01) and Higgins I2 heterogeneity index revealed considerable heterogeneity. Studies with both symptomatic and asymptomatic patients had higher specificity than those with only symptomatic patients using CO-RADS ≥ 3 and CO-RADS ≥ 4. Using CO-RADS ≥ 4, studies with participants aged < 60 years had higher sensitivity (0.88 vs. 0.80, p = 0.02) and lower specificity (0.77 vs. 0.87, p = 0.01) than studies with participants aged > 60 years. Conclusions CO-RADS has favorable performance in detecting COVID-19. CO-RADS ≥ 3/4 might be applied as cutoff values given their high sensitivity and specificity. However, there is a need for more well-designed studies on CO-RADS. Key Points • CO-RADS shows a favorable performance in detecting COVID-19. • CO-RADS ≥ 3 had a high sensitivity 0.89 (95% CI 0.85–0.93), and it may prove advantageous in screening the potentially infected people to prevent the spread of COVID-19. • CO-RADS ≥ 4 had high specificity 0.84 (95% CI 0.78–0.88) and may be more suitable for definite diagnosis of COVID-19.
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Jin KN, Do KH, Nam BD, Hwang SH, Choi M, Yong HS. [Korean Clinical Imaging Guidelines for Justification of Diagnostic Imaging Study for COVID-19]. TAEHAN YONGSANG UIHAKHOE CHI 2022; 83:265-283. [PMID: 36237918 PMCID: PMC9514447 DOI: 10.3348/jksr.2021.0117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 09/10/2021] [Accepted: 09/17/2021] [Indexed: 06/16/2023]
Abstract
To develop Korean coronavirus disease (COVID-19) chest imaging justification guidelines, eight key questions were selected and the following recommendations were made with the evidence-based clinical imaging guideline adaptation methodology. It is appropriate not to use chest imaging tests (chest radiograph or CT) for the diagnosis of COVID-19 in asymptomatic patients. If reverse transcription-polymerase chain reaction testing is not available or if results are delayed or are initially negative in the presence of symptoms suggestive of COVID-19, chest imaging tests may be considered. In addition to clinical evaluations and laboratory tests, chest imaging may be contemplated to determine hospital admission for asymptomatic or mildly symptomatic unhospitalized patients with confirmed COVID-19. In hospitalized patients with confirmed COVID-19, chest imaging may be advised to determine or modify treatment alternatives. CT angiography may be considered if hemoptysis or pulmonary embolism is clinically suspected in a patient with confirmed COVID-19. For COVID-19 patients with improved symptoms, chest imaging is not recommended to make decisions regarding hospital discharge. For patients with functional impairment after recovery from COVID-19, chest imaging may be considered to distinguish a potentially treatable disease.
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11
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Kaushal K, Kaur H, Sarma P, Bhattacharyya A, Sharma DJ, Prajapat M, Pathak M, Kothari A, Kumar S, Rana S, Kaur M, Prakash A, Mirza AA, Panda PK, Vivekanandan S, Omar BJ, Medhi B, Naithani M. Serum ferritin as a predictive biomarker in COVID-19. A systematic review, meta-analysis and meta-regression analysis. J Crit Care 2021; 67:172-181. [PMID: 34808527 PMCID: PMC8604557 DOI: 10.1016/j.jcrc.2021.09.023] [Citation(s) in RCA: 66] [Impact Index Per Article: 22.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 09/27/2021] [Accepted: 09/28/2021] [Indexed: 12/15/2022]
Abstract
Ferritin is a known inflammatory biomarker in COVID-19. However, many factors and co-morbidities can confound the level of serum ferritin. This current metaanalysis evaluates serum ferritin level in different severity levels in COVID-19. Studies evaluating serum ferritin level in different clinical contexts (COVID-19 vs. control, mild to moderate vs. severe to critical, non-survivor vs. survivor, organ involvement, ICU and mechanical ventilation requirement) were included (total 9 literature databases searched). Metaanalysis and metaregression was carried out using metaphor “R” package. Compared to control (COVID-19 negative), higher ferritin levels were found among the COVID-19 patients [SMD −0.889 (95% C.I. −1.201, −0.577), I2 = 85%]. Severe to critical COVID-19 patients showed higher ferritin levels compared to mild to moderate COVID-19 patients [SMD 0.882 (0.738, 1.026), I2 = 85%]. In meta-regression, high heterogeneity was observed could be attributed to difference in “mean age”, and “percentage of population with concomitant co-morbidities”. Non-survivors had higher serum ferritin level compared to survivors [SMD 0.992 (0.672, 1.172), I2 = 92.33%]. In meta-regression, high heterogeneity observed could be attributed to difference in “mean age” and “percentage of male sex”. Patients requiring ICU [SMD 0.674 (0.515 to 0.833), I2 = 80%] and mechanical ventilation [SMD 0.430 (0.258, 0.602), I2 = 32%] had higher serum ferritin levels compared to those who didn't. To conclude, serum ferritin level may serve as an important biomarker which can aid in COVID-19 management. However, presence of other co-morbid conditions/confounders warrants cautious interpretation.
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Affiliation(s)
| | - Hardeep Kaur
- Department of Pharmacology, PGIMER, Chandigarh, India
| | - Phulen Sarma
- Department of Pharmacology, PGIMER, Chandigarh, India
| | | | - Dibya Jyoti Sharma
- Department of Internal medicine, Silchar Medical College and Hospital, India
| | | | - Mona Pathak
- Department of Research & Development, Kalinga Institute of Medical Sciences, Bhubaneswar, India
| | | | - Subodh Kumar
- Department of Pharmacology, PGIMER, Chandigarh, India
| | | | | | - Ajay Prakash
- Department of Pharmacology, PGIMER, Chandigarh, India
| | | | | | | | | | - Bikash Medhi
- Department of Pharmacology, PGIMER, Chandigarh, India.
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12
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Inui S, Gonoi W, Kurokawa R, Nakai Y, Watanabe Y, Sakurai K, Ishida M, Fujikawa A, Abe O. The role of chest imaging in the diagnosis, management, and monitoring of coronavirus disease 2019 (COVID-19). Insights Imaging 2021; 12:155. [PMID: 34727257 PMCID: PMC8561360 DOI: 10.1186/s13244-021-01096-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Accepted: 09/22/2021] [Indexed: 02/07/2023] Open
Abstract
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.
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Affiliation(s)
- Shohei Inui
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. .,Department of Radiology, Japan Self-Defense Forces Central Hospital, 1-2-24, Ikejiri, Setagaya-ku, Tokyo, 154-0001, Japan.
| | - Wataru Gonoi
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Ryo Kurokawa
- Division of Neuroradiology, Department of Radiology, University of Michigan, 1500 E Medical Center Dr, UH B2, Ann Arbor, MI, 48109, USA
| | - Yudai Nakai
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Yusuke Watanabe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Keita Sakurai
- Department of Radiology, National Center for Geriatrics and Gerontology, 7-430, Morioka-cho, Obu, Aichi, 474-8511, Japan
| | - Masanori Ishida
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
| | - Akira Fujikawa
- Department of Radiology, Japan Self-Defense Forces Central Hospital, 1-2-24, Ikejiri, Setagaya-ku, Tokyo, 154-0001, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, 7-3-1, Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan
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13
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Liu J, Yang X, Zhu Y, Zhu Y, Liu J, Zeng X, Li H. Diagnostic value of chest computed tomography imaging for COVID-19 based on reverse transcription-polymerase chain reaction: a meta-analysis. Infect Dis Poverty 2021; 10:126. [PMID: 34674774 PMCID: PMC8529575 DOI: 10.1186/s40249-021-00910-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 10/08/2021] [Indexed: 11/11/2022] Open
Abstract
Background The computed tomography (CT) diagnostic value of COVID-19 is controversial. We summarized the value of chest CT in the diagnosis of COVID-19 through a meta-analysis based on the reference standard. Methods All Chinese and English studies related to the diagnostic value of CT for COVID-19 across multiple publication platforms, was searched for and collected. Studies quality evaluation and plotting the risk of bias were estimated. A heterogeneity test and meta-analysis, including plotting sensitivity (Sen), specificity (Spe) forest plots, pooled positive likelihood ratio (+LR), negative likelihood ratio (-LR), dignostic odds ratio (DOR) values and 95% confidence interval (CI), were estimated. If there was a threshold effect, summary receiver operating characteristic curves (SROC) was further plotted. Pooled area under the receiver operating characteristic curve (AUROC) and 95% CI were also calculated. Results Twenty diagnostic studies that represented a total of 9004 patients were included from 20 pieces of literatures after assessing all the aggregated studies. The reason for heterogeneity was caused by the threshold effect, so the AUROC = 0.91 (95% CI: 0.89–0.94) for chest CT of COVID-19. Pooled sensitivity, specificity, +LR, -LR from 20 studies were 0.91 (95% CI: 0.88–0.94), 0.71 (95% CI: 0.59–0.80), 3.1(95% CI: 2.2–4.4), 0.12 (95% CI: 0.09–0.17), separately. The I2 was 85.6% (P = 0.001) by Q-test. Conclusions The results of this study showed that CT diagnosis of COVID-19 was close to the reference standard. The diagnostic value of chest CT may be further enhanced if there is a unified COVID-19 diagnostic standard. However, please pay attention to rational use of CT. Graphic Abstract ![]()
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Affiliation(s)
- Jing Liu
- Department of Radiology, The Affiliated Infectious Diseases Hospital of Soochow University, The Fifth People's Hospital of Suzhou, Suzhou, 215000, Jiangsu, People's Republic of China
| | - Xue Yang
- Department of Radiology, Beijing Youan Hospital Capital Medical University, Beijing, 100069, People's Republic of China
| | - Yunxian Zhu
- Department of Radiology, The Affiliated Infectious Diseases Hospital of Soochow University, The Fifth People's Hospital of Suzhou, Suzhou, 215000, Jiangsu, People's Republic of China
| | - Yi Zhu
- Department of Radiology, The Affiliated Infectious Diseases Hospital of Soochow University, The Fifth People's Hospital of Suzhou, Suzhou, 215000, Jiangsu, People's Republic of China
| | - Jingzhe Liu
- Department of Radiology, The First Hospital of Tsinghua University, Beijing, 100016, People's Republic of China
| | - Xiantao Zeng
- Center for Evidence-Based and Translational Medicine, Zhongnan Hospital of Wuhan University, Wuhan, 430071, Hubei, People's Republic of China
| | - Hongjun Li
- Department of Radiology, Beijing Youan Hospital Capital Medical University, Beijing, 100069, People's Republic of China.
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14
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Laino ME, Ammirabile A, Posa A, Cancian P, Shalaby S, Savevski V, Neri E. The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review. Diagnostics (Basel) 2021; 11:1317. [PMID: 34441252 PMCID: PMC8394327 DOI: 10.3390/diagnostics11081317] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/02/2021] [Accepted: 07/09/2021] [Indexed: 12/23/2022] Open
Abstract
Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.
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Affiliation(s)
- Maria Elena Laino
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, Pieve Emanuele, 20072 Milan, Italy;
- Department of Radiology, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy
| | - Alessandro Posa
- Department of Diagnostic Imaging, Oncological Radiotherapy and Hematology, Fondazione Policlinico Universitario Agostino Gemelli—IRCCS, 00168 Rome, Italy;
| | - Pierandrea Cancian
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Sherif Shalaby
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Via Roma 67, 56126 Pisa, Italy; (S.S.); (E.N.)
| | - Victor Savevski
- Artificial Intelligence Center, IRCCS Humanitas Research Hospital, via Manzoni 56, Rozzano, 20089 Milan, Italy; (P.C.); (V.S.)
| | - Emanuele Neri
- Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, Via Roma 67, 56126 Pisa, Italy; (S.S.); (E.N.)
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, Via della Signora 2, 20122 Milano, Italy
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15
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Guo Y, Zhang Y, Lyu T, Prosperi M, Wang F, Xu H, Bian J. The application of artificial intelligence and data integration in COVID-19 studies: a scoping review. J Am Med Inform Assoc 2021; 28:2050-2067. [PMID: 34151987 PMCID: PMC8344463 DOI: 10.1093/jamia/ocab098] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Revised: 05/03/2021] [Accepted: 05/06/2021] [Indexed: 12/23/2022] Open
Abstract
Objective To summarize how artificial intelligence (AI) is being applied in COVID-19 research and determine whether these AI applications integrated heterogenous data from different sources for modeling. Materials and Methods We searched 2 major COVID-19 literature databases, the National Institutes of Health’s LitCovid and the World Health Organization’s COVID-19 database on March 9, 2021. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, 2 reviewers independently reviewed all the articles in 2 rounds of screening. Results In the 794 studies included in the final qualitative analysis, we identified 7 key COVID-19 research areas in which AI was applied, including disease forecasting, medical imaging-based diagnosis and prognosis, early detection and prognosis (non-imaging), drug repurposing and early drug discovery, social media data analysis, genomic, transcriptomic, and proteomic data analysis, and other COVID-19 research topics. We also found that there was a lack of heterogenous data integration in these AI applications. Discussion Risk factors relevant to COVID-19 outcomes exist in heterogeneous data sources, including electronic health records, surveillance systems, sociodemographic datasets, and many more. However, most AI applications in COVID-19 research adopted a single-sourced approach that could omit important risk factors and thus lead to biased algorithms. Integrating heterogeneous data for modeling will help realize the full potential of AI algorithms, improve precision, and reduce bias. Conclusion There is a lack of data integration in the AI applications in COVID-19 research and a need for a multilevel AI framework that supports the analysis of heterogeneous data from different sources.
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Affiliation(s)
- Yi Guo
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.,Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, Florida, USA
| | - Yahan Zhang
- Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, Florida, USA
| | - Tianchen Lyu
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.,Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, Florida, USA
| | - Mattia Prosperi
- Department of Epidemiology, College of Public Health and Health Professions & College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Hua Xu
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, Texas, USA
| | - Jiang Bian
- Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA.,Cancer Informatics Shared Resource, University of Florida Health Cancer Center, Gainesville, Florida, USA
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16
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Machine learning is the key to diagnose COVID-19: a proof-of-concept study. Sci Rep 2021; 11:7166. [PMID: 33785852 PMCID: PMC8009887 DOI: 10.1038/s41598-021-86735-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 03/16/2021] [Indexed: 12/28/2022] Open
Abstract
The reverse transcription-polymerase chain reaction (RT-PCR) assay is the accepted standard for coronavirus disease 2019 (COVID-19) diagnosis. As any test, RT-PCR provides false negative results that can be rectified by clinicians by confronting clinical, biological and imaging data. The combination of RT-PCR and chest-CT could improve diagnosis performance, but this would requires considerable resources for its rapid use in all patients with suspected COVID-19. The potential contribution of machine learning in this situation has not been fully evaluated. The objective of this study was to develop and evaluate machine learning models using routine clinical and laboratory data to improve the performance of RT-PCR and chest-CT for COVID-19 diagnosis among post-emergency hospitalized patients. All adults admitted to the ED for suspected COVID-19, and then hospitalized at Rennes academic hospital, France, between March 20, 2020 and May 5, 2020 were included in the study. Three model types were created: logistic regression, random forest, and neural network. Each model was trained to diagnose COVID-19 using different sets of variables. Area under the receiving operator characteristics curve (AUC) was the primary outcome to evaluate model’s performances. 536 patients were included in the study: 106 in the COVID group, 430 in the NOT-COVID group. The AUC values of chest-CT and RT-PCR increased from 0.778 to 0.892 and from 0.852 to 0.930, respectively, with the contribution of machine learning. After generalization, machine learning models will allow increasing chest-CT and RT-PCR performances for COVID-19 diagnosis.
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17
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Islam N, Ebrahimzadeh S, Salameh JP, Kazi S, Fabiano N, Treanor L, Absi M, Hallgrimson Z, Leeflang MM, Hooft L, van der Pol CB, Prager R, Hare SS, Dennie C, Spijker R, Deeks JJ, Dinnes J, Jenniskens K, Korevaar DA, Cohen JF, Van den Bruel A, Takwoingi Y, van de Wijgert J, Damen JA, Wang J, McInnes MD. Thoracic imaging tests for the diagnosis of COVID-19. Cochrane Database Syst Rev 2021; 3:CD013639. [PMID: 33724443 PMCID: PMC8078565 DOI: 10.1002/14651858.cd013639.pub4] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND The respiratory illness caused by SARS-CoV-2 infection continues to present diagnostic challenges. Our 2020 edition of this review showed thoracic (chest) imaging to be sensitive and moderately specific in the diagnosis of coronavirus disease 2019 (COVID-19). In this update, we include new relevant studies, and have removed studies with case-control designs, and those not intended to be diagnostic test accuracy studies. OBJECTIVES To evaluate the diagnostic accuracy of thoracic imaging (computed tomography (CT), X-ray and ultrasound) in people with suspected COVID-19. SEARCH METHODS We searched the COVID-19 Living Evidence Database from the University of Bern, the Cochrane COVID-19 Study Register, The Stephen B. Thacker CDC Library, and repositories of COVID-19 publications through to 30 September 2020. We did not apply any language restrictions. SELECTION CRITERIA We included studies of all designs, except for case-control, that recruited participants of any age group suspected to have COVID-19 and that reported estimates of test accuracy or provided data from which we could compute estimates. DATA COLLECTION AND ANALYSIS The review authors independently and in duplicate screened articles, extracted data and assessed risk of bias and applicability concerns using the QUADAS-2 domain-list. We presented the results of estimated sensitivity and specificity using paired forest plots, and we summarised pooled estimates in tables. We used a bivariate meta-analysis model where appropriate. We presented the uncertainty of accuracy estimates using 95% confidence intervals (CIs). MAIN RESULTS We included 51 studies with 19,775 participants suspected of having COVID-19, of whom 10,155 (51%) had a final diagnosis of COVID-19. Forty-seven studies evaluated one imaging modality each, and four studies evaluated two imaging modalities each. All studies used RT-PCR as the reference standard for the diagnosis of COVID-19, with 47 studies using only RT-PCR and four studies using a combination of RT-PCR and other criteria (such as clinical signs, imaging tests, positive contacts, and follow-up phone calls) as the reference standard. Studies were conducted in Europe (33), Asia (13), North America (3) and South America (2); including only adults (26), all ages (21), children only (1), adults over 70 years (1), and unclear (2); in inpatients (2), outpatients (32), and setting unclear (17). Risk of bias was high or unclear in thirty-two (63%) studies with respect to participant selection, 40 (78%) studies with respect to reference standard, 30 (59%) studies with respect to index test, and 24 (47%) studies with respect to participant flow. For chest CT (41 studies, 16,133 participants, 8110 (50%) cases), the sensitivity ranged from 56.3% to 100%, and specificity ranged from 25.4% to 97.4%. The pooled sensitivity of chest CT was 87.9% (95% CI 84.6 to 90.6) and the pooled specificity was 80.0% (95% CI 74.9 to 84.3). There was no statistical evidence indicating that reference standard conduct and definition for index test positivity were sources of heterogeneity for CT studies. Nine chest CT studies (2807 participants, 1139 (41%) cases) used the COVID-19 Reporting and Data System (CO-RADS) scoring system, which has five thresholds to define index test positivity. At a CO-RADS threshold of 5 (7 studies), the sensitivity ranged from 41.5% to 77.9% and the pooled sensitivity was 67.0% (95% CI 56.4 to 76.2); the specificity ranged from 83.5% to 96.2%; and the pooled specificity was 91.3% (95% CI 87.6 to 94.0). At a CO-RADS threshold of 4 (7 studies), the sensitivity ranged from 56.3% to 92.9% and the pooled sensitivity was 83.5% (95% CI 74.4 to 89.7); the specificity ranged from 77.2% to 90.4% and the pooled specificity was 83.6% (95% CI 80.5 to 86.4). For chest X-ray (9 studies, 3694 participants, 2111 (57%) cases) the sensitivity ranged from 51.9% to 94.4% and specificity ranged from 40.4% to 88.9%. The pooled sensitivity of chest X-ray was 80.6% (95% CI 69.1 to 88.6) and the pooled specificity was 71.5% (95% CI 59.8 to 80.8). For ultrasound of the lungs (5 studies, 446 participants, 211 (47%) cases) the sensitivity ranged from 68.2% to 96.8% and specificity ranged from 21.3% to 78.9%. The pooled sensitivity of ultrasound was 86.4% (95% CI 72.7 to 93.9) and the pooled specificity was 54.6% (95% CI 35.3 to 72.6). Based on an indirect comparison using all included studies, chest CT had a higher specificity than ultrasound. For indirect comparisons of chest CT and chest X-ray, or chest X-ray and ultrasound, the data did not show differences in specificity or sensitivity. AUTHORS' CONCLUSIONS Our findings indicate that chest CT is sensitive and moderately specific for the diagnosis of COVID-19. Chest X-ray is moderately sensitive and moderately specific for the diagnosis of COVID-19. Ultrasound is sensitive but not specific for the diagnosis of COVID-19. Thus, chest CT and ultrasound may have more utility for excluding COVID-19 than for differentiating SARS-CoV-2 infection from other causes of respiratory illness. Future diagnostic accuracy studies should pre-define positive imaging findings, include direct comparisons of the various modalities of interest in the same participant population, and implement improved reporting practices.
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Affiliation(s)
- Nayaar Islam
- Department of Radiology , University of Ottawa, Ottawa, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
| | | | | | - Sakib Kazi
- Department of Radiology , University of Ottawa, Ottawa, Canada
| | | | - Lee Treanor
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | - Marissa Absi
- Department of Radiology, University of Ottawa, Ottawa, Canada
| | | | - Mariska Mg Leeflang
- Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Lotty Hooft
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht , Netherlands
| | | | - Ross Prager
- Department of Medicine, University of Ottawa , Ottawa, Canada
| | - Samanjit S Hare
- Department of Radiology , Royal Free London NHS Trust, London , UK
| | - Carole Dennie
- Department of Radiology , University of Ottawa, Ottawa, Canada
- Department of Medical Imaging, The Ottawa Hospital, Ottawa, Canada
| | - René Spijker
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht , Netherlands
- Medical Library, Amsterdam UMC, University of Amsterdam, Amsterdam Public Health, Amsterdam, Netherlands
| | - Jonathan J Deeks
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Jacqueline Dinnes
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham , UK
| | - Kevin Jenniskens
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Daniël A Korevaar
- Department of Respiratory Medicine, Amsterdam UMC, University of Amsterdam, Amsterdam, Netherlands
| | - Jérémie F Cohen
- Obstetrical, Perinatal and Pediatric Epidemiology Research Team (EPOPé), Centre of Research in Epidemiology and Statistics (CRESS), UMR1153, Université de Paris, Paris, France
| | | | - Yemisi Takwoingi
- NIHR Birmingham Biomedical Research Centre, University Hospitals Birmingham NHS Foundation Trust and University of Birmingham, Birmingham, UK
- Test Evaluation Research Group, Institute of Applied Health Research, University of Birmingham, Birmingham, UK
| | - Janneke van de Wijgert
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
- Institute of Infection, Veterinary, and Ecological Sciences, University of Liverpool, Liverpool, UK
| | - Johanna Aag Damen
- Cochrane Netherlands, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Junfeng Wang
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, Netherlands
| | - Matthew Df McInnes
- Department of Radiology, University of Ottawa, Ottawa, Canada
- Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Canada
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Brogna B, Bignardi E, Brogna C, Volpe M, Lombardi G, Rosa A, Gagliardi G, Capasso PFM, Gravino E, Maio F, Pane F, Picariello V, Buono M, Colucci L, Musto LA. A Pictorial Review of the Role of Imaging in the Detection, Management, Histopathological Correlations, and Complications of COVID-19 Pneumonia. Diagnostics (Basel) 2021; 11:437. [PMID: 33806423 PMCID: PMC8000129 DOI: 10.3390/diagnostics11030437] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2020] [Revised: 02/22/2021] [Accepted: 02/27/2021] [Indexed: 02/07/2023] Open
Abstract
Imaging plays an important role in the detection of coronavirus (COVID-19) pneumonia in both managing the disease and evaluating the complications. Imaging with chest computed tomography (CT) can also have a potential predictive and prognostic role in COVID-19 patient outcomes. The aim of this pictorial review is to describe the role of imaging with chest X-ray (CXR), lung ultrasound (LUS), and CT in the diagnosis and management of COVID-19 pneumonia, the current indications, the scores proposed for each modality, the advantages/limitations of each modality and their role in detecting complications, and the histopathological correlations.
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Affiliation(s)
- Barbara Brogna
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Elio Bignardi
- Radiology Unit, Cotugno Hospital, Naples, Via Quagliariello 54, 80131 Naples, Italy;
| | - Claudia Brogna
- Neuropsychiatric Unit ASL Avellino, Via Degli Imbimbo 10/12, 83100 Avellino, Italy;
| | - Mena Volpe
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Giulio Lombardi
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Alessandro Rosa
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Giuliano Gagliardi
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Pietro Fabio Maurizio Capasso
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Enzo Gravino
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Francesca Maio
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Francesco Pane
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Valentina Picariello
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Marcella Buono
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Lorenzo Colucci
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
| | - Lanfranco Aquilino Musto
- Department of Radiology, San Giuseppe Moscati Hospital, Contrada Amoretta, 83100 Avellino, Italy; (M.V.); (G.L.); (A.R.); (G.G.); (P.F.M.C.); (E.G.); (F.M.); (F.P.); (V.P.); (M.B.); (L.C.); (L.A.M.)
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Kwee RM, Adams HJA, Kwee TC. Diagnostic Performance of CO-RADS and the RSNA Classification System in Evaluating COVID-19 at Chest CT: A Meta-Analysis. Radiol Cardiothorac Imaging 2021; 3:e200510. [PMID: 33778660 PMCID: PMC7808356 DOI: 10.1148/ryct.2021200510] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
PURPOSE To determine the diagnostic performance of the COVID-19 Reporting and Data System (CO-RADS) and the Radiological Society of North America (RSNA) categorizations in patients with clinically suspected coronavirus disease 2019 (COVID-19) infection. MATERIALS AND METHODS In this meta-analysis, studies from 2020, up to August 24, 2020 were assessed for inclusion criteria of studies that used CO-RADS or the RSNA categories for scoring chest CT in patients with suspected COVID-19. A total of 186 studies were identified. After review of abstracts and text, a total of nine studies were included in this study. Patient information (n¸ age, sex), CO-RADS and RSNA scoring categories, and other study characteristics were extracted. Study quality was assessed with the QUADAS-2 tool. Meta-analysis was performed with a random effects model. RESULTS Nine studies (3283 patients) were included. Overall study quality was good, except for risk of non-performance of repeated reverse transcriptase polymerase chain reaction (RT-PCR) after negative initial RT-PCR and persistent clinical suspicion in four studies. Pooled COVID-19 frequencies in CO-RADS categories were: 1, 8.8%; 2, 11.1%; 3, 24.6%; 4, 61.9%; and 5, 89.6%. Pooled COVID-19 frequencies in RSNA classification categories were: negative 14.4%; atypical, 5.7%; indeterminate, 44.9%; and typical, 92.5%. Pooled pairs of sensitivity and specificity using CO-RADS thresholds were the following: at least 3, 92.5% (95% CI: 87.1, 95.7) and 69.2% (95%: CI: 60.8, 76.4); at least 4, 85.8% (95% CI: 78.7, 90.9) and 84.6% (95% CI: 79.5, 88.5); and 5, 70.4% (95% CI: 60.2, 78.9) and 93.1% (95% CI: 87.7, 96.2). Pooled pairs of sensitivity and specificity using RSNA classification thresholds for indeterminate were 90.2% (95% CI: 87.5, 92.3) and 75.1% (95% CI: 68.9, 80.4) and for typical were 65.2% (95% CI: 37.0, 85.7) and 94.9% (95% CI: 86.4, 98.2). CONCLUSION COVID-19 infection frequency was higher in patients categorized with higher CORADS and RSNA classification categories.
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Affiliation(s)
- Robert M. Kwee
- From the Department of Radiology, Zuyderland Medical Center, Heerlen/Sittard/Geleen, The Netherlands (R.M.K.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands. (H.J.A.A.); Department of Radiology, Nuclear Medicine and Molecular Imaging University Medical Center Groningen, University of Groningen, Groningen, The Netherlands (T.C.K.)
| | - Hugo J. A. Adams
- From the Department of Radiology, Zuyderland Medical Center, Heerlen/Sittard/Geleen, The Netherlands (R.M.K.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands. (H.J.A.A.); Department of Radiology, Nuclear Medicine and Molecular Imaging University Medical Center Groningen, University of Groningen, Groningen, The Netherlands (T.C.K.)
| | - Thomas C. Kwee
- From the Department of Radiology, Zuyderland Medical Center, Heerlen/Sittard/Geleen, The Netherlands (R.M.K.); Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, The Netherlands. (H.J.A.A.); Department of Radiology, Nuclear Medicine and Molecular Imaging University Medical Center Groningen, University of Groningen, Groningen, The Netherlands (T.C.K.)
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Sun R, Ammari S, Bockel S, Achkar S, Merad M, Dercle L, Rivera S, Chargari C, Deutsch E. Optimization of Patient Management During the COVID-19 Pandemic: Chest CT Scan and PCR as Gatekeepers of the Radiation Therapy Workflow. Front Oncol 2020; 10:556334. [PMID: 33312944 PMCID: PMC7708327 DOI: 10.3389/fonc.2020.556334] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Accepted: 10/23/2020] [Indexed: 12/12/2022] Open
Affiliation(s)
- Roger Sun
- Gustave Roussy, Département de Radiothérapie, INSERM 1030, Université Paris-Saclay, Villejuif, France
| | - Samy Ammari
- Gustave Roussy, Département d'Imagerie Médicale, Université Paris-Saclay, Villejuif, France
| | - Sophie Bockel
- Gustave Roussy, Département de Radiothérapie, INSERM 1030, Université Paris-Saclay, Villejuif, France
| | - Samir Achkar
- Gustave Roussy, Département de Radiothérapie, INSERM 1030, Université Paris-Saclay, Villejuif, France
| | - Mansouria Merad
- Gustave Roussy, Département d'Oncologie Médicale, Université Paris-Saclay, Villejuif, France
| | - Laurent Dercle
- Department of Radiology, Columbia University Irving Medical Center, New York, NY, United States
| | - Sofia Rivera
- Gustave Roussy, Département de Radiothérapie, INSERM 1030, Université Paris-Saclay, Villejuif, France
| | - Cyrus Chargari
- Gustave Roussy, Département de Radiothérapie, INSERM 1030, Université Paris-Saclay, Villejuif, France
| | - Eric Deutsch
- Gustave Roussy, Département de Radiothérapie, INSERM 1030, Université Paris-Saclay, Villejuif, France
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