medigraphic.com

2020, Number 5

<< Back Next >>

Rev Fac Med UNAM 2020; 63 (5)

SARS-CoV-2 Infection (COVID 19) and its Imaging Findings

Muñoz-Jarillo NY, Arenal-Serna J, Muñoz-Jarillo R, Camacho-ZE

Language: Spanish
References: 24
Page: 18-25
PDF size: 607.69 Kb.


ABSTRACT

Due to the emergence of the pandemic caused by the SARSCoV- 2 virus (coronavirus disease or COVID-19) the generalities since its emergence, pathophysiology and clinical picture, as well as the findings observed in imaging methods such as x-ray, tomography and ultrasound should be disseminated and known to all health personnel involved in the diagnosis and treatment of patients. This article is an overview of the clinical and radiological characteristics observed in the infection, the limitations of the different imaging methods, as well as their correlation with the time of evolution of the disease. Additionally, reference is made to the use of artificial intelligence in radiology for the diagnosis of COVID-19.


REFERENCES

  1. Yang R, Li X, Huan L, et al. Chest CT Severity Score: An Imaging Tool for Assessing Severe COVID-19. RSNA, Radiology: Cardiothoracic Imaging. 2020;2(2). Disponible en: https://pubs.rsna.org/doi/full/10.1148/ryct.2020200047

  2. Zhao X, Liu B, Yu Y, et al. COVID-19 patient with initially negative RT-PCR. ESR [Internet] 2020 [Consultado 2020]. Disponible en: https://www.eurorad.org/case/16631

  3. Kooraki S, Hosseiny M, Myers L. Coronavirus (COVID-19) Outbreak: What the Department of Radiology Should Know. JACR. 2020;4:447-51.

  4. Guo YR, Cao QD, Hong ZS, Tan YY, Chen SD, Jin H-J, et al. The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak – an update on the status. Mil Med Res. 2020;7(1):11.

  5. Tran J, Glavis-Bloom J, Bryan T. COVID-19 patient presenting with initial gastrointestinal symtoms. ESR [Internet] 2020 [Consultado 2020]. Disponible en: https://www. eurorad.org/case/16654

  6. Pan Y, Guan H. Imaging changes in patients with 2019- nCov. Eur Radiol [Internet]. 2020 [Consultado 2020]. Disponible en: https://link.springer.com/article/10.1007/ s00330-020-06713-z#citeas

  7. Wong HYF, Lam HYS, Fong AH-T, et al. Frequency and Distribution of Chest Radiographic Findings in COVID-19 Positive Patients. RSNA, Radiology [Internet]. 2019 [Consultado 2020]. Disponible en: https://pubs.rsna.org/doi/ pdf/10.1148/radiol.2020201160

  8. Akl E, Blazic I, Yaacoub S, et al. Use of Chest Imaging in the Diagnosis and Management Of COVID-19: a WHO Rapid Advice Guide. Radiology. [Internet]. 2020 [consultado 2020]. Disponible en: https://doi.org/10.1148/ radiol.2020203173

  9. Ng M-Y, Lee EYP, Yang J, et al. Imaging Profile of the COVID-19 Infection: Radiologic Findings and Literature Review. RSNA, Radiology: Cardiothoracic Imaging. 2020;2(1):e200034.

  10. Bandirali M, Sconfienza LM, Serra R, et al. Chest X-ray findings in asymptomatic and minimally symptomatic quarantined patients in Codogno, Italy. RSNA, Radiology [Internet] 2020 [Consultado 2020]; 201102. Disponible en: https://pubs.rsna.org/doi/10.1148/radiol.2020201102

  11. BSTI. [Internet]. COVID-19 BSTI Reporting Templates and Codes. 22/05/2020. [Consultado 2020]. Disponible en: https://www.bsti.org.uk/covid-19-resources/covid-19- bsti-reporting-templates

  12. Simpson S, Kay FU, Abbara S, et al. Radiological Society of North America Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19. Endorsed by the Society of Thoracic Radiology, the American College of Radiology, and RSNA. RSNA, Radiology: Cardiothoracic Imaging. 2020;2(2):e200152.

  13. Li X, Zeng X, Liu B, et al. COVID-19 Infection Presenting with CT Halo Sign. RSNA, Radiology: Cardiothoracic Imaging. 2020;2(1):e200026.

  14. Wu Y, Xie Y-l, Wang X. Longitudinal CT Findings in COVID-19 Pneumonia: Case Presenting Organizing Pneumonia Pattern. RSNA, Radiology: Cardiothoracic Imaging. 2020;2(1):e200031.

  15. Prokop M, Van Everdingen W, Van Rees Vellinga T, et al. CO-RADS: A Categorical CT Assessment Scheme for Patients Suspected of Having COVID-19 Definition and Evaluation. Radiology. [Internet] 2020 [consultado 2020]. 296 (2). Disponible en: https://doi.org/10.1148/ radiol.2020201473

  16. Poyiadji1 N, Shahin G, Noujaim D, et al. COVID-19–associated Acute Hemorrhagic Necrotizing Encephalopathy: CT and MRI Features. RSNA, Radiology. 2020;201187.

  17. Klironomos S, Tzortzakakis A, Kits A, et al. Nervous System Involvement in COVID-19: Results from a Retrospective Consecutive Neuroimaging Cohort. Radiology. [Internet] 2020 [consultado 2020]. Disponible en: https:// doi.org/10.1148/radiol.20202027915

  18. Wong A, Olusanya O, Wilkinson J, et al. Ultrasound in Times of COVID-19. ICU MANAGEMENT & PRACTICE. [Internet] 2020 [consultado 2020]; 20 (1). Disponible en: https://healthmanagement.org/c/icu/issuearticle/ ultrasound-in-times-of-covid-19

  19. Qian-Yi Peng, Xiao-Ting Wang, Li-Na Zhang, et al. Findings of lung ultrasonography of novel corona virus pneumonia during the 2019-2020. ICM Journal - ESICM [Internet] 2020 [consultado 2020];46(5):849-850. Disponible en: https://link.springer.com/content/pdf/10.1007/s00134- 020-05996-6.pdf

  20. Carrillo R, Carillo J, Carrillo L, et al. Patrones ultrasonograficos pulmonares en el enfermo grave. Asociación Mexicana de Medicina Crítica y Terapia Intensiva. [Internet] 2020 [consultado 2020];25(1):24-32. Disponible en: https://www.medigraphic.com/pdfs/medcri/ti-2011/ ti111d.pdf

  21. Soccorsa S, Boccatonda A, Montanari M, et al. Thoracic ultrasound and SARS-COVID-19: a pictorial essay. Journal of Ultrasound [Internet]. 2020 [consultado 2020];23:217-221. Disponible: https://link.springer.com/ content/pdf/10.1007/s40477-020-00458-7.pdf

  22. Huang L, Han R, Ai T, et al. Serial Quantitative Chest CT Assessment of COVID-19: Deep-Learning Approach. RSNA, Radiology: Cardiothoracic Imaging. 2020;2(2): e200075.

  23. Lessmann N, Sánchez C, Beenen L, et al. Automated Assessment of CO-RADS and Chest CT Severity Scores in Patients with Suspected COVID-19 Using Artificial Intelligence. Radiology. [Internet] 2020 [consultado 2020]. Disponible en: https://pubs.rsna.org/doi/pdf/10.1148/ radiol.2020202439

  24. Matthew D. Li, Thumbavanam N, Gidwani M, et al. Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity no Chest Radiographs using Convolutional Siamese Neural Networks. Radiology. [Internet] 2020 [consultado 2020]. Disponible en: https://doi.org/10.1148/ ryai.2020200079




2020     |     www.medigraphic.com