|
 
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| REVIEW ARTICLE |
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| Year : 2022 | Volume
: 9
| Issue : 2 | Page : 62-67 |
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Computed tomography-aided diagnosis of COVID-19
Xiao Chen1, Qiuyuan Yang1, Haijun He1, Caiqiong Wang1, Zefei Peng1, Yingchun Liu1, Peiqi Wang1, Jialei Wu2, Bin Yang2
1 Department of Clinical Medicine, Dali University, Dali, China
2 Department of Medical Imaging, Calmette Hospital, The First Hospital of Kunming, Kunming, China
| Date of Submission | 07-Apr-2022 |
| Date of Acceptance | 18-Jun-2022 |
| Date of Web Publication | 8-Nov-2022 |
Correspondence Address:
Bin Yang
Department of Medical Imaging, Calmette Hospital, The First Hospital of Kunming, Kunming Yunnan
China
 Source of Support: None, Conflict of Interest: None
DOI: 10.4103/RID.RID_23_22
Coronavirus disease (COVID-19) is highly infectious, has spread worldwide, and has a relatively high mortality rate. Early diagnosis and timely isolation are essential to control the spread of COVID-19. Computed tomography (CT) is considered to be an effective tool for the rapid diagnosis of COVID-19 and plays a key role in diagnosis, clinical course monitoring, and the evaluation of treatment outcomes. Artificial intelligence (AI) has emerged as a useful technology for early diagnosis, lesion quantification, and prognosis evaluation in patients with COVID-19. In this review, we discuss the role of CT in the diagnosis of COVID-19, typical CT manifestations of COVID-19 throughout the disease course, differential diagnoses, and the application of AI as a diagnostic and therapeutic tool in this patient population.
Keywords: Artificial intelligence, COVID-19, tomography
How to cite this article:
Chen X, Yang Q, He H, Wang C, Peng Z, Liu Y, Wang P, Wu J, Yang B. Computed tomography-aided diagnosis of COVID-19. Radiol Infect Dis 2022;9:62-7 |
How to cite this URL:
Chen X, Yang Q, He H, Wang C, Peng Z, Liu Y, Wang P, Wu J, Yang B. Computed tomography-aided diagnosis of COVID-19. Radiol Infect Dis [serial online] 2022 [cited 2023 Mar 23];9:62-7. Available from: http://www.ridiseases.org/text.asp?2022/9/2/62/360504 |
| Introduction | |  |
In December 2019, an outbreak of pneumonia with unknown etiology was confirmed in Hubei Province, China. This highly contagious disease rapidly spread worldwide[1],[2] and was declared a pandemic by the World Health Organization (WHO) in March 2020.[3],[4] The WHO named this disease coronavirus disease 2019 (COVID-19), announced on February 11, 2020.[1] It is caused by infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).[5] The etiology and clinical manifestations of COVID-19 are similar to those of respiratory syndromes caused by other coronaviruses.[6]
| Epidemiology and Clinical Manifestations of COVID-19 | | |
The mortality rate of COVID-19 is lower than that of Middle East respiratory syndrome and SARS. However, the infectivity and transmissibility rates are significantly higher.[7] Similar to other viruses, repeated evolutionary mutations have resulted in the generation of new SARS-CoV-2 variants with altered biological properties. The WHO has labeled the different SARS-CoV-2 “variants of concern” as alpha, beta, gamma, delta, and omicron. Currently, the omicron variant has replaced the delta variant as the predominant variant. A growing body of evidence shows that the omicron variant is less virulent but significantly more transmissible than delta, and therefore is more contagious.
Patients with COVID-19 are the main source of infection and are contagious during the incubation period of the virus.[8] COVID-19 is primarily transmitted through respiratory droplets and close contact, as well as through aerosols in relatively enclosed environments.[9] Contact with virus-contaminated objects and surfaces has also been implicated in disease transmission.[10] The clinical presentation of COVID-19 commonly includes dry cough, fever, and general fatigue. However, this disease also manifests with nonspecific signs and symptoms. Some patients develop a sore throat, runny nose, stuffy nose, diarrhea, myalgia, or olfactory and taste disorders.[11] The incubation period is typically 1–14 days (commonly 3–7 days).[12]
Clinically, most patients present with mild symptoms without pulmonary manifestations and have a good prognosis. Fully vaccinated patients and those infected with the omicron variant tend to be asymptomatic and present with mild infection. A small number of elderly patients or patients with underlying chronic diseases develop severe clinical manifestations, such as hypoxemia and dyspnea, within a week of infection. Severe infection can progress to acute respiratory distress syndrome, coagulation dysfunction, metabolic acidosis, multiple organ failure, and even death.[13]
| Computed Tomography-Aided Diagnosis of COVID-19 | | |
To date, no definitive treatment has been established for COVID-19. Therefore, the early diagnosis and prompt isolation of infected patients are important for controlling the spread of the disease. Reverse transcription-polymerase chain reaction (RT-PCR) is the standard method for identifying SARS-CoV-2 infection.[14],[15] However, the disease course, sampling quality, and viral load are known to affect the RT-PCR test results. Therefore, RT-PCR has low sensitivity for virus detection, with high false-negative rates. In particular, the patient's first RT-PCR test for SARS-CoV-2 can yield false-negative results.[16],[17] Therefore, negative RT-PCR test results do not exclude the possibility of SARS-CoV-2 infection.[18],[19],[20] Moreover, sampling delays, the unstable performance of test kits, and specific laboratory requirements can result in delayed RT-PCR test results, leading to delayed diagnosis and increasing the risk of continued virus transmission.[16],[17]
Medical imaging is a useful component of patient evaluation for the rapid and effective diagnosis of COVID-19, with a sensitivity of >90%. Notably, chest computed tomography (CT) plays a key role in the diagnosis and treatment of COVID-19, as well as in monitoring disease progression and treatment outcomes.[21],[22] The sensitivity of CT is higher than that of RT-PCR, although its specificity is relatively low.[23] Therefore, a combination of CT with other evaluation methods (such as RT-PCR) is necessary to improve the accuracy of COVID-19 diagnosis.
| Computed Tomographic Features of COVID-19 | | |
Chest CT findings in patients with COVID-19 typically include bilateral and multifocal peripheral and subpleural ground-glass opacities (GGO).[24],[25],[26] GGO represents the most common imaging finding in COVID-19, with an incidence of 98%[27],[28] and is often accompanied by grid-like changes, air-bronchial signs, interlobular septal thickening, and consolidation.[25] Pulmonary consolidation, which is a typical imaging finding in COVID-19,[28] is usually multifocal, flaky, or segmental, and it tends to occur subpleurally or adjacent to the bronchovascular bundle with an incidence of 2%–64%.[29],[30] Consolidation can be attributable to fibrous mucinous exudates.[28] The paving stone sign refers to the CT appearance of interlobular septal thickening superimposed on a background of GGO.[31],[32] and indicates disease progression or the peak period of the disease.[33] Critically ill patients may show areas of bilateral multifocal consolidation, some of which tend to coalesce into sheets or may be accompanied by pleural effusion; some COVID-19 patients can even show “white lungs.” Pleural effusion and lymphadenopathy are rare manifestations of COVID-19.[34],[35]
Most pulmonary lesions in patients infected with the delta variant have been detected in the lower lobes, subpleural areas and/or the lung periphery, with a few lesions spreading over the mid-lung fields and along the bronchovascular bundles.[36] Studies have found that patients infected with the omicron variant are usually asymptomatic or have mild symptoms, and some patients do not show any CT abnormalities. Imaging findings and changes in these patients are not completely consistent with those observed following infection with previous variants. Further data are needed to verify these differences.[37]
| Computed Tomography-Based Evolution of COVID-19 | | |
COVID-19 shows the characteristic of CT findings throughout the disease course. Therefore, regular CT evaluation can be used to monitor disease progression and ensure timely and effective treatment. Based on the scope and type of lesions, the CT findings in patients with COVID-19 can be categorized as those observed during the early stage (days 0–4), progressive stage (days 5–8), severe stage (days 9–13), and absorption period (after day 14). Lung involvement usually peaks on the 10th day of infection and gradually improves after 14 days.[38]
CT findings in early-stage COVID-19 are characterized by localized unilateral or bilateral pulmonary inflammatory infiltrates, which may be subpleural, segmental, or subsegmental in distribution. There may also be lumps, patchy GGO, or vascular congestion and thickening. Pulmonary nodules, consolidation, GGO, interstitial changes, and interlobular septal thickening are relatively rare. Progressive COVID-19 tends to present with gradual bilateral multilobar involvement with an increase in the number and scope of lesions on CT and consolidation in some lesions. Patients may also show consolidation concomitant with GGO, as well as the paving stone sign, fibrosis, and signs of bronchial inflation. Severe COVID-19 typically presents as diffuse bilateral lesions, consolidation, and GGO concomitant with the paving stone sign, and pulmonary fibrous stripes. The severe form of diffuse bilateral disease presents as white lung, and most areas of the lungs show consolidation or GGO. Signs of bronchial inflation, vascular invasion, and thoracic cavity lesions are also observed in some severe cases. However, lymph node enlargement is rare. In most patients, lesions are absorbed and improve gradually during the remission stage. Most studies have reported fibrous lesions.[39],[40] The foci of mild pneumonia disappear completely in some patients, while in many others, particularly those with severe disease, reticular fibrosis can develop after 1 month or more following resorption and involution of the lesions.[41]
| Differential Diagnosis of COVID-19 | | |
A diagnosis of COVID-19 is based on a comprehensive assessment of the epidemiological history, clinical manifestations, and laboratory and imaging findings. Chest CT findings in patients with COVID-19 have some typical characteristics, but these are nonspecific. In addition to recognizing the characteristics of chest CT in patients with COVID-19, radiologists must be able to distinguish between COVID-19 and other types of viral and nonviral pneumonia. Similar to COVID-19, most patients with viral pneumonia show multilobe involvement with a propensity for the posterior and peripheral lung segments. Respiratory syncytial virus pneumonia mostly occurs in infants and young children and can also be seen in patients with congenital defects, immunosuppression, or chronic lung disease. It is characterized by asymmetrically distributed central lobular nodules, distinguishing it from COVID-19. Respiratory syncytial virus pneumonia can also show bilateral asymmetrically distributed GGO, consolidation, and bronchial wall thickening. The imaging findings of human parainfluenza virus pneumonia are diverse and include multiple small nodules around the bronchovascular bundles, GGO, and solids with inflatable bronchial signs. These images can also show centrilobular nodules and thickening of the bronchial wall, which differs from the characteristic subpleural distribution of COVID-19. Adenovirus pneumonia manifests as patchy GGO, and in severe cases, the entire lung segment or lobe is involved. The lesions are usually bilateral and multifocal in distribution and have a high density and blurred edges.[42],[43],[44] Human rhinovirus pneumonia presents as multiple GGO with unclear margins and thickening of the interlobular septa in both lungs. Cytomegalovirus pneumonia more often occurs in immunocompromised patients, such as those who have undergone organ transplantation or hematopoietic stem cell transplantation. This pneumonia type presents as multiple patchy GGO and tiny nodules in both lungs. In some patients, consolidation can be seen in both lower lungs, which is often accompanied by interlobular septal thickening, bronchiectasis, pleural effusion, and pleural hypertrophy, but this presentation is less common in COVID-19. Herpes zoster virus pneumonia is more common in patients with lymphomas and immunodeficiencies. It presents with multiple nodules, GGO and halo signs in both lungs, and the nodules coalesce. Multiple pinpoint-like calcifications are scattered throughout the lungs, and pleural effusion is rare.[45],[46] Influenza A (H1N1) virus pneumonia primarily presents with unilateral or bilateral GGO, with or without consolidation, distributed along the bronchovascular bundles or distributed subpleurally with thickening of the interlobular septa. Pulmonary fibrosis can appear as subpleural reticular changes as the disease progresses.[47] Avian influenza (H7N9) virus pneumonia usually presents with solitary, multiple or diffuse GGO, which may be accompanied by consolidation and inflatable bronchial signs; pleural effusion is common, thus being distinguishable from COVID-19 to a certain extent.[48]. The imaging findings in patients with COVID-19 do not significantly differ from those in patients with SARS, which is also a coronavirus-associated syndrome. In the early stages, SARS usually presents as small pieces of intrapulmonary GGO that are more common in the lower and peripheral lungs. In the progressive stage, GGO can be consolidated or grid shadows can appear, and the interlobular septum can become thickened. The lesions progress and change rapidly. The number of affected lobes also increases rapidly; the extent can also increase and progress to multiple lobes involving both lungs. Limited progression is seen in the absorption period as the density begins to decrease and the range gradually narrows, but some patients still have some manifestations of pulmonary interstitial fibrosis.[49]
COVID-19 should be distinguished from nonviral and noninfectious pulmonary lesions. Mycoplasma pneumonia is more common in children and adolescents and typically shows as central lobular nodules, GGO, consolidation, the “tree bud sign” and lymphadenopathy; these features are more common in Mycoplasma pneumonia than in COVID-19. Symptoms and imaging manifestations are often not concordant with the signs.[50] Consolidation foci, centrilobular nodules, the tree bud sign, and leukocytosis are more common in patients with bacterial pneumonia than in those with COVID-19. However, the incidences of GGO, grid shadows, and the paving stone sign are lower in patients with bacterial pneumonia. Cryptogenic organized pneumonia is characterized by bilateral subpleural patchy GGO, consolidation, and bronchial inflation signs. Some patients show central GGO, marginal ring, or crescentic consolidation, the “reversed halo sign,” features of wandering pneumonia, rare pleural effusion, and lymphadenopathy.[51] Patients with acute eosinophilic pneumonia often show diffuse GGO and micronodular infiltration and (rarely) pleural effusion. Laboratory investigations can reveal significant eosinophilia.[52]
The CT findings of COVID-19 patients, particularly pulmonary edema and hemorrhage, can mimic those observed in other pulmonary diseases. On CT, pulmonary edema presents as centrally distributed GGO and is often accompanied by interlobular septal thickening, pleural effusion, and cardiomegaly. Diffuse pulmonary hemorrhage is characterized by patchy or diffuse GGO, often accompanied by consolidation or centrilobular shadows with indistinct boundaries.[53]
| Application of Artificial Intelligence for COVID-19 Diagnosis | | |
As imaging, especially CT examination, plays an important role in the screening, diagnosis, and effective evaluation of COVID-19, the surge in imaging demand has greatly increased the pressure and challenges for radiologists. Therefore, radiologists and scientists have explored using artificial intelligence (AI) technology to assist in the diagnosis and prognosis of COVID-19. Improvements have been made in intelligent segmentation, lesion detection, image analysis, and prognosis evaluation. AI models can assist radiologists in making rapid diagnoses by detecting lesions and preliminarily identifying common community-acquired pneumonia and other viral pneumonia. The area under the receiver operating characteristic curve of the differential diagnosis of some models has reached 0.95, and the sensitivity, specificity, and accuracy are >90%.[54],[55],[56] Most models can detect and quantify COVID-19. Compared with pure AI diagnosis, AI-assisted radiologists have a higher diagnostic efficiency. In terms of disease and efficacy assessment, the current AI models can accurately calculate the density of lesions and the volume ratio of lesions in the lungs. They can also highlight changes in the disease during follow-up and evaluate the treatment efficacy and help determine patient prognosis. AI models can also monitor disease progression and detect the evolution of lesions.[57] AI has emerged as a promising strategy for the accurate marking, segmentation, and quantitative analysis of COVID-19 lesions. However, further research is warranted to determine the exact role of AI in COVID-19 diagnosis and optimal patient management.
| Summary and Future Perspectives | | |
RT-PCR is the gold standard for the clinical diagnosis of COVID-19. However, compared with RT-PCR, CT is a more sensitive tool. CT images from patients with COVID-19 mainly show bilateral multifocal GGO. Currently, chest CT is the most effective method for the rapid diagnosis of COVID-19 in clinical practice and plays an important role in disease diagnosis, monitoring the disease course, and evaluating the treatment outcomes. AI technology is increasingly recognized as a useful strategy for early diagnosis, lesion quantification, and prognosis evaluation. The standardization of AI for the diagnosis and treatment of COVID-19 will aid in the rapid diagnosis and prognosis evaluation of patients with COVID-19.
Acknowledgments
We would like to thank Katherine Thieltges from Liwen Bianji (Edanz) (www.liwenbianji.cn/) for editing the English text of a draft of this manuscript.
Financial support and sponsorship
This work was supported by the program for Cultivating Reserve Talents in Medical Disciplines from the Health Committee of Yunnan Province(H-2018008 for B. Y). And the National Natural Science Foundation of China (NSFC) (82160348 for Y.B).
Conflicts of interest
There are no conflicts of interest.
| References | | |
| 1. | Zhu N, Zhang D, Wang W, Li X, Yang B, Song J, et al. A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med 2020;382:727-33.  |
| 2. | Farias LP, Fonseca EK, Strabelli DG, Loureiro BM, Neves YC, Rodrigues TP, et al. Imaging findings in COVID-19 pneumonia. Clinics (Sao Paulo) 2020;75:e2027. |
| 3. | Tian S, Hu N, Lou J, Chen K, Kang X, Xiang Z, et al. Characteristics of COVID-19 infection in Beijing. J Infect 2020;80:401-6. |
| 4. | Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: A single-centered, retrospective, observational study. Lancet Respir Med 2020;8:475-81. |
| 5. | Zu ZY, Jiang MD, Xu PP, Chen W, Ni QQ, Lu GM, et al. Coronavirus disease 2019 (COVID-19): A perspective from China. Radiology 2020;296:E15-25. |
| 6. | Xie M, Chen Q. Insight into 2019 novel coronavirus – An updated interim review and lessons from SARS-CoV and MERS-CoV. Int J Infect Dis 2020;94:119-24. |
| 7. | Chen H, Guo J, Wang C, Luo F, Yu X, Zhang W, et al. Clinical characteristics and intrauterine vertical transmission potential of COVID-19 infection in nine pregnant women: A retrospective review of medical records. Lancet 2020;395:809-15. |
| 8. | Umakanthan S, Sahu P, Ranade AV, Bukelo MM, Rao JS, Abrahao-Machado LF, et al. Origin, transmission, diagnosis and management of coronavirus disease 2019 (COVID-19). Postgrad Med J 2020;96:753-8. |
| 9. | Udugama B, Kadhiresan P, Kozlowski HN, Malekjahani A, Osborne M, Li VY, et al. Diagnosing COVID-19: The disease and tools for detection. ACS Nano 2020;14:3822-35. |
| 10. | Hoehl S, Rabenau H, Berger A, Kortenbusch M, Cinatl J, Bojkova D, et al. Evidence of SARS-CoV-2 infection in returning travelers from Wuhan, China. N Engl J Med 2020;382:1278-80. |
| 11. | Chen N, Zhou M, Dong X, Qu J, Gong F, Han Y, et al. Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan, China: A descriptive study. Lancet 2020;395:507-13. |
| 12. | Hui DS, I Azhar E, Madani TA, Ntoumi F, Kock R, Dar O, et al. The continuing 2019-nCoV epidemic threat of novel coronaviruses to global health – The latest 2019 novel coronavirus outbreak in Wuhan, China. Int J Infect Dis 2020;91:264-6. |
| 13. | National Health Commission of the People's Republic of China. Diagnosis and treatment plan for COVID-19 (trial version 8 revision). Chin J Clin Infect Dis 2021;14:81-8. |
| 14. | Long C, Xu H, Shen Q, Zhang X, Fan B, Wang C, et al. Diagnosis of the coronavirus disease (COVID-19): rRT-PCR or CT? Eur J Radiol 2020;126:108961. |
| 15. | Shi F, Wang J, Shi J, Wu Z, Wang Q, Tang Z, et al. Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE Rev Biomed Eng 2021;14:4-15. |
| 16. | Huang P, Liu T, Huang L, Liu H, Lei M, Xu W, et al. Use of chest CT in combination with negative RT-PCR assay for the 2019 novel coronavirus but high clinical suspicion. Radiology 2020;295:22-3. |
| 17. | Xie X, Zhong Z, Zhao W, Zheng C, Wang F, Liu J. Chest CT for typical coronavirus disease 2019 (COVID-19) pneumonia: Relationship to negative RT-PCR testing. Radiology 2020;296:E41-5. |
| 18. | Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, et al. Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: A report of 1014 cases. Radiology 2020;296:E32-40. |
| 19. | Hao W, Li M. Clinical diagnostic value of CT imaging in COVID-19 with multiple negative RT-PCR testing. Travel Med Infect Dis 2020;34:101627. |
| 20. | Li Y, Yao L, Li J, Chen L, Song Y, Cai Z, et al. Stability issues of RT-PCR testing of SARS-CoV-2 for hospitalized patients clinically diagnosed with COVID-19. J Med Virol 2020;92:903-8. |
| 21. | Kooraki S, Hosseiny M, Myers L, Gholamrezanezhad A. Coronavirus (COVID-19) Outbreak: What the department of radiology should know. J Am Coll Radiol 2020;17:447-51. |
| 22. | Rodriguez-Morales AJ, Cardona-Ospina JA, Gutiérrez-Ocampo E, Villamizar-Peña R, Holguin-Rivera Y, Escalera-Antezana JP, et al. Clinical, laboratory and imaging features of COVID-19: A systematic review and meta-analysis. Travel Med Infect Dis 2020;34:101623. |
| 23. | Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Zhang N, et al. Chest CT findings in coronavirus disease-19 (COVID-19): Relationship to duration of infection. Radiology 2020;295:200463. |
| 24. | Pan Y, Guan H, Zhou S, Wang Y, Li Q, Zhu T, et al. Initial CT findings and temporal changes in patients with the novel coronavirus pneumonia (2019-nCoV): A study of 63 patients in Wuhan, China. Eur Radiol 2020;30:3306-9. |
| 25. | Zhao Y, Liu X, Duan W, Liu Y, Du W, Du Y, et al. A typical computed tomography presentations of coronavirus disease 2019. Radiol Infect Dis 2020;7:130-4. |
| 26. | Ng MY, Lee EY, Yang J, Yang F, Li X, Wang H, et al. Imaging profile of the COVID-19 infection: Radiologic findings and literature review. Radiol Cardiothorac Imaging 2020;2:e200034. |
| 27. | Zheng Y, Wang L, Ben S. Meta-analysis of chest CT features of patients with COVID-19 pneumonia. J Med Virol 2021;93:241-9. |
| 28. | Hansell DM, Bankier AA, MacMahon H, McLoud TC, Müller NL, Remy J. Fleischner society: Glossary of terms for thoracic imaging. Radiology 2008;246:697-722. |
| 29. | Wu J, Wu X, Zeng W, Guo D, Fang Z, Chen L, et al. Chest CT findings in patients with coronavirus disease 2019 and its relationship with clinical features. Invest Radiol 2020;55:257-61. |
| 30. | Li K, Wu J, Wu F, Guo D, Chen L, Fang Z, et al. The clinical and chest CT features associated with severe and critical COVID-19 pneumonia. Invest Radiol 2020;55:327-31. |
| 31. | Fields BK, Demirjian NL, Dadgar H, Gholamrezanezhad A. Imaging of COVID-19: CT, MRI, and PET. Semin Nucl Med 2021;51:312-20. |
| 32. | Wong KT, Antonio GE, Hui DS, Lee N, Yuen EH, Wu A, et al. Thin-section CT of severe acute respiratory syndrome: Evaluation of 73 patients exposed to or with the disease. Radiology 2003;228:395-400. |
| 33. | Pan F, Ye T, Sun P, Gui S, Liang B, Li L, et al. Time course of lung changes at chest CT during recovery from coronavirus disease 2019 (COVID-19). Radiology 2020;295:715-21. |
| 34. | Li H. Guideline for medical imaging in auxiliary diagnosis of coronavirus disease 2019. Chin J Med Imaging Technol 2020;36:321-31. |
| 35. | Shi H, Han X, Jiang N, Cao Y, Alwalid O, Gu J, et al. Radiological findings from 81 patients with COVID-19 pneumonia in Wuhan, China: A descriptive study. Lancet Infect Dis 2020;20:425-34. |
| 36. | Yu C, Yang Y, Hu T, Zhang L, Jiang S, Chen B, et al. High resolution CT findings and clinical features of the novel coronavirus B.1.617.2 variant. Chin J Radiol 2021;55:1054-8. |
| 37. | |
| 38. | Shaw B, Daskareh M, Gholamrezanezhad A. The lingering manifestations of COVID-19 during and after convalescence: Update on long-term pulmonary consequences of coronavirus disease 2019 (COVID-19). Radiol Med 2021;126:40-6. |
| 39. | Committee of the Infectious Diseases Radiology Group of Chinese Society of Radiology; Committee of the Infectious Diseases Radiology Section of Chinese Medical Doctor Association; Infectious Disease Imaging Group, Infectious Disease Branch, Chinese Research Hospital Association; Imaging Committee of Chinese Association for the Prevention and Treatment of STD AIDS Infection (Infectious Disease); Infectious Diseases Group, General Radiological Equipment Committee, China Association of Medical Equipment; Beijing Imaging Diagnosis and Treatment Technology Innovation Alliance. Guideline for imaging diagnosis of novel coronavirus (2019nCoV) infected pneumonia (1 st Edition 2020). New Med 2020;30:2234. |
| 40. | Jin YH, Cai L, Cheng ZS, Cheng H, Deng T, Fan YP, et al. A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus (2019-nCoV) infected pneumonia (standard version). Mil Med Res 2020;7:4. |
| 41. | Ding X, Xu J, Zhou J, Long Q. Chest CT findings of COVID-19 pneumonia by duration of symptoms. Eur J Radiol 2020;127:109009. |
| 42. | Bai HX, Hsieh B, Xiong Z, Halsey K, Choi JW, Tran TML, et al. Performance of radiologists in differentiating COVID-19 from non-COVID-19 viral pneumonia at chest CT. Radiology 2020;296:E46-54. |
| 43. | Li Y, Xia L. Coronavirus disease 2019 (COVID-19): Role of chest CT in diagnosis and management. AJR Am J Roentgenol 2020;214:1280-6. |
| 44. | Dai WC, Zhang HW, Yu J, Xu HJ, Chen H, Luo SP, et al. CT imaging and differential diagnosis of COVID-19. Can Assoc Radiol J 2020;71:195-200. |
| 45. | Song L, Zeng YT, Gong XM, Zhang XC, Lu ZS. Imaging manifestations and differential diagnosis of novel coronavirus pneumonia. Electron J Emerg Infect Dis 2020;5:83-6. |
| 46. | Chen Z, Wang RF. Application of CT in the diagnosis and differential diagnosis of novel coronavirus pneumonia. CT Theory Appl 2020;29:273-9. |
| 47. | Koo HJ, Lim S, Choe J, Choi SH, Sung H, Do KH. Radiographic and CT features of viral pneumonia. Radiographics 2018;38:719-39. |
| 48. | Castañer E, Alguersuari A, Gallardo X, Andreu M, Pallardó Y, Mata JM, et al. When to suspect pulmonary vasculitis: Radiologic and clinical clues. Radiographics 2010;30:33-53. |
| 49. | Paul NS, Roberts H, Butany J, Chung T, Gold W, Mehta S, et al. Radiologic pattern of disease in patients with severe acute respiratory syndrome: The toronto experience. Radiographics 2004;24:553-63. |
| 50. | Kumar S. Mycoplasma pneumoniae: A significant but underrated pathogen in paediatric community-acquired lower respiratory tract infections. Indian J Med Res 2018;147:23-31. [ PUBMED] [Full text] |
| 51. | Wang Q, Zhang Z, Shi Y, Jiang Y. Emerging H7N9 influenza a (novel reassortant avian-origin) pneumonia: Radiologic findings. Radiology 2013;268:882-9. |
| 52. | Elicker BM, Jones KT, Naeger DM, Frank JA. Imaging of acute lung injury. Radiol Clin North Am 2016;54:1119-32. |
| 53. | Saba L, Sverzellati N. Is COVID evolution due to occurrence of pulmonary vascular thrombosis? J Thorac Imaging 2020;35:344-5. |
| 54. | Chen J, Wu L, Zhang J, Zhang L, Gong D, Zhao Y, et al. Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography. Sci Rep 2020;10:19196. |
| 55. | Li L, Qin L, Xu Z, Yin Y, Wang X, Kong B, et al. Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: Evaluation of the diagnostic accuracy. Radiology 2020;296:E65-71. |
| 56. | Jin C, Chen W, Cao Y, Xu Z, Tan Z, Zhang X, et al. Development and evaluation of an artificial intelligence system for COVID-19 diagnosis. Nat Commun 2020;11:5088. |
| 57. | Shi W, Peng X, Liu T, Cheng Z, Lu H, Yang S, et al. A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: A retrospective study of 196 patients. Ann Transl Med 2021;9:216. |
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