RSNA konsensüsünün değerlendirilmesi: BT ile COVID-19’un tanısı için yeterli mi?

Year 2023, Volume: 48 Issue: 2, 593 - 600, 02.07.2023

Sinan Sözütok , Ömer Kaya , Okan Dılek , Cenk Parlatan , Nazli Nida Kaya , Ferhat Can Pişkin , Sevgül Köse

https://doi.org/10.17826/cumj.1283652

Abstract

Amaç: Koronavirüs hastalığı 19 (COVID-19) Kuzey Amerika Radyoloji Derneği (RSNA) Konsensüsü, toraks bilgisayarlı tomografide (BT) akciğer tutulumunu değerlendirmek ve ortak bir raporlama dili oluşturmak için geliştirilmiştir. Bu çalışmanın amacı, COVID-19'lu hastalarda cinsiyet ve yaş gruplarında BT bulgularının sıklığını belirlemek, bulguları RSNA Konsensüs sınıflamalarına göre karşılaştırmak ve sınıflamalar ile bulguların uyumluluğunu değerlendirmektir.
Gereç ve Yöntem: COVID-19'lu 281 hastanın toraks BT görüntüleri değerlendirildi. Hastalar uygun RSNA konsensüs sınıfında not edildi. Hastaların verileri yaş ve cinsiyet gruplarına göre analiz edildi.
Bulgular: Sık bulgular arasında buzlu cam opasiteleri, konsolidasyon ve hava bronkogramı vardı. Yaygın tutulum paternleri şu şekildeydi: bilateral, periferal ve multifokal. RSNA konsensüsüne göre tipik, atipik ve belirsiz sınıflandırma oranları sırasıyla %63,6, %9,6 ve %27,0 idi. Subplevral fibrotik çizgilenmeler erkeklerde daha sıktı. Hava bronkogramı, lenfadenopati, plevral efüzyon, subplevral fibrotik çizgilenmeler, bilateral tutulum ve tipik sınıflandırma 65 yaş üstü grupta daha sıktı.
Sonuç: Tipik görünüm sınıflandırması bulgularla tutarlı sonuçlara sahipken, belirsiz ve atipik görünüm olarak belirtilen sınıflandırmaların bulgularla yeterli bir uyum göstermediği ve doğru tanısal yönlendirmeler için revizyona ihtiyaç olduğunu düşünmekteyiz.

Keywords

COVID-19, BT özellikleri, yaş, cinsiyet, RSNA konsensüsü

Evaluation of the RSNA consensus: is it sufficient for the diagnosis of COVID-19 with CT?

Abstract

Purpose: Radiological Society of North America (RSNA) Consensus for coronavirus disease 19 (COVID-19) is developed to evaluate the lung involvement on chest computed tomography (CT) and create a common reporting lexicon. Aim of this study is to determine the frequency of CT features in sex and age groups in patients with COVID-19, compare the findings according to the RSNA consensus classifications, and evaluate the compatibility of the classifications and findings.
Materials and Methods: Chest CT images of 281 patients with COVID-19 were evaluated. Patients were noted in the appropriate RSNA consensus class. The patients’ data were analyzed by group according to age and sex.
Results: The main findings included ground-glass opacity, consolidation, and air bronchogram. The common involvement patterns were as follows: bilateral, peripheral, and multifocal. The rates for the typical, atypical, and indeterminate classifications, according to the RSNA consensus, were 63.6%, 9.6%, and 27.0%, respectively. Subpleural fibrous streaking was more frequent in males. Air bronchogram, lymphadenopathy, pleural effusion, subpleural fibrous streaking, bilateral involvement, and a typical classification on CT features were more frequent in the ≥ 65-year age group.
Conclusion: While the typical appearance classification has results consistent with the findings, we think that the classifications specified as indeterminate and atypical appearance do not show sufficient agreement with the findings and revision is needed for correct diagnostic guidance.

Keywords

COVID-19, CT features, age, sex, RSNA consensus

References

• Ufuk F, Savas R. Chest CT features of the novel coronavirus disease (COVID-19). Turk J Med Sci. 2020;50:664-78.
• 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.
• Simpson S, Kay FU, Abbara S, Bhalla S, Chung JH, Chung M 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 - secondary publication. J Thorac Imaging. 2020;35:219-27.
• Dilek O, Kaya O, Akkaya H, Ceylan C, Kızıloglu A, Sahin Eker B et al. Diagnostic performance and interobserver agreement of CO-RADS: evaluation of classification in radiology practice. Diagn Interv Radiol. 2021;5:615-20.
• Salehi S, Abedi A, Balakrishnan S, Gholamrezanezhad A. Coronavirus disease 2019 (COVID-19): a systematic review of imaging findings in 919 Patients. AJR Am J Roentgenol. 2020;14:1-7.
• Rocha CO, Prioste TAD, Faccin CS, Folador L, Tonettoa MS, Knijnik PG et al. Diagnostic performance of the RSNA-proposed classification for COVID-19 pneumonia versus pre-pandemic controls. Braz J Infect Dis. 2022;26:101665.
• Ai T, Yang Z, Hou H, Zhan H, 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;296:32-40.
• Yu M, Xu D, Lan L, Tu M, Liao R, Cai S et al. Thin-section chest CT imaging of coronavirus disease 2019 pneumonia: comparison between patients with mild and severe disease. Radiol Cardiothorac Imaging. 2020;23;2:e200126.
• Chen H, Ai L, Lu H et al. Clinical and imaging features of COVID-19. Radiol Infect Dis 2020;7:43-50.
• Hani C, Trieu NH, Saab I, Dangeard S, Bennani S, Chassagnon G et al. COVID-19 pneumonia: A review of typical CT findings and differential diagnosis. Diagn Interv Imaging. 2020;101:263-8.
• Bao C, Liu X, Zhang H, Li Y, Liu J. Coronavirus Disease 2019 (COVID-19) CT Findings: A systematic review and meta-analysis. J Am Coll Radiol. 2020;17:701-9.
• 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.
• Wang Y, Dong C, Hu Y, Li C, Ren Q, Zhang X et al. Temporal changes of CT Findings in 90 patients with COVID-19 pneumonia: A longitudinal study. Radiology. 2020;296:55-64.
• Fu F, Lou J, Xi D, Bai Y, Ma G, Zhao B et al. Chest computed tomography findings of coronavirus disease 2019 (COVID-19) pneumonia. Eur J Radiol. 2020;30:5489-98.
• Zhou S, Wang Y, Zhu T, Xia L. CT features of coronavirus disease 2019 (COVID-19) pneumonia in 62 patients in Wuhan, China. AJR Am J Roentgenol. 2020;214:1287-94.
• Yang W, Cao Q, Qin L, Wang X, Cheng Z, Pan A et al. Clinical characteristics and imaging manifestations of the 2019 novel coronavirus disease (COVID-19): a multi-center study in Wenzhou city, Zhejiang, China. J Infect. 2020;80:388-93.
• Cheng Z, Lu Y, Cao Q, Qin L, Pan Z, Yan F et al. Clinical features and chest CT manifestations of coronavirus disease 2019 (COVID-19) in a single-center study in Shanghai, China. AJR Am J Roentgenol. 2020;215:121-6.
• Caruso D, Zerunian M, Polici M, Pucciarelli F, Polidori T, Rucci C et al. Chest CT features of COVID-19 in Rome, Italy. Radiology. 2020;296:79-85.
• Shang Y, Xu C, Jiang F, Huang R, Li Y, Zhou Y et al. Clinical characteristics and changes of chest CT features in 307 patients with common COVID-19 pneumonia infected SARS-CoV-2: A multicenter study in Jiangsu, China. Int J Infect Dis. 2020;96:157-62.
• Fan N, Fan W, Li Z, Shi M, Liang Y. Imaging characteristics of initial chest computed tomography and clinical manifestations of patients with COVID-19 pneumonia. Jpn J Radiol. 2020;21:1-6.
• Song F, Shi N, Shan F, Zhang Z, Shen J, Lu H et al. Emerging 2019 novel coronavirus (2019-nCoV) pneumonia. Radiology. 2020;295:210-7.
• Chen Z, Fan H, Cai J, Li Y, Wu B, Hou Y et al. High-resolution computed tomography manifestations of COVID-19 infections in patients of different ages. Eur J Radiol. 2020;126:108972.
• Liu K, Chen Y, Lin R, Han K. Clinical features of COVID-19 in elderly patients: A comparison with young and middle-aged patients. J Infect. 2020;80:14-8.