Evaluation of chest CT-scan appearances of COVID-19 according to RSNA classification system

被引:1
|
作者
Arian, Arvin [1 ]
Gity, Masoumeh [1 ]
Kolahi, Shahriar [2 ]
Khani, Sina [3 ]
Ahmadi, Mehran Arab [2 ]
Salehi, Mohammadreza [4 ]
Delazar, Sina [2 ]
机构
[1] Univ Tehran Med Sci, Tehran Univ Med Sci, Adv Diagnost & Intervent Radiol Res Ctr ADIR, Dept Radiol, Tehran, Iran
[2] Univ Tehran Med Sci, Adv Diagnost & Intervent Radiol Res Ctr ADIR, Dept Radiol, Tehran, Iran
[3] Shahid Beheshti Univ Med Sci, Sch Med, Students Res Comm, Tehran, Iran
[4] Univ Tehran Med Sci, Dept Infect Dis, Imam Khomeini Hosp Complex, Tehran, Iran
关键词
COVID-19; CT-scan; pneumonia; primary care; DISEASE; 2019; COVID-19; ANTIBIOTIC-RESISTANCE; ABORTED BOVINE; STRAINS; CAPRINE; BUFFALO; OVINE;
D O I
10.4103/jfmpc.jfmpc_8_22
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Background: The Radiologic Society of North America (RSNA) divides patients into four sections: negative, atypical, indeterminate, and typical coronavirus disease 2019 (COVID-19) pneumonia based on their computed tomography (CT) scan findings. Herein, we evaluate the frequency of the chest CT-scan appearances of COVID-19 according to each RSNA categorical group. Methods: A total of 90 patients with real-time reverse transcriptase-polymerase chain reaction (RT-PCR)-confirmed COVID-19 were enrolled in this study and differences in age. sex, cardiac characteristics, and imaging features of lung parenchyma were evaluated in different categories of RSNA classification. Results: According to the RSNA classification 87.8, 5.56, 4.44, and 2.22% of the patients were assigned as typical, indeterminate, atypical. and negative. respectively. The proportion of "atypical" patients was higher in the patients who had mediastinal lymphadenopathy and pleural effusion. Moreover, ground-glass opacity (GGO) and consolidation were more pronounced in the lower lobes and left lung compared to the upper lobes and right lung, respectively. While small nodules were mostly seen in the atypical group, small GGO was associated with the typical group, especially when it is present in the right lung and indeterminate group. Conclusion: Regardless of its location, non-round GGO is the most prevalent finding in the typical group of the RSNA classification systems. Mediastinal lymphadenopathy, pleural effusion, and small nodules are mostly observed in the atypical group and small GGO in the right lung is mostly seen in the typical group.
引用
收藏
页码:4410 / 4416
页数:7
相关论文
共 50 条
  • [31] Chest CT scan features from 302 patients with COVID-19 in Jordan
    Albtoush, Omar M.
    Al-Shdefat, Rawan B.
    Al-Akaileh, Alabed
    EUROPEAN JOURNAL OF RADIOLOGY OPEN, 2020, 7
  • [32] The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs
    Paras Lakhani
    J. Mongan
    C. Singhal
    Q. Zhou
    K. P. Andriole
    W. F. Auffermann
    P. M. Prasanna
    T. X. Pham
    Michael Peterson
    P. J. Bergquist
    T. S. Cook
    S. F. Ferraciolli
    G. C. A. Corradi
    MS Takahashi
    C. S. Workman
    M. Parekh
    S. I. Kamel
    J. Galant
    A. Mas-Sanchez
    E. C. Benítez
    M. Sánchez-Valverde
    L. Jaques
    M. Panadero
    M. Vidal
    M. Culiañez-Casas
    D. Angulo-Gonzalez
    S. G. Langer
    María de la Iglesia-Vayá
    G. Shih
    Journal of Digital Imaging, 2023, 36 : 365 - 372
  • [33] The 2021 SIIM-FISABIO-RSNA Machine Learning COVID-19 Challenge: Annotation and Standard Exam Classification of COVID-19 Chest Radiographs
    Lakhani, Paras
    Mongan, J.
    Singhal, C.
    Zhou, Q.
    Andriole, K. P.
    Auffermann, W. F.
    Prasanna, P. M.
    Pham, T. X.
    Peterson, Michael
    Bergquist, P. J.
    Cook, T. S.
    Ferraciolli, S. F.
    Corradi, G. C. A.
    Takahashi, M. S.
    Workman, C. S.
    Parekh, M.
    Kamel, S., I
    Galant, J.
    Mas-Sanchez, A.
    Benitez, E. C.
    Sanchez-Valverde, M.
    Jaques, L.
    Panadero, M.
    Vidal, M.
    Culianez-Casas, M.
    Angulo-Gonzalez, D.
    Langer, S. G.
    de la Iglesia-Vaya, Maria
    Shih, G.
    JOURNAL OF DIGITAL IMAGING, 2023, 36 (01) : 365 - 372
  • [34] Towards Framework for Edge Computing Assisted COVID-19 Detection using CT-scan Images
    Rohila, Varan Singh
    Gupta, Nitin
    Kaul, Amit
    Ghosh, Uttam
    IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC 2021), 2021,
  • [35] Comparison of chest radiography and chest CT for evaluation of pediatric COVID-19 pneumonia: Does CT add diagnostic value?
    Das, Karuna M.
    Alkoteesh, Jamal A.
    Al Kaabi, Jumaa
    Al Mansoori, Taleb
    Winant, Abbey J.
    Singh, Rajvir
    Paraswani, Rajesh
    Syed, Rizwan
    Sharif, Elsadeg M.
    Balhaj, Ghazala B.
    Lee, Edward Y.
    PEDIATRIC PULMONOLOGY, 2021, 56 (06) : 1409 - 1418
  • [36] A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images
    Ahamed, Khabir Uddin
    Islam, Manowarul
    Uddin, Ashraf
    Akhter, Arnisha
    Paul, Bikash Kumar
    Abu Yousuf, Mohammad
    Uddin, Shahadat
    Quinn, Julian M. W.
    Moni, Mohammad Ali
    COMPUTERS IN BIOLOGY AND MEDICINE, 2021, 139
  • [37] Two-Parallel-Step CNN Framework for Detection of COVID-19 Based on Segmented CT-Scan and Chest X-Ray Images
    Bahroun, Sahbi
    VIETNAM JOURNAL OF COMPUTER SCIENCE, 2025,
  • [38] RSNA Expert Consensus Statement on Reporting Chest CT Findings Related to COVID-19: Interobserver Agreement Between Chest Radiologists
    Byrne, Danielle
    Neill, Siobhan B. O'
    Muller, Nestor L.
    Muller, C. Isabela Silva
    Walsh, John P.
    Jalal, Sabeena
    Parker, William
    Bilawich, Ana-Maria
    Nicolaou, Savvas
    CANADIAN ASSOCIATION OF RADIOLOGISTS JOURNAL-JOURNAL DE L ASSOCIATION CANADIENNE DES RADIOLOGISTES, 2021, 72 (01): : 159 - 166
  • [39] CT-scan findings of COVID-19 pneumonia based on the time elapsed from the beginning of symptoms to the CT imaging evaluation: a descriptive study in Iran
    Jafari, Sirous
    Tabary, Mohammadreza
    Eshraghi, Sahereh
    Araghi, Farnaz
    Aryannejad, Armin
    Mohammadnejad, Esmaeil
    Rasoolinejad, Mehrnaz
    Hajiabdolbaghi, Mahboubeh
    Koochak, Hamid Emadi
    Ahmadinejad, Zahra
    Abbasian, Ladan
    Manshadi, Seyed Ali Dehghan
    Salehi, Mohammadreza
    Khalili, Hossein
    Yazdi, Niloofar Ayoobi
    Seifi, Arash
    ROMANIAN JOURNAL OF INTERNAL MEDICINE, 2020, 58 (04) : 242 - 250
  • [40] ET-NET: an ensemble of transfer learning models for prediction of COVID-19 infection through chest CT-scan images
    Kundu, Rohit
    Singh, Pawan Kumar
    Ferrara, Massimiliano
    Ahmadian, Ali
    Sarkar, Ram
    MULTIMEDIA TOOLS AND APPLICATIONS, 2022, 81 (01) : 31 - 50