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 TUMS, 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, Students Res Comm, Sch Med, 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; RESISTANCE PROPERTIES; 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
  • [41] MiniCovid-Unet: CT-Scan Lung Images Segmentation for COVID-19 Identification
    Salazar-Urbina, Alvaro
    Ventura-Molina, Elias
    Yanez-Marquez, Cornelio
    Aldape-Perez, Mario
    Lopez-Yanez, Itzama
    COMPUTACION Y SISTEMAS, 2024, 28 (01): : 75 - 84
  • [42] Pilon fractures: A new classification system based on CT-scan
    Leonetti, Danilo
    Tigani, Domenico
    INJURY-INTERNATIONAL JOURNAL OF THE CARE OF THE INJURED, 2017, 48 (10): : 2311 - 2317
  • [43] Radio-Histological Correlation of Lung Features in Severe COVID-19 Through CT-Scan and Lung Ultrasound Evaluation
    Trias-Sabria, Pere
    Dorca Duch, Eduard
    Molina-Molina, Maria
    Aso, Samantha
    Diez-Ferrer, Marta
    Marin Muniz, Alfredo
    Bordas-Martinez, Jaume
    Sabater, Joan
    Luburich, Patricio
    del Rio, Belen
    Solanich, Xavier
    Dorca, Jordi
    Santos, Salud
    Suarez-Cuartin, Guillermo
    FRONTIERS IN MEDICINE, 2022, 9
  • [44] Deep learning-based important weights-only transfer learning approach for COVID-19 CT-scan classification
    Tejalal Choudhary
    Shubham Gujar
    Anurag Goswami
    Vipul Mishra
    Tapas Badal
    Applied Intelligence, 2023, 53 : 7201 - 7215
  • [45] A Basic Concept of Image Classification for Covid-19 Patients Using Chest CT Scan and Convolutional Neural Network
    Sari, Irma Permata
    Widodo
    Nugraheni, Murien
    Wanda, Putra
    2020 1ST INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY, ADVANCED MECHANICAL AND ELECTRICAL ENGINEERING (ICITAMEE 2020), 2020, : 175 - 178
  • [46] Deep learning-based important weights-only transfer learning approach for COVID-19 CT-scan classification
    Choudhary, Tejalal
    Gujar, Shubham
    Goswami, Anurag
    Mishra, Vipul
    Badal, Tapas
    APPLIED INTELLIGENCE, 2023, 53 (06) : 7201 - 7215
  • [47] Segmentation and classification of lungs CT-scan for detecting COVID-19 abnormalities by deep learning technique: U-Net model
    Moosavi, Abdoulreza S.
    Mahboobi, Ashraf
    Arabzadeh, Farzin
    Ramezani, Nazanin
    Moosavi, Helia S.
    Mehrpoor, Golbarg
    JOURNAL OF FAMILY MEDICINE AND PRIMARY CARE, 2024, 13 (02) : 691 - 698
  • [48] Role of chest CT scan in patients with preexisting cancer and COVID-19 pneumonia
    Khorasanizadeh, Faezeh
    Kaviani, Soori
    Salamroudi, Shadi
    Seyyedsalehi, Monireh Sadat
    Gity, Masoumeh
    Zendehdel, Kazem
    BMC MEDICAL IMAGING, 2023, 23 (01)
  • [49] 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
  • [50] Role of chest CT scan in patients with preexisting cancer and COVID-19 pneumonia
    Faezeh Khorasanizadeh
    Soori Kaviani
    Shadi Salamroudi
    Monireh Sadat Seyyedsalehi
    Masoumeh Gity
    Kazem Zendehdel
    BMC Medical Imaging, 23