Coronavirus Disease 2019 (COVID-19) Candidate Chest CT Features: A Systematic Review of Extracted Imaging Features from 7571 Individuals

  1. Javad Zahiri
  2. Mohammad Hossein Afsharinia
  3. Zhaleh Hekmati
  4. Mohsen Khodarahmi
  5. Shahrzad Hekmati
  6. Ramin Pourghorban

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Since the outbreak of Coronavirus Disease 2019 (COVID-19) causing novel coronavirus (2019-nCoV)-infected pneumonia (NCIP), over 45 million affected cases have been reported worldwide. Many patients with COVID-19 have involvement of their respiratory system. According to studies in the radiology literature, chest computed tomography (CT) is recommended in suspected cases for initial detection, evaluating the disease progression and monitoring the response to therapy. The aim of this article is to review the most frequently reported imaging features in COVID-19 patients in order to provide a reliable insight into expected CT imaging manifestations in patients with positive reverse-transcription polymerase chain reaction (RT-PCR) test results, and also for the initial detection of patients with suspicious clinical presentation whose RT-PCR test results are false negative. A total of 60 out of 173 initial COVID-19 studies, comprising 7571 individuals, were identified by searching PubMed database for articles published between the months of January and June 2020. The data of these studies were related to patients from China, Japan, Italy, USA, Iran and Singapore. Among 40 reported features, presence of ground glass opacities (GGO), consolidation, bilateral lung involvement and peripheral distribution are the most frequently observed ones, reported in 100%, 91.7%, 85%, and 83.3% of articles, respectively. In a similar way, we extracted CT imaging studies of similar pulmonary syndromes outbreaks caused by other strains of coronavirus family: Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS). For MERS and SARS, 2 out of 21 and 5 out of 153 initially retrieved studies had CT findings, respectively. Herein, we have indicated the most common coronavirus family related and COVID-19 specific features. Presence of GGO, consolidation, bilateral lung involvement and peripheral distribution were the features reported in at least 83% of COVID-19 articles, while air bronchogram, multi-lobe involvement and linear opacity were the three potential COVID-19 specific CT imaging findings. This is necessary to recognize the most promising imaging features for diagnosis and follow-up of patients with COVID-19. Furthermore, we identified co-existed CT imaging features.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.11.03.20225326: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Firstly, we performed a search in titles and abstracts of articles stored in Pubmed database using the keywords “COVID-19”,”SARS-CoV-2”,”SARS2” and “severe acute respiratory syndrome coronavirus 2” for COVID-19; “MERS”, “Middle East Respiratory Syndrome”, “MERS-coronavirus” or “MERS-CoV” for MERS; and “severe acute respiratory syndrome”, “SARS”, “SARS-CoV-1” and “SARSr-CoV” for SARS, each together with “chest CT” or “Computed Tomography” in order to obtain articles related to both coronavirus family and CT imaging.
    Pubmed
    suggested: (PubMed, RRID:SCR_004846)

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.11.03.20225326v1 on medRxiv