Ultra-High-Resolution CT Follow-Up in Patients with Imported Early-Stage Coronavirus Disease 2019 (COVID-19) Related Pneumonia

  1. Yu Lin
  2. Shaomao Lv
  3. Jinan Wang
  4. Jianghe Kang
  5. Youbin Zhang
  6. Zhipeng Feng

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Abstract

Background

An ongoing outbreak of mystery pneumonia in Wuhan was caused by coronavirus disease 2019 (COVID-19). The infectious disease has spread globally and become a major threat to public health.

Purpose

We aim to investigate the ultra-high-resolution CT (UHR-CT) findings of imported COVID-19 related pneumonia from the initial diagnosis to early-phase follow-up.

Methods

This retrospective study included confirmed cases with early-stage COVID-19 related pneumonia imported from the epicenter. Initial and early-phase follow-up UHR-CT scans (within 5 days) were reviewed for characterizing the radiological findings. The normalized total volumes of ground-glass opacities (GGOs) and consolidations were calculated and compared during the radiological follow-up by artificial-intelligence-based methods.

Results

Eleven patients (3 males and 8 females, aged 32-74 years) with confirmed COVID-19 were evaluated. Subpleural GGOs with inter/intralobular septal thickening were typical imaging findings. Other diagnostic CT features included distinct margins (8/11, 73%), pleural retraction or thickening (7/11, 64%), intralesional vasodilatation (6/11, 55%). Normalized volumes of pulmonary GGOs ( p =0.003) and consolidations ( p =0.003) significantly increased during the CT follow-up.

Conclusions

The abnormalities of GGOs with peripleural distribution, consolidated areas, septal thickening, pleural involvement and intralesional vasodilatation on UHR-CT indicate the diagnosis of COVID-19. COVID-19 cases could manifest significantly progressed GGOs and consolidations with increased volume during the early-phase CT follow-up.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.31.20048256: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: 2.1 Patients: This study was approved by our institutional review board; informed consent was waived for the retrospective nature.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Quantitative analyses were automatically performed in all cases using an updated artificial intelligence-based image analysis system (Intelligent Evaluation System of Chest CT, Yitu Healthcare, China, https://www.yitutech.com/en).
    Yitu Healthcare
    suggested: None
    2.4 Statistical Analysis: Statistical analyses were performed using Statistical Package for the Social Sciences (IBM Inc., USA).
    Statistical Package for the Social Sciences
    suggested: (SPSS, RRID:SCR_002865)

    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: We detected the following sentences addressing limitations in the study:
    Our study has several limitations. Firstly, our study was based on a short-term follow-up and a small sample from a single center. Furthermore, most COVID-19 cases in our study were imported from the remote endemic center, and lack pediatric population and severe infection. In addition, some patients may receive early medical intervention before the base-line CT scans. Finally, some fibrous lesions, incidental nodules (not related to viral inflammation), motion artifact and intralesional vessels might become confounding factors in the artificial intelligence-based analysis. In summary, UHR-CT imaging patterns of peripleural GGO with interlobular and intralobular septal thickening, pleural retraction and thickening, and intralesional vasodilatation indicate the preliminary diagnosis of COVID-19. Most imported COVID-19 cases manifest marked progressed volume of both GGOs and consolidations during the early-phase CT follow-up. The UHR-CT based artificial intelligence methodology should be further explored for assessing pneumonia development in COVID-19 patients.

    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 10.1101/2020.03.31.20048256v1 on medRxiv