Development and validation of chest CT-based imaging biomarkers for early stage COVID-19 screening

  1. Xiao-Ping Liu
  2. Xu Yang
  3. Miao Xiong
  4. Xuanyu Mao
  5. Xiaoqing Jin
  6. Zhiqiang Li
  7. Shuang Zhou
  8. Hang Chang

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Abstract

Coronavirus Disease 2019 (COVID-19) is currently a global pandemic, and early screening is one of the key factors for COVID-19 control and treatment. Here, we developed and validated chest CT-based imaging biomarkers for COVID-19 patient screening from two independent hospitals with 419 patients. We identified the vasculature-like signals from CT images and found that, compared to healthy and community acquired pneumonia (CAP) patients, COVID-19 patients display a significantly higher abundance of these signals. Furthermore, unsupervised feature learning led to the discovery of clinical-relevant imaging biomarkers from the vasculature-like signals for accurate and sensitive COVID-19 screening that have been double-blindly validated in an independent hospital (sensitivity: 0.941, specificity: 0.920, AUC: 0.971, accuracy 0.931, F1 score: 0.929). Our findings could open a new avenue to assist screening of COVID-19 patients.

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  1. Version published to 10.3389/fpubh.2022.1004117
  2. ScreenIT

    SciScore for 10.1101/2020.05.15.20103473: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Statistical Analysis: The difference in the vasculature-like signals among different groups (COVID-19, CAP and healthy) was assessed by non-parametric test, and the association between signatures and COVID-19 by logistic regression.
    COVID-19
    suggested: None
    Software and Algorithms
    SentencesResources
    Visualization of these imaging biomarkers was created in three-dimensional space using ITK-Snap (version 3.8.0), Python (version 3.7.0), Matplotlib (version 3.1.2), Blender (version 2.82) and Three.js (version r115 on GitHub).
    ITK-Snap
    suggested: (ITK-SNAP, RRID:SCR_002010)
    Python
    suggested: (IPython, RRID:SCR_001658)
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)
    Blender
    suggested: (Blender, RRID:SCR_008606)
    Principle component analysis (PCA) and heatmap cluster analysis were performed in R (version 3.6.1) and MATLAB (version 2012b), respectively.
    MATLAB
    suggested: (MATLAB, RRID:SCR_001622)

    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

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  3. Version published to 10.1101/2020.05.15.20103473v2 on medRxiv
  4. Version published to 10.1101/2020.05.15.20103473v1 on medRxiv