Machine learning-based CT radiomics method for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study

  1. Hongmei Yue
  2. Qian Yu
  3. Chuan Liu
  4. Yifei Huang
  5. Zicheng Jiang
  6. Chuxiao Shao
  7. Hongguang Zhang
  8. Baoyi Ma
  9. Yuancheng Wang
  10. Guanghang Xie
  11. Haijun Zhang
  12. Xiaoguo Li
  13. Ning Kang
  14. Xiangpan Meng
  15. Shan Huang
  16. Dan Xu
  17. Junqiang Lei
  18. Huihong Huang
  19. Jie Yang
  20. Jiansong Ji
  21. Hongqiu Pan
  22. Shengqiang Zou
  23. Shenghong Ju
  24. Xiaolong Qi

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Abstract

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  1. Version published to 10.21037/atm-20-3026
  2. ScreenIT

    SciScore for 10.1101/2020.02.29.20029603: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Study population: The multicenter study was conducted according to principles of the Declaration of Helsinki and approved by all institutional review board.
    Consent: The need for written informed consent from the participants was waived.
    Randomizationnot detected.
    BlindingAll imaging processes were blinded to clinical data.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Images containing lesions were segmented using Python (3.6, https://www.python.org) and 3Dslicer (version 4.10.0; https://www.slicer.org/) with two steps.
    https://www.python.org
    suggested: (CVXOPT - Python Software for Convex Optimization, RRID:SCR_002918)
    Feature selection and model building were implemented with FeAture Explorer (FAE, v0.2.5, https://github.com/salan668/FAE) on Python (3.6.8, https://www.python.org/)).
    Python
    suggested: (IPython, RRID:SCR_001658)
    Test values like areas under the receiver operating characteristic curves (95% confidence interval), sensitivity, specificity was calculated in SPSS and Python. A P-value < 0.05 was considered statistically significant.
    SPSS
    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: 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.

  3. Version published to 10.1101/2020.02.29.20029603 on medRxiv