A Quantitative Lung Computed Tomography Image Feature for Multi-Center Severity Assessment of COVID-19

  1. Biswajoy Ghosh
  2. Nikhil Kumar
  3. Anup K. Sadhu
  4. Nirmalya Ghosh
  5. Pabitra Mitra
  6. Jyotirmoy Chatterjee

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Abstract

The COVID-19 pandemic has affected millions and congested healthcare systems globally. Hence an objective severity assessment is crucial in making therapeutic decisions judiciously. Computed Tomography (CT)-scans can provide demarcating features to identify severity of pneumonia —commonly associated with COVID-19—in the affected lungs. Here, a quantitative severity assessing chest CT image feature is demonstrated for COVID-19 patients. An open-source multi-center Italian database 1 was used, among which 60 cases were incorporated in the study (age 27-86, 71% males) from 27 CT imaging centers. Lesions in the form of opacifications, crazy-paving patterns, and consolidations were segmented. The severity determining feature — L norm was quantified and established to be statistically distinct for the three —mild, moderate, and severe classes (p-value < 0.0001). The thresholds of L norm for a 3-class classification were determined based on the optimum sensitivity/specificity combination from Receiver Operating Characteristic (ROC) analyses. The feature L norm classified the cases in the three severity categories with 86.88% accuracy. ‘Substantial’ to ‘almost-perfect’ intra-rater and inter-rater agreements were achieved involving expert and non-expert based evaluations ( κ -score 0.79-0.97). We trained machine learning based classification models and showed L norm alone has a superior diagnostic accuracy over standard image intensity and texture features. Classification accuracy was further increased when L norm was used for 2-class classification i.e. to delineate the severe cases from non-severe ones with a high sensitivity (97.7%), and specificity (97.49%). Therefore, key highlights of this severity assessment feature are accuracy, lower dependency on expert availability, and wide utility across different imaging centers.

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  1. Version published to 10.1101/2020.07.13.20152231v2 on medRxiv
  2. ScreenIT

    SciScore for 10.1101/2020.07.13.20152231: (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 variableA total of 509 CT and HRCT images of 101 COVID-19 positive individuals were taken between the age of 20 to 90 (60% males).

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Once all the three values (A, B, L) are determined, the Lnorm was calculated by

    All analyses were performed in MATLAB R2019a.

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 11. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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 10.1101/2020.07.13.20152231v1 on medRxiv