Value of radiomics features from adrenal gland and periadrenal fat CT images predicting COVID-19 progression

  1. Mudan Zhang
  2. Xuntao Yin
  3. Wuchao Li
  4. Yan Zha
  5. Xianchun Zeng
  6. Xiaoyong Zhang
  7. Jingjing Cui
  8. Jie Tian
  9. Rongpin Wang
  10. Chen Liu

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background

Value of radiomics features from the adrenal gland and periadrenal fat CT images for predicting disease progression in patients with COVID-19 has not been studied.

Methods

A total of 1,245 patients (685 moderate and 560 severe patients) were enrolled in a retrospective study. We proposed 3D V-Net to segment adrenal glands in onset CT images automatically, and periadrenal fat was obtained using inflation operation around the adrenal gland. Next, we built a clinical model (CM), three radiomics models (adrenal gland model [AM], periadrenal fat model [PM], and fusion of adrenal gland and periadrenal fat model [FM]), and radiomics nomogram (RN) after radiomics features extracted to predict disease progression in patients with COVID-19.

Results

The auto-segmentation framework yielded a dice value of 0.79 in the training set. CM, AM, PM, FM, and RN obtained AUCs of 0.712, 0.692, 0.763, 0.791, and 0.806, respectively in the training set. FM and RN had better predictive efficacy than CM ( P < 0.0001) in the training set. RN showed that there was no significant difference in the validation set (mean absolute error [MAE] = 0.04) and test set (MAE = 0.075) between predictive and actual results. Decision curve analysis showed that if the threshold probability was more than 0.3 in the validation set or between 0.4 and 0.8 in the test set, it could gain more net benefits using RN than FM and CM.

Conclusion

Radiomics features extracted from the adrenal gland and periadrenal fat CT images may predict progression in patients with COVID-19.

Funding

This study was funded by Science and Technology Foundation of Guizhou Province (QKHZC [2020]4Y002, QKHPTRC [2019]5803), the Guiyang Science and Technology Project (ZKXM [2020]4), Guizhou Science and Technology Department Key Lab. Project (QKF [2017]25), Beijing Medical and Health Foundation (YWJKJJHKYJJ-B20261CS) and the special fund for basic Research Operating Expenses of public welfare research institutes at the central level from Chinese Academy of Medical Sciences (2019PT320003).

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.01.03.21249183: (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

    Software and Algorithms
    SentencesResources
    Radiomics feature selection and models building: Radiomic features were extracted from ROIs on the CT images using a Python package (PyRadiomics V3.0).
    Python
    suggested: (IPython, RRID:SCR_001658)
    We analyzed all data using SPSS for Windows version 26.0 (IBM Corp., Armonk
    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: We detected the following sentences addressing limitations in the study:
    Our work has some limitations. First, we used the chest CT images as data resources. However, taking clinical practicality and radiation to patients into consideration, there is no need to perform another CT scan using professional adrenal glands CT parameters to observe adrenal lesions a little better because CT examination of patients with COVID-19 is mainly to detect and observe pulmonary lesions. Second, different from other studies, we selected the entire organ as ROIs rather than the lesion itself. The periadrenal fat area may not be precise and contain some other tissue that is indistinguishable to the human eye. Although there are some deficiencies, our findings validated the potential for radiomics features extraction from adrenal glands and periadrenal fat CT images to be the indicators of COVID-19 prognosis. However, these need to be validated in large-scale prospective studies.

    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 10.1101/2021.01.03.21249183v1 on medRxiv