SCIENCE CHINA Information Sciences, Volume 63 , Issue 7 : 172103(2020) https://doi.org/10.1007/s11432-020-2849-3

CT radiomics can help screen the coronavirus disease 2019 (COVID-19): a preliminary study

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  • ReceivedMar 1, 2020
  • AcceptedMar 23, 2020
  • PublishedApr 15, 2020



This work was supported by National Key RD Program of China (Grant Nos. 2017YFC1308700, 2017YFA0205200, 2017YFC1309100, 2017YFA0700401), National Natural Science Foundation of China (Grant Nos. 819300- 53, 91959130, 81971776, 81771924, 81930053, 81227901).


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  • Figure 1

    (Color online) Radiomic heatmap on the overall set. Unsupervised clustering of patients ($n=75$) and radiomics features ($n=77$) reveal clusters of patients with similar radiomic expression patterns.

  • Figure 2

    Results of consensus clustering analysis for radiomic features. The curve depicts the minimum Spearman correlation coefficient between the medoid features with their intra-cluster features.

  • Figure 3

    (Color online) Bar plot of the radiomic signature scores for patients with COVID-19 and other pneumonias types in the (a) training and (b) test sets.

  • Figure 4

    (Color online) ROC curves (a) and calibration curves (b) of the radiomic signature in each set.

  • Figure 5

    (Color online) ROC curves of radiomic signature for each subgroup stratified by gender (a), age (b), with/without chronic disease (c) and degree of severity (d).

  • Table 1   Characteristics of patients in the training and test sets
    Characteristics Total Training set Test set
    Age (mean$\pm$SD, years) 47.8$\pm$20.2 46.2$\pm$20.3 51.1$\pm$19.9
    Gender (male/female, No.) 40/35 26/24 14/11
    Pneumonias type (COVID-19/other types, No.) 46/29 30/20 16/9
  • Table 2   Features and coefficients of the radiomic signature$^{\rm~a)}$
    Name Group Data AUC
    S_GLRLM_GLN GLRLM feature S 0.633
    O_GLCM_Contrast GLCM feature O 0.577
    S_I_Mean Intensity-based statistical feature S 0.552
    O_I_Krutosis Intensity-based statistical feature O 0.508
    S_I_Range Intensity-based statistical feature S 0.605
    O_GLCM_Variance GLCM feature O 0.728
    O_I_Minimum Intensity-based statistical feature O 0.538
    S_GLRLM_LGLRE GLRLM feature S 0.558
    S_I_Krutosis Intensity-based statistical feature S 0.607
    S_I_Skewness Intensity-based statistical feature S 0.582
    O_GLCM_Energy GLCM feature O 0.628
    O_GLRLM_LRLGLE GLRLM feature O 0.487
    S_GLCM_Cluster_shade GLCM feature S 0.605
    O_GLRLM_RP GLRLM feature O 0.490
    O_GLRLM_SRLGLE GLRLM feature O 0.528
    S_GLRLM_SRLGLE GLRLM feature S 0.518
    S_GLRLM_LRHGLE GLRLM feature S 0.513