Evaluation of textural-based radiomics features for differentiation of COVID-19 pneumonia from non-COVID pneumonia

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Abstract

Background: The false-positive rate of computed tomography (CT) images in the diagnosis of coronavirus disease 2019 (COVID-19) is a challenge for the management in the pandemic. The main purpose of this study is to investigate the textural radiomics features on chest CT images of COVID-19 pneumonia patients and compare them with those of non-COVID pneumonia. This is a retrospective study. Some textural radiomics features were extracted from the CT images of 66 patients with COVID-19 pneumonia and 40 with non-COVID pneumonia. For radiomics analysis, the regions of interest (ROIs) were manually identified inside the pulmonary ground-glass opacities. For each ROI, 12 textural features were obtained and, then, statistical analysis was performed to assess the differences in these features between the two study groups. Results: 8 of the 12 texture features demonstrated a significant difference (P < 0.05) in two groups, with COVID-19 pneumonia lesions tending to be more heterogeneous in comparison with the non-COVID cases. Among the 8 significant features, only two (homogeneity and energy) were found to be higher in non-COVID cases. Conclusions: Textural radiomics features can be used for differentiating COVID-19 pneumonia from non-COVID pneumonia, as a non-invasive method, and help with better prognosis and diagnosis of COVID-19 patients.

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Soleymani, Y., Jahanshahi, A. R., Hefzi, M., Fazel Ghaziani, M., Pourfarshid, A., & Khezerloo, D. (2021). Evaluation of textural-based radiomics features for differentiation of COVID-19 pneumonia from non-COVID pneumonia. Egyptian Journal of Radiology and Nuclear Medicine, 52(1). https://doi.org/10.1186/s43055-021-00592-0

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