Abstract
BackgroundThe 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.Results8 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.ConclusionsTextural 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.
Highlights
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
This study aimed to explore the association of textural radiomics features with COVID-19 pneumonia and nonCOVID pneumonia on lung CT scans; that may provide a noninvasive means to profound recognition of the radiomics pattern of COVID-19 pneumonia as well as help to differentiate between COVID and non-COVID patients
No statistically significant differences were observed in cluster shade of Gray-level co-occurrence matrix (GLCM) and long-run emphasis, run-length nonuniformity, and shortrun emphasis of gray-level run-length matrices (GLRLM) between two groups (P > 0.05)
Summary
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. Some textural radiomics features were extracted from the CT images of 66 patients with COVID-19 pneumonia and 40 with non-COVID pneumonia. Clinical signs of COVID-19 are fever (85%), cough (70%), and shortness of breath (43%) as well as gastrointestinal and abdominal symptoms. In some cases, it can even be asymptomatic.
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