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The authors of this study aimed to develop and validate a radiomics nomogram for the prompt prediction of severe COVID-19 pneumonia. This was done through the retrospective collection of 316 COVID-19 patients (246 non-severe and 70 severe cases), which were allocated to training, validation, and testing cohorts. The authors found that the CT-based radiomics signature showed favourable predictive efficacy for severe COVID-19, which may help to assist clinicians in customising more precise therapy.

Key points

  • Radiomics can be applied in CT images of COVID-19 and radiomics signature was an independent predictor of severe COVID-19.
  • CT-based radiomics model can predict severe COVID-19 with satisfactory accuracy compared with subjective CT findings and clinical factors.
  • Radiomics nomogram integrated with the radiomics signature, subjective CT findings, and clinical factors can achieve better severity prediction with improved diagnostic performance.

Article: Development and multicenter validation of a CT-based radiomics signature for predicting severe COVID-19 pneumonia

Authors: Liang Li, Li Wang, Feifei Zeng, Gongling Peng, Zan Ke, Huan Liu & Yunfei Zha

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