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