Settings
  • Light
  • Dark
  • Auto
Select Page

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

Latest posts

Welcome to the blog on Artificial Intelligence of the European Society of Radiology

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.