Publication date: Jun 21, 2026
Early identification of patients at risk of severe pneumonia during Omicron SARS-CoV-2 infection is critical for optimizing care and allocating resources. While clinical markers provide insights, imaging-derived radiomics features may enhance prognostic accuracy. We developed a multimodal predictive model combining Delta Radiomics features from serial chest CT scans with clinical data, including blood biochemical markers and lymphocyte subsets. The primary prediction target was severe/critical Omicron pneumonia during hospitalization. Mild and moderate cases were grouped as non-severe disease, whereas severe and critical cases were defined as the severe class for binary classification. The model was trained on 91 patients from the first center, internally validated on 23 patients, and externally tested on 32 patients from a second center. Machine learning algorithms including Logistic Regression, Random Forest, and MLP were applied, and a nomogram was constructed for individualized risk prediction. The combined model showed high discrimination in the training cohort and maintained favorable performance in the internal validation and independent external test cohorts, achieving AUCs of 0. 885 and 0. 875, respectively. The Delta Radiomics signature, particularly with MLP, showed comparatively stable predictive performance. These findings suggest the added value of temporal CT-derived radiomics when integrated with clinical biomarkers, although further validation in larger prospective cohorts is required. Integrating temporal imaging features with clinical data offers a non-invasive method for early prediction of severe/critical Omicron pneumonia, supporting individualized triage and more efficient allocation of medical resources.
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| Concepts | Keywords |
|---|---|
| Biomarkers | Clinical biomarkers |
| Hospitalization | Delta radiomics |
| Optimizing | Machine learning |
| Pneumonia | Nomogram |
| Radiomics | Omicron COVID-19 |
| Prognostic risk stratification |