Chest Imaging and Clinical Evaluation in COVID-19

Chest imaging has been central to the diagnosis, risk stratification and follow-up of patients with COVID-19. High-resolution computed tomography (CT) typically reveals bilateral, peripheral ground-glass opacities and mixed consolidations that evolve with disease stage into septal thickening and a “crazy-paving” pattern. Plain chest radiography offers a rapid bedside tool in resource-limited settings, although its sensitivity is lower in early disease. Imaging findings correlate closely with clinical parameters, including vital signs, oxygen requirement and laboratory markers such as neutrophil-to-lymphocyte ratio. Semi-quantitative scoring systems applied to CT and radiographs provide reproducible measures of pulmonary involvement and have been incorporated into nomograms alongside age and inflammatory markers to predict progression to severe illness. Advanced analysis of radiomics features and artificial-intelligence algorithms has refined outcome prediction by integrating imaging phenotypes with biochemical and genomic data. Follow-up imaging demonstrates that many patients achieve complete radiological resolution within weeks, though some exhibit fibrotic sequelae. Together, imaging and clinical evaluation form a complementary framework for individualising treatment, optimising resource allocation and assessing long-term pulmonary recovery.

Research from Nature Portfolio

A multicentre retrospective study demonstrated that a combined model of CT severity score, age and neutrophil-to-lymphocyte ratio on admission can accurately predict which patients with moderate COVID-19 pneumonia will progress to severe disease. By developing a calibrated nomogram incorporating these risk factors, clinicians can stratify patients at presentation and guide decisions on monitoring intensity and early escalation of therapy. This research underscores the utility of integrating quantitative imaging scores with routine blood tests to forecast clinical trajectories.

Chest Imaging and Clinical Evaluation in COVID-19 publication trend

The graph below shows the total number of articles in chest imaging and clinical evaluation in covid-19 across all publications each year (not limited to Nature Index journals).

Technical terms

Computed Tomography (CT): A non-invasive imaging technique that uses X-rays and computer processing to produce cross-sectional images of the chest.

Ground-glass opacity (GGO): A hazy radiographic area of increased attenuation in the lung without obscuring underlying vessels, commonly seen in early COVID-19.

Consolidation: A region of lung tissue filled with liquid instead of air, appearing as a dense opacity on imaging.

CT severity score: A semi-quantitative scale assessing the extent of pulmonary involvement, often assigning points to each lobe.

Radiomics features: Quantitative image descriptors extracted by algorithms to characterise lesion texture, shape and intensity.

Neutrophil-to-lymphocyte ratio (NLR): A blood parameter calculated by dividing the neutrophil count by the lymphocyte count, serving as a marker of systemic inflammation.

References

1. Multimodal data fusion using sparse canonical correlation analysis and cooperative learning: a COVID-19 cohort study. npj Digital Medicine (2024).
2. Clinical utilization of artificial intelligence-based COVID-19 pneumonia quantification using chest computed tomography – a multicenter retrospective cohort study in Japan. Respiratory Research (2023).
3. Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics. Nature Communications (2020).
4. Coronavirus Disease 2019 (COVID-19): A Systematic Review of Imaging Findings in 919 Patients.. American Journal of Roentgenology (2020).
5. Correlation between Chest CT Severity Scores and the Clinical Parameters of Adult Patients with COVID‐19 Pneumonia. Radiology Research and Practice (2021).
6. The pulmonary sequalae in discharged patients with COVID-19: a short-term observational study. Respiratory Research (2020).