It has been widely demonstrated that radiological imaging significantly contributes to diagnosing and monitoring pulmonary and systemic involvement of patients affected by COVID-19 using different techniques like chest X-ray (CXR), Computed Tomography (CT), and Magnetic Resonance Imaging.

Recently, several authors proposed the application of advanced imaging tools including machine learning and radiomics for COVID-19. For instance, Chandra et al. developed an automatic screening method based on radiomic features and Wang et al. used radiomics to distinguish COVID-19 from other viral infections.

Moreover, Ferreira Junior et al. [6], using a publicly available cohort, demonstrated that radiomics not only correlates with the etiologic agent of acute infections but also supports the short-term risk stratification of COVID-19 patients. Inspired by these interesting results, we developed and tested a CXR-based radiomics integrated model including demographics, first-line laboratory and clinical findings collected at admission and we assessed the predicting role of such model for Intensive Care Unit (ICU) hospitalization and overall outcome.

We retrospectively examined CXR at admission of 203 patients hospitalized in our tertiary center for COVID-19.

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