T he scientific, academic, medical and data science communities have come together in the face of the COVID-19 pandemic crisis to rapidly assess novel paradigms in artificial intelligence (AI) that are rapid and secure, and potentially incentivize data sharing and model training and testing without the usual privacy and data ownership hurdles of conventional collaborations 1,2 . Healthcare providers, researchers and industry have pivoted their focus to address unmet and critical clinical needs created by the crisis, with remarkable results [3][4][5][6][7][8][9] . Clinical trial recruitment has been expedited and facilitated by national regulatory bodies and an international cooperative spirit 10-12 . The data analytics and AI disciplines have always fostered open

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Purpose To compare prediction of disease outcome, severity, and patient triage in COVID-19 pneumonia with whole lung radiomics, radiologists’ interpretation, and clinical variables. Methods Our IRB-approved retrospective study included 315 adult patients (mean age 56 (21-100) years, 190 males, 125 females) with COVID-19 pneumonia who underwent non-contrast chest CT. All patients (inpatients, n=210; outpatients, n=105) were followed up for at least two-weeks to record disease outcome. Clinical variables such as presenting symptoms, laboratory data, peripheral oxygen saturation, and comorbid diseases were recorded. Two radiologists assessed each CT in consensus and graded the extent of pulmonary involvement (by percentage of involved lobe) and type of opacities within each lobe. We obtained radiomics for the entire lung and multiple logistic regression analyses with areas under the curve (AUC) as outputs were performed. Results Most patients (276/315,88%) recovered from COVID-19 pneumonia; 36/315 patients (11%) died and 3/315 patients (1%) remain admitted in the hospital. Radiomics differentiated chest CT in outpatient vs inpatient with an AUC of 0.84 (p<0.005), while radiologists’ interpretations of disease extent and opacity type had an AUC of 0.69 (p<0.0001). Whole lung radiomics were superior to the radiologists’ interpretation for predicting patient outcome in terms of ICU admission (AUC:0.75 vs 0.68) and death (AUC:0.81 vs 0.68) (p<0.002). Addition of clinical variables to radiomics improved the AUC to 0.84 for predicting ICU admission. Conclusion Radiomics from non-contrast chest CT were superior to radiologists’ assessment of extent and type of pulmonary opacities in predicting COVID-19 pneumonia outcome, disease severity, and patient triage.