Highlights Holistic information in COVID-19 patients with imaging and non-imaging data can help predict patient outcome in terms of the need for ICU admission. Validation of model over multiple sites is important to establish its generalizablity. Both volume and radiomic features of pulmonary opacities are key to quantifying the extent of lung involvement.

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase-polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method; however, its accuracy in detection is only ~70–75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80–98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source framework, CovidCTNet, composed of a set of deep learning algorithms that accurately differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 95% compared to radiologists (70%). CovidCTNet is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. To facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and model parameter details as open-source. Open-source sharing of CovidCTNet enables developers to rapidly improve and optimize services while preserving user privacy and data ownership.

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.