AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia

Guillaume Chassagnon; Maria Vakalopoulou; Enzo Battistella; Stergios Christodoulidis; Trieu-Nghi Hoang-Thi; Severine Dangeard; Eric Deutsch; Fabrice Andre; Enora Guillo; Nara Halm; Stefany El Hajj; Florian Bompard; Sophie Neveu; Chahinez Hani; Ines Saab; Alienor Campredon; Hasmik Koulakian; Souhail Bennani; Gael Freche; Aurelien Lombard; Laure Fournier; Hippolyte Monnier; Teodor Grand; Jules Gregory; Antoine Khalil; Elyas Mahdjoub; Pierre-Yves Brillet; Stephane Tran Ba; Valerie Bousson; Marie-Pierre Revel; Nikos Paragios

To assess the prognostic value of the Chest computed tomography (CT) an extended multi-centric data set was built. We reviewed outcomes in patient charts within the 4 days following chest CT and divided the patients in 3 groups: those who didn't survive, those who required mechanical ventilation and those who were still alive and not intubated. Out of the 478 included patients, 27 died (6%) and 83 were intubated (17%), forming a group of 110 patients with severe short-term outcome (23%). Data of 383 patients from 3 centers were used for training and those of 85 patients from 3 other centers composed an independent test dataset (Table 3) . Radiomics-based prognosis gained significant attention in the recent years towards predicting treatment outcomes 27 . In this study we have adopted a similar strategy, we extracted 107 features related to first order, higher order statistics, texture and shape information for lungs, disease extent and heart. Feature selection was performed on a basis of predictive value consensus. We created several representative partitions 117 of the training set (80% training and 20% validation) and run 13 different supervised classification methods towards optimal separation of the observed clinical ground truth between severe and non-severe cases ( Table 4 ). The features that were shared between the different classifiers were retained as robust imaging biomarkers using a cut-off probability of 0.25 and were aggregated to patients' age and gender (Table 5 ). In total 12 features were retained for the prognosis part and included age, gender, disease extent, descriptors of disease heterogeneity and extension, features of healthy lung and a descriptor of cardiac heterogeneity. Correlations for some these features and the clinical outcome are presented in Figure 5 while a representation of these feature space with respect to the different classes is presented in Figure 3 .