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

were considered separately both for the disease extent and entire lung). The features included first order statistics, shape-based features in 2D and 3D together with texture-based features. Radiomics features were enriched with clinical data available from the image metadata (age, gender), disease extent and number of diseased regions. The minimum and maximum values were calculated for the training and validation cohorts and Min-Max normalization was used to normalize the features, the same values were also applied on the test set. As a first step, a number of features were selected using a lasso linear model in order to decrease the dimensionality. The lasso estimator seeks to optimize the following objective function: