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

For the radiomics driven prognosis study, 298 additional patients from centers A (96 patients), B (64 patients) and D (138 patients) were included to increase the size of the dataset. Data of 383 patients from 3 centers (A, B and D) were used for training and those of 85 patients from 3 other centers (C, E, F) composed an independent test set ( Table 3 ). Only one CT examination was included for each patient. Exclusion criteria were (i) contrast medium injection and (ii) important motion artifacts. For short-term outcome assessment, patients were divided into 2 groups: those who died or were intubated in the 4 days following the CT scan composed the severe short-term outcome subgroup, while the others composed the non-severe short-term outcome subgroup.