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

The statistical analysis for the deep learning-based segmentation framework and the radiomics study was performed using Python 3.7, Scipy 35 , Scikit-learn 36 , TensorFlow 37 and Pyradiomics 34 libraries. The dice similarity score (DSC) 26 was calculated to assess the similarity between the 2 manual segmentations of each CT exam of the test dataset and between manual and automated segmentations. The DSC between manual segmentations served as reference to evaluate the similarity between the automated and the two manual segmentations. Moreover, the Hausdorff distance was also calculated to evaluate the quality of the automated segmentations in a similar manner. Disease extent was calculated by dividing the volume of diseased lung by the lung volume and expressed in percentage of the total lung volume. Disease extent measurement between manual segmentations and between automated and manual segmentations were compared using paired Student's t-tests. For the stratification of the dataset into the different categories, classic machine learning metrics, namely balanced accuracy, weighted precision, and weighted specificity and sensitivity were used. Moreover, the correlations between each feature and the outcome was computing using a Pearson correlation over the entire dataset.