Radiomic features based automatic classification of CT lung findings for COVID-19 patients

Introduction. The lung CT images of COVID-19 patients can be typically characterized by three different findings- Ground Glass Opacity (GGO), consolidation and pleural effusion. GGOs have been shown to precede consolidations and has different heterogeneous appearance. Conventional severity scoring only uses total area of lung involvement ignoring appearance of the effected regions. This study proposes a baseline to select heterogeneity/radiomic features that can distinguish these three pathological lung findings.Methods. Four approaches were implemented to select features from a pool of 44 features. First one is a manual feature selection method. The rest are automatic feature selection methods based on Genetic Algorithm (GA) coupled with (1) K-Nearest-Neighbor (GA-KNN), (2) binary-decision-tree (GA-BDT) and (3) Artificial-Neural-Network (GA-ANN). For the purpose of validation, an ANN was trained using the selected features and tested on a completely independent data set.Results. Manual selection of nine radiomic features was found to provide the most accurate results with the highest sensitivity, specificity and accuracy (85.7% overall accuracy and 0.90 area under receiver operating characteristic curve) followed by GA-BDT, GA-KNN and GA-ANN (accuracy 78%, 77.5% and 76.8%).Conclusion. Manually selected nine radiomic features can be used in accurate severity scoring allowing the clinician to plan for more effective personalized treatment. They can also be useful for monitoring the progression of COVID-19 and response to therapy for clinical trials.