Radiomics for Predicting Oxygen Necessity in COVID-19 Patients Using Longitudinal Lung Computed Tomography

Rafaela Cappelari Ravazio, Christian Mattjie de Oliveira, Luis Vinícius de Moura, Lucas S. Kupssinskü, Rodrigo C. Barros, Denise Cantarelli Machado, Ana Maria Marques da Silva

Abstract: The COVID-19 pandemic has overwhelmed healthcare systems worldwide. Computed tomography imaging has emerged as an essential tool in diagnosing and monitoring cases, allowing for the detection of pulmonary changes even in the early stages of the disease. This study introduces a radiomics-based predictive model aimed at predicting the need for oxygen support in COVID-19 patients. Utilizing a private dataset collected from a local hospital with 81 patients that underwent two longitudinal chest CT scans, we employed two machine learning algorithms, Random Forest and XGBoost, to analyze radiomic features extracted from lung segmentation. We also explore incorporating clinical features in addition to the radiomic ones and using feature selection techniques to handle the high-dimensionality of the dataset. Our best model achieves an AUC of 0.81 in the test set. Our results indicate that only using radiomic features from the last time point reach a higher performance than with additional data. Therefore, it should be practical to implement a framework to predict the need of oxygen support in a clinical setting, as all the information required by the model comes from a single CT scan.