Determination of reliable whole-lung CT features for robust standard radiomics and delta-radiomics analysis in a crab-eating macaque model of COVID-19: stability and sensitivity analysis

As of 14 December 2021, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the virus that causes coronavirus disease 2019 (COVID-19), caused nearly 269 million confirmed cases and almost 5.3 million deaths worldwide. Chest computed tomography (CT) has high diagnostic sensitivity for the detection of pulmonary disease in COVID-19 patients. Toward timely and accurate clinical evaluation and prognostication, radiomic analyses of CT images have been explored to investigate the correlation of imaging and non-imaging clinical manifestations and outcomes. Delta (∆) radiomics optimally performed from pre-infection to the post-critical phase, requires baseline data typically not obtained in clinical settings; additionally, their robustness is affected by differences in acquisition protocols. In this work, we investigated the reliability, sensitivity, and stability of whole-lung radiomic features of CT images of nonhuman primates either mock-exposed or exposed to SARS-CoV-2 to study imaging biomarkers of SARS-CoV-2 infection. Images were acquired at a pre-exposure baseline and post-exposure days, and lung fields were segmented. The reliability of radiomic features was assessed, and the dynamic range of each feature was compared to the maximum normal intra-subject variation and ranked.