Predicting Mechanical Ventilation Requirement and Mortality in COVID-19 using Radiomics and Deep Learning on Chest Radiographs: A Multi-Institutional Study

OBJECTIVES: To predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs (CXR) for coronavirus disease 2019 (COVID-19) patients We also investigate the relative advantages of deelearning (DL), radiomics, and DL of radiomic-embedded feature maps in predicting these outcomes METHODS: This two-center, retrospective study analyzed deidentified CXRs taken from 514 patients suspected of COVID-19 infection on presentation at Stony Brook University Hospital (SBUH) and Newark Beth Israel Medical Center (NBIMC) between the months of March and June 2020 A DL segmentation pipeline was developed to generate masks for both lung fields and artifacts for each CXR Machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated on 353 baseline CXRs taken from COVID-19 positive patients A novel radiomic embedding framework is also explored for outcome prediction RESULTS: Classification models for mechanical ventilation requirement (test N=154) and mortality (test N=190) had AUCs of uto 0 904 and 0 936, respectively We also found that the inclusion of radiomic-embedded maps improved DL model predictions of clinical outcomes CONCLUSIONS: We demonstrate the potential for computerized analysis of baseline CXR in predicting disease outcomes in COVID-19 patients Our results also suggest that radiomic embedding improves DL models in medical image analysis, a technique that might be explored further in other pathologies The models proposed in this study and the prognostic information they provide, complementary to other clinical data, might be used to aid physician decision making and resource allocation during the COVID-19 pandemic