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 deep learning (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 CXRstaken 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 up to 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.
We predict mechanical ventilation requirement and mortality using computational modeling of chest radiographs ( CXRs ) for coronavirus disease 2019 (COVID-19) patients. This two-center, retrospective study analyzed 530 deidentified CXRs from 515 COVID-19 patients treated at Stony Brook University Hospital and Newark Beth Israel Medical Center between March and August 2020. DL and machine learning classifiers to predict mechanical ventilation requirement and mortality were trained and evaluated using patient CXRs . A novel radiomic embedding framework was also explored for outcome prediction. All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic and DL classification models had mAUCs of 0.78+/-0.02 and 0.81+/-0.04, compared with expert scores mAUCs of 0.75+/-0.02 and 0.79+/-0.05 for mechanical ventilation requirement and mortality prediction , respectively. Combined classifiers using both radiomics and expert severity scores resulted in mAUCs of 0.79+/-0.04 and 0.83+/-0.04 for each prediction task, demonstrating improvement over either artificial intelligence or radiologist interpretation alone . Our results also suggest instances where inclusion of radiomic features in DL improves model predictions, something that might be explored in other pathologies. The models proposed in this study and the prognostic information they provide might aid physician decision making and resource allocation during the COVID-19 pandemic.
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We also investigate the relative advantages of deep learning (DL), radiomics, and DL of radiomic-embedded feature maps in predicting these outcomes. Methods:
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deidentified CXRs taken from 514 patients suspected of
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infection on presentation
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(SBUH)
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(NBIMC) between the months of March and June
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A DL segmentation pipeline was developed to generate masks for both lung fields and artifacts for each CXR. Machine
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on 353 baseline CXRstaken from COVID-19 positive patients
using patient CXRs
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Results: Classification models
All results are compared against radiologist grading of CXRs (zone-wise expert severity scores). Radiomic and DL classification models had mAUCs of 0.78+/-0.02 and 0.81+/-0.04, compared with expert scores mAUCs of 0.75+/-0.02 and 0.79+/-0.05
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(test N=154) and mortality (test N=190) had AUCs of up to 0.904
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mortality prediction
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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
Combined classifiers using both radiomics and expert severity scores resulted in mAUCs of 0.79+/-0.04 and 0.83+/-0.04 for each prediction task, demonstrating improvement over either artificial intelligence or radiologist interpretation alone
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that radiomic embedding improves DL models in medical image analysis, a technique
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