Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs.

Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios / Iori, M.; Di Castelnuovo, C.; Verzellesi, L.; Meglioli, G.; Lippolis, D. G.; Nitrosi, A.; Monelli, F.; Besutti, G.; Trojani, V.; Bertolini, M.; Botti, A.; Castellani, G.; Remondini, D.; Sghedoni, R.; Croci, S.; Salvarani, C.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 12:8(2022), pp. 3903-N/A. [10.3390/app12083903]

Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios

Monelli F.;Besutti G.;Botti A.;Salvarani C.
2022

Abstract

Aim: The aim of this study was to develop robust prognostic models for mortality prediction of COVID-19 patients, applicable to different sets of real scenarios, using radiomic and neural network features extracted from chest X-rays (CXRs) with a certified and commercially available software. Methods: 1816 patients from 5 different hospitals in the Province of Reggio Emilia were included in the study. Overall, 201 radiomic features and 16 neural network features were extracted from each COVID-19 patient’s radiography. The initial dataset was balanced to train the classifiers with the same number of dead and survived patients, randomly selected. The pipeline had three main parts: balancing procedure; three-step feature selection; and mortality prediction with radiomic features through three machine learning (ML) classification models: AdaBoost (ADA), Quadratic Discriminant Analysis (QDA) and Random Forest (RF). Five evaluation metrics were computed on the test samples. The performance for death prediction was validated on both a balanced dataset (Case 1) and an imbalanced dataset (Case 2). Results: accuracy (ACC), area under the ROC-curve (AUC) and sensitivity (SENS) for the best classifier were, respectively, 0.72 ± 0.01, 0.82 ± 0.02 and 0.84 ± 0.04 for Case 1 and 0.70 ± 0.04, 0.79 ± 0.03 and 0.76 ± 0.06 for Case 2. These results show that the prediction of COVID-19 mortality is robust in a different set of scenarios. Conclusions: Our large and varied dataset made it possible to train ML algorithms to predict COVID-19 mortality using radiomic and neural network features of CXRs.
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Mortality Prediction of COVID-19 Patients Using Radiomic and Neural Network Features Extracted from a Wide Chest X-ray Sample Size: A Robust Approach for Different Medical Imbalanced Scenarios / Iori, M.; Di Castelnuovo, C.; Verzellesi, L.; Meglioli, G.; Lippolis, D. G.; Nitrosi, A.; Monelli, F.; Besutti, G.; Trojani, V.; Bertolini, M.; Botti, A.; Castellani, G.; Remondini, D.; Sghedoni, R.; Croci, S.; Salvarani, C.. - In: APPLIED SCIENCES. - ISSN 2076-3417. - 12:8(2022), pp. 3903-N/A. [10.3390/app12083903]
Iori, M.; Di Castelnuovo, C.; Verzellesi, L.; Meglioli, G.; Lippolis, D. G.; Nitrosi, A.; Monelli, F.; Besutti, G.; Trojani, V.; Bertolini, M.; Botti, A.; Castellani, G.; Remondini, D.; Sghedoni, R.; Croci, S.; Salvarani, C.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1280531
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