COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Shiri, Isaac; Salimi, Yazdan; Pakbin, Masoumeh; Hajianfar, Ghasem; Avval, Atlas Haddadi; Sanaat, Amirhossein; Mostafaei, Shayan; Akhavanallaf, Azadeh; Saberi, Abdollah; Mansouri, Zahra; Askari, Dariush; Ghasemian, Mohammadreza; Sharifipour, Ehsan; Sandoughdaran, Saleh; Sohrabi, Ahmad; Sadati, Elham; Livani, Somayeh; Iranpour, Pooya; Kolahi, Shahriar; Khateri, Maziar; Bijari, Salar; Atashzar, Mohammad Reza; Shayesteh, Sajad P.; Khosravi, Bardia; Babaei, Mohammad Reza; Jenabi, Elnaz; Hasanian, Mohammad; Shahhamzeh, Alireza; Ghomi, Seyaed Yaser Foroghi; Mozafari, Abolfazl; Teimouri, Arash; Movaseghi, Fatemeh; Ahmari, Azin; Goharpey, Neda; Bozorgmehr, Rama; Shirzad‐Aski, Hesamaddin; Mortazavi, Roozbeh; Karimi, Jalal; Mortazavi, Nazanin; Besharat, Sima; Afsharpad, Mandana; Abdollahi, Hamid; Geramifar, Parham; Radmard, Amir Reza; Arabi, Hossein; Rezaei‐Kalantari, Kiara; Oveisi, Mehrdad; Rahmim, Arman; Zaidi, Habib

doi:10.1016/j.compbiomed.2022.105467

Cited by 47 publications

(58 citation statements)

References 62 publications

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“… (2) Using single-center data. 2022/[ 14 ] 1110 CT image of COVID-19 patients. Four class severity scoring of severe-, moderate-, mild-, and non-pneumonic were studied.…”

Section: Resultsmentioning

confidence: 99%

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Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics

Khaniabadi

Bouchareb²,

Al-Dhuhli³

et al. 2022

Computers in Biology and Medicine

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“… (2) Using single-center data. 2022/[ 14 ] 1110 CT image of COVID-19 patients. Four class severity scoring of severe-, moderate-, mild-, and non-pneumonic were studied.…”

Section: Resultsmentioning

confidence: 99%

“…In a large cohort multicenter study by CT radiomics features extracted from Lung regions and machine learning from 26,307 patients, prognostic [ 14 ] model was developed. Elsewhere [ 15 ], COVID-19 severity scoring was performed using CT radiomics features and multinomial multiclass machine learning models.…”

Section: Introductionmentioning

confidence: 99%

Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics

Khaniabadi

Bouchareb²,

Al-Dhuhli³

et al. 2022

Computers in Biology and Medicine

Self Cite

View full text Add to dashboard Cite

Section: Sars-cov-2 Disease (Covid-19mentioning

confidence: 99%

“…Many recent studies have also attempted to predict COVID-19 patient clinical prognosis (either mortality, mechanical ventilation requirement, hospitalization or need for intubation) by feeding machine learning (ML) methods with clinical/demographic and/or radiomic features extracted from CXRs or HRCTs [3,[17][18][19][20][21][22][23][24][25]. In their recent study, Bae [17], Varghese [19], and Shiri [23] showed the potential usefulness of information extracted from radiographs. Radiomics is an image data mining framework that extracts extensive information from medical images using a range of features, based on the pixel values of the images; a correlation is then established with clinical and biological findings.…”

Section: Sars-cov-2 Disease (Covid-19mentioning

confidence: 99%

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

et al. 2022

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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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“…The study in [ 146 ] analyzed the prognostic capabilities of radiomics modeling in CT-based imaging. A dataset of CT scans was curated from 24,478 patients and was reduced to 14,339 patients.…”

Section: Covid-19 Prognostic and Longitudinal Modelsmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Used to Combat COVID-19

et al. 2022

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Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

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COVID-19 prognostic modeling using CT radiomic features and machine learning algorithms: Analysis of a multi-institutional dataset of 14,339 patients

Cited by 47 publications

References 62 publications

Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics

Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics

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

A Comprehensive Review of Machine Learning Used to Combat COVID-19

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