Two-step machine learning to diagnose and predict involvement of lungs in COVID-19 and pneumonia using CT radiomics. (November 2022)

• Record Type: Journal Article
• Authors: Moradi Khaniabadi, Pegah; Bouchareb, Yassine; Al-Dhuhli, Humoud; Shiri, Isaac; Al-Kindi, Faiza; Moradi Khaniabadi, Bita; Zaidi, Habib; Rahmim, Arman
• Is Part Of: Computers in biology and medicine. Volume 150(2022)
• Publication Date: 2022-11
• Subjects: COVID-19 -- Machine learning -- Pneumonia -- Radiomics -- CT images -- Diagnosis -- Prediction; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106165
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Objective: To develop a two-step machine learning (ML) based model to diagnose and predict involvement of lungs in COVID-19 and non COVID-19 pneumonia patients using CT chest radiomic features. Methods: Three hundred CT scans (3-classes: 100 COVID-19, 100 pneumonia, and 100 healthy subjects) were enrolled in this study. Diagnostic task included 3-class classification. Severity prediction score for COVID-19 and pneumonia was considered as mild (0-25%), moderate (26-50%), and severe (>50%). Whole lungs were segmented utilizing deep learning-based segmentation. Altogether, 107 features including shape, first-order histogram, second and high order texture features were extracted. Pearson correlation coefficient (PCC≥90%) followed by different features selection algorithms were employed. ML-based supervised algorithms (Naïve Bays, Support Vector Machine, Bagging, Random Forest, K-nearest neighbors, Decision Tree and Ensemble Meta voting) were utilized. The optimal model was selected based on precision, recall and area-under-curve (AUC) by randomizing the training/validation, followed by testing using the test set. Results: Nine pertinent features (2 shape, 1 first-order, and 6 second-order) were obtained after features selection for both phases. In diagnostic task, the performance of 3-class classification using Random Forest was 0.909±0.026, 0.907±0.056, 0.902±0.044, 0.939±0.031, and 0.982±0.010 for precision, recall, F1-score, accuracy, and AUC, respectively. The severity prediction task using Random Forest achieved 0.868±0.123 precision, 0.865±0.121 recall, 0.853±0.139 F1-score, 0.934±0.024 accuracy, and 0.969±0.022 AUC. Conclusion: The two-phase ML-based model accurately classified COVID-19 and pneumonia patients using CT radiomics, and adequately predicted severity of lungs involvement. This 2-steps model showed great potential in assessing COVID-19 CT images towards improved management of patients. Highlights: A novel 2-phase ML radiomics signature approach was developed using radiomic features. The 2-phase ML approach accurately classified COVID-19 and pneumonia patients. The 2-phase ML approach adequately predicted severity of lungs involvement. Lung CT radiomics are promising for generalizable diagnosis and severity prediction. The 2-phase ML approach could help in better managing COVID-19 and pneumonia patients.