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

BACKGROUND: We aimed to analyze the prognostic power of CT-based radiomics models using data of 14,339 COVID-19 patients. METHODS: Whole lung segmentations were performed automatically using a deep learning-based model to extract 107 intensity and texture radiomics features. We used four feature selection algorithms and seven classifiers. We evaluated the models using ten different splitting and cross-validation strategies, including non-harmonized and ComBat-harmonized datasets. The sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were reported. RESULTS: In the test dataset (4,301) consisting of CT and/or RT-PCR positive cases, AUC, sensitivity, and specificity of 0.83 ± 0.01 (CI95%: 0.81-0.85), 0.81, and 0.72, respectively, were obtained by ANOVA feature selector + Random Forest (RF) classifier. Similar results were achieved in RT-PCR-only positive test sets (3,644). In ComBat harmonized dataset, Relief feature selector + RF classifier resulted in the highest performance of AUC, reaching 0.83 ± 0.01 (CI95%: 0.81-0.85), with a sensitivity and specificity of 0.77 and 0.74, respectively. ComBat harmonization did not depict statistically significant improvement compared to a non-harmonized dataset. In leave-one-center-out, the combination of ANOVA feature selector and RF classifier resulted in the highest performance. CONCLUSION: Lung CT radiomics features can be used for robust prognostic modeling of COVID-19. The predictive power of the proposed CT radiomics model is more reliable when using a large multicentric heterogeneous dataset, and may be used prospectively in clinical setting to manage COVID-19 patients.

Clinical, laboratory, and chest CT features of severe versus non-severe pediatric patients with COVID-19 infection among different age groups.

BACKGROUND: This study was performed with the intention of comparing the clinical, laboratory, and chest computed tomography (CT) findings between severe and non-severe patients as well as between different age groups composed of pediatric patients with confirmed COVID-19. METHOD: This study was carried out on a total of 53 confirmed COVID-19 pediatric patients who were hospitalized in Namazi and Ali Asghar Hospitals, Shiraz, Iran. The patients were divided into two severe (n = 27) and non-severe (n = 28) groups as well as into other three groups in terms of their age: aged less than two years, aged 3-12 years and 13-17 years. It should be noted that CT scans, laboratory, and clinical features were taken from all patients at the admission time. Abnormal chest CT in COVID-19 pneumonia was found to show one of the following findings: ground-glass opacities (GGO), bilateral involvement, peripheral and diffuse distribution. RESULT: Fever (79.2%) and dry cough (75.5%) were the most common clinical symptoms. Severe COVID-19 patients showed lymphocytosis, while the non-severe ones did not (P = 0.03). C-reactive protein (CRP) was shown to be significantly lower in patients aged less than two years than those aged 3-12 and 13-17 years (P = 0.01). It was shown also that O2 saturation experienced a significant increase as did patients' age (P = 0.01). Severe patients had significantly higher CT abnormalities than non-severe patients (48.0% compared to 17.9%, respectively) (P = 0.02). CONCLUSION: Lymphocytosis and abnormal CT findings are among the factors most associated with COVID-19 severity. It was, moreover, showed that the severity of COVID-19, O2 saturation, and respiratory distress were improved as the age of confirmed COVID-19 pediatric patients increased.