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Radiomics analysis enables fatal outcome prediction for hospitalized patients with coronavirus disease 2019 (COVID-19).
- Source :
-
Acta Radiologica . Mar2022, Vol. 63 Issue 3, p319-327. 9p. - Publication Year :
- 2022
-
Abstract
- Background: In December 2019, a rare respiratory disease named coronavirus disease 2019 (COVID-19) broke out, leading to great concern around the world. Purpose: To develop and validate a radiomics nomogram for predicting the fatal outcome of COVID-19 pneumonia. Material and Methods: The present study consisted of a training dataset (n = 66) and a validation dataset (n = 30) with COVID-19 from January 2020 to March 2020. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) Cox regression model. A radiomics score (Rad-score) was developed from the training cohort. The radiomics model, clinical model, and integrated model were built to assess the association between radiomics signature/clinical characteristics and the mortality of COVID-19 cases. The radiomics signature combined with the Rad-score and the independent clinical factors and radiomics nomogram were constructed. Results: Seven stable radiomics features associated with the mortality of COVID-19 were finally selected. A radiomics nomogram was based on a combined model consisting of the radiomics signature and the clinical risk factors indicating optimal predictive performance for the fatal outcome of patients with COVID-19 with a C-index of 0.912 (95% confidence interval [CI] 0.867–0.957) in the training dataset and 0.907 (95% CI 0.849–0.966) in the validation dataset. The calibration curves indicated optimal consistency between the prediction and the observation in both training and validation cohorts. Conclusion: The CT-based radiomics nomogram indicated favorable predictive efficacy for the overall survival risk of patients with COVID-19, which could help clinicians intensively follow up high-risk patients and make timely diagnoses. [ABSTRACT FROM AUTHOR]
- Subjects :
- *COVID-19
*RADIOMICS
*HOSPITAL patients
*COVID-19 pandemic
*DISEASE nomenclature
*DIGITAL image processing
*CONFIDENCE intervals
*CALIBRATION
*REGRESSION analysis
*RISK assessment
*DATABASE management
*HOSPITAL care
*PREDICTION models
*PROPORTIONAL hazards models
*LONGITUDINAL method
*ALGORITHMS
*EVALUATION
Subjects
Details
- Language :
- English
- ISSN :
- 02841851
- Volume :
- 63
- Issue :
- 3
- Database :
- Academic Search Index
- Journal :
- Acta Radiologica
- Publication Type :
- Academic Journal
- Accession number :
- 154591477
- Full Text :
- https://doi.org/10.1177/0284185121994695