ArticleBMC infectious diseases2021
A novel CT-based radiomics in the distinction of severity of coronavirus disease 2019 (COVID-19) pneumonia.
Article in BMC infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it, 15 citations in OpenAlex.
- A meta-analysis of the diagnostic test accuracy of CT-based radiomics for the prediction of COVID-19 severity.La Radiologia medica · 2022Pooled it
- Prediction of potential severe coronavirus disease 2019 patients based on CT radiomics: A retrospective study.Medical physics · 2022Trial
- CT-based radiomics nomogram for distinguishingTranslational pediatrics · 2026Article
- Development and Validation of a CT-based Deep Transfer Learning Radiomic Model for Predicting Post-COVID-19 Pulmonary FibrosisCurrent medical imaging · 2026Article
- Predictive models of severe disease in patients with COVID-19 pneumonia at an early stage on CT images using topological properties.Radiological physics and technology · 2025Article
- The severity assessment and nucleic acid turning-negative-time prediction in COVID-19 patients with COPD using a fused deep learning model.BMC pulmonary medicine · 2024Article
- CT radiomics model combined with clinical and radiographic features for discriminating peripheral small cell lung cancer from peripheral lung adenocarcinoma.Frontiers in oncology · 2023Article
- Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting.European radiology · 2022Article
- Severity Assessment of COVID-19 Using a CT-Based Radiomics Model.Stem cells international · 2021Article
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Authors and funding
12 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundConvenient and precise assessment of the severity in coronavirus disease 2019 (COVID-19) contributes to the timely patient treatment and prognosis improvement. We aimed to evaluate the ability of CT-based radiomics nomogram in discriminating the severity of patients with COVID-19 Pneumonia.
methodsA total of 150 patients (training cohort n = 105; test cohort n = 45) with COVID-19 confirmed by reverse transcription polymerase chain reaction (RT-PCR) test were enrolled. Two feature selection methods, Max-Relevance and Min-Redundancy (mRMR) and least absolute shrinkage and selection operator (LASSO), were used to extract features from CT images and construct model. A total of 30 radiomic features were finally retained. Rad-score was calculated by summing the selected features weighted by their coefficients. The radiomics nomogram incorporating clinical-radiological features was eventually constructed by multivariate regression analysis. Nomogram, calibration, and decision-curve analysis were all assessed.
resultsIn both cohorts, 40 patients with COVID-19 pneumonia were severe and 110 patients were non-severe. By combining the 30 radiomic features extracted from CT images, the radiomics signature showed high discrimination between severe and non-severe patients in the training set [Area Under the Curve (AUC), 0.857; 95% confidence interval (CI), 0.775-0.918] and the test set (AUC, 0.867; 95% CI, 0.732-949). The final combined model that integrated age, comorbidity, CT scores, number of lesions, ground glass opacity (GGO) with consolidation, and radiomics signature, improved the AUC to 0.952 in the training cohort and 0.98 in the test cohort. The nomogram based on the combined model similarly exhibited excellent discrimination performance in both training and test cohorts.
conclusionsThe developed model based on a radiomics signature derived from CT images can be a reliable marker for discriminating the severity of COVID-19 pneumonia.
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