Development and multicenter validation of a CT-based radiomics signature for predicting severe COVID-19 pneumonia.

To develop and validate a radiomics nomogram for timely predicting severe COVID-19 pneumonia. Three hundred and sixteen COVID-19 patients (246 non-severe and 70 severe) were retrospectively collected from two institutions and allocated to training, validation, and testing cohorts. Radiomics features were extracted from chest CT images. Radiomics signature was constructed based on reproducible features using the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm with 5-fold cross-validation. Logistic regression modeling was employed to build different models based on quantitative CT features, radiomics signature, clinical factors, and/or the former combined features. Nomogram performance for severe COVID-19 prediction was assessed with respect to calibration, discrimination, and clinical usefulness. Sixteen selected features were used to build the radiomics signature. The CT-based radiomics model showed good calibration and discrimination in the training cohort (AUC, 0.9; 95% CI, 0.843-0.942), the validation cohort (AUC, 0.878; 95% CI, 0.796-0.958), and the testing cohort (AUC, 0.842; 95% CI, 0.761-0.922). The CT-based radiomics model showed better discrimination capability (all p < 0.05) compared with the clinical factors joint quantitative CT model (AUC, 0.781; 95% CI, 0.708-0.843) in the training cohort, the validation cohort (AUC, 0.814; 95% CI, 0.703-0.897), and the testing cohort (AUC, 0.696; 95% CI, 0.581-0.796). Decision curve analysis demonstrated that in terms of clinical usefulness, the radiomics model outperformed the clinical factors model and quantitative CT model alone. The CT-based radiomics signature shows favorable predictive efficacy for severe COVID-19, which might assist clinicians in tailoring precise therapy. • Radiomics can be applied in CT images of COVID-19 and radiomics signature was an independent predictor of severe COVID-19. • CT-based radiomics model can predict severe COVID-19 with satisfactory accuracy compared with subjective CT findings and clinical factors. • Radiomics nomogram integrated with the radiomics signature, subjective CT findings, and clinical factors can achieve better severity prediction with improved diagnostic performance.

Li L ，Wang L ，Zeng F ，Peng G ，Ke Z ，Liu H ，Zha Y ... -  《-》

Radiomics nomogram for the prediction of 2019 novel coronavirus pneumonia caused by SARS-CoV-2.

Fang X ，Li X ，Bian Y ，Ji X ，Lu J ... -  《-》

A CT-based radiomics nomogram for differentiation of renal angiomyolipoma without visible fat from homogeneous clear cell renal cell carcinoma.

Nie P ，Yang G ，Wang Z ，Yan L ，Miao W ，Hao D ，Wu J ，Zhao Y ，Gong A ，Cui J ，Jia Y ，Niu H ... -  《-》

CT-based radiomic nomogram for predicting the severity of patients with COVID-19.

The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severity of COVID-19 determines the management, treatment, and even prognosis. We aim to develop and validate a radiomics nomogram for identifying patients with severe COVID-19. There were 156 and 104 patients with COVID-19 enrolled in primary and validation cohorts, respectively. Radiomics features were extracted from chest CT images. Least absolute shrinkage and selection operator (LASSO) method was used for feature selection and radiomics signature building. Multivariable logistic regression analysis was used to develop a predictive model, and the radiomics signature, abnormal WBC counts, and comorbidity were incorporated and presented as a radiomics nomogram. The performance of the nomogram was assessed through its calibration, discrimination, and clinical usefulness. The radiomics signature consisting of four selected features was significantly associated with clinical condition of patients with COVID-19 in the primary and validation cohorts (P < 0.001). The radiomics nomogram including radiomics signature, comorbidity and abnormal WBC counts showed good discrimination of severe COVID-19, with an AUC of 0.972, and good calibration in the primary cohort. Application of the nomogram in the validation cohort still gave good discrimination with an AUC of 0.978 and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful to identify the severe COVID-19. We present an easy-to-use radiomics nomogram to identify the patients with severe COVID-19 for better guiding a prompt management and treatment.

Shi H ，Xu Z ，Cheng G ，Ji H ，He L ，Zhu J ，Hu H ，Xie Z ，Ao W ，Wang J ... -  《-》

A novel CT-based radiomics in the distinction of severity of coronavirus disease 2019 (COVID-19) pneumonia.

Xie Z ，Sun H ，Wang J ，Xu H ，Li S ，Zhao C ，Gao Y ，Wang X ，Zhao T ，Duan S ，Hu C ，Ao W ... -  《-》