tetano
Editor, Senior Moderator
Eur Radiol
. 2021 Mar 30.
doi: 10.1007/s00330-021-07727-x. Online ahead of print.
Development and multicenter validation of a CT-based radiomics signature for predicting severe COVID-19 pneumonia
Liang Li[SUP] #[/SUP][SUP] 1 [/SUP], Li Wang[SUP] #[/SUP][SUP] 2 [/SUP], Feifei Zeng[SUP] 1 [/SUP], Gongling Peng[SUP] 3 [/SUP], Zan Ke[SUP] 1 [/SUP], Huan Liu[SUP] 4 [/SUP], Yunfei Zha[SUP] 5 [/SUP]
Affiliations
Abstract
Objectives: To develop and validate a radiomics nomogram for timely predicting severe COVID-19 pneumonia.
Materials and methods: 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.
Results: 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.
Conclusions: The CT-based radiomics signature shows favorable predictive efficacy for severe COVID-19, which might assist clinicians in tailoring precise therapy.
Key points: ? 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.
Keywords: COVID-19; Nomograms; Pneumonia; Tomography, X-ray computed.
. 2021 Mar 30.
doi: 10.1007/s00330-021-07727-x. Online ahead of print.
Development and multicenter validation of a CT-based radiomics signature for predicting severe COVID-19 pneumonia
Liang Li[SUP] #[/SUP][SUP] 1 [/SUP], Li Wang[SUP] #[/SUP][SUP] 2 [/SUP], Feifei Zeng[SUP] 1 [/SUP], Gongling Peng[SUP] 3 [/SUP], Zan Ke[SUP] 1 [/SUP], Huan Liu[SUP] 4 [/SUP], Yunfei Zha[SUP] 5 [/SUP]
Affiliations
- PMID: 33786655
- DOI: 10.1007/s00330-021-07727-x
Abstract
Objectives: To develop and validate a radiomics nomogram for timely predicting severe COVID-19 pneumonia.
Materials and methods: 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.
Results: 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.
Conclusions: The CT-based radiomics signature shows favorable predictive efficacy for severe COVID-19, which might assist clinicians in tailoring precise therapy.
Key points: ? 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.
Keywords: COVID-19; Nomograms; Pneumonia; Tomography, X-ray computed.