tetano
Editor, Senior Moderator
Eur J Med Res
. 2022 Jan 25;27(1):13.
doi: 10.1186/s40001-022-00634-x.
CT-based radiomic nomogram for predicting the severity of patients with COVID-19
Hengfeng Shi[SUP] #[/SUP][SUP] 1 [/SUP], Zhihua Xu[SUP] #[/SUP][SUP] 2 [/SUP], Guohua Cheng[SUP] 3 [/SUP], Hongli Ji[SUP] 3 [/SUP], Linyang He[SUP] 3 [/SUP], Juan Zhu[SUP] 1 [/SUP], Hanjin Hu[SUP] 1 [/SUP], Zongyu Xie[SUP] 4 [/SUP], Weiqun Ao[SUP] #[/SUP][SUP] 5 [/SUP], Jian Wang[SUP] #[/SUP][SUP] 6 [/SUP]
Affiliations
Abstract
Background: 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.
Methods: 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.
Results: 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.
Conclusion: We present an easy-to-use radiomics nomogram to identify the patients with severe COVID-19 for better guiding a prompt management and treatment.
Keywords: COVID-19; Radiomics; SARS-CoV-2; X-ray computed tomography.
. 2022 Jan 25;27(1):13.
doi: 10.1186/s40001-022-00634-x.
CT-based radiomic nomogram for predicting the severity of patients with COVID-19
Hengfeng Shi[SUP] #[/SUP][SUP] 1 [/SUP], Zhihua Xu[SUP] #[/SUP][SUP] 2 [/SUP], Guohua Cheng[SUP] 3 [/SUP], Hongli Ji[SUP] 3 [/SUP], Linyang He[SUP] 3 [/SUP], Juan Zhu[SUP] 1 [/SUP], Hanjin Hu[SUP] 1 [/SUP], Zongyu Xie[SUP] 4 [/SUP], Weiqun Ao[SUP] #[/SUP][SUP] 5 [/SUP], Jian Wang[SUP] #[/SUP][SUP] 6 [/SUP]
Affiliations
- PMID: 35078525
- DOI: 10.1186/s40001-022-00634-x
Abstract
Background: 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.
Methods: 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.
Results: 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.
Conclusion: We present an easy-to-use radiomics nomogram to identify the patients with severe COVID-19 for better guiding a prompt management and treatment.
Keywords: COVID-19; Radiomics; SARS-CoV-2; X-ray computed tomography.