A COVID-19 risk score combining chest CT radiomics and clinical characteristics to differentiate COVID-19 pneumonia from other viral pneumonias

Chen, Zuhua; Li, Xiadong; Li, Jiawei; Zhang, Shirong; Zhou, Pengfei; Yu, Xin; Ren, Yanling; Wang, Jiahao; Zhang, Lidan; Li, Yunjiang; Wu, Baoliang; Hou, Yanchun; Zhang, Ke; Tang, Rongjun; Liu, Yongguang; Ding, Zhongxian; Yang, Bin; Deng, Qinghua; Lin, Qin; Nie, Ke; Cai, Zhen; Ma, Shenglin; Kuang, Yu

doi:10.18632/aging.202735

Cited by 14 publications

(16 citation statements)

References 40 publications

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“…Thin black line hypothesizes that all patients are non-severe COVID-19. The calculation of net benefit was performed by subtracting the proportion of false positive from proportion of true positive in all patients, weighting with the relative harm of giving up treatment compared with the negative consequence of an unnecessary treatment [ 24 ]. The calculation of relative harm was performed by (Pt/(1Pt)), Pt is the threshold probability, where the expected benefit of treatment is equal to the expected benefit of avoiding treatment.…”

Section: Resultsmentioning

confidence: 99%

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

Shi

Cheng³

et al. 2022

Eur J Med Res

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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.

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Section: Resultsmentioning

confidence: 99%

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

Shi

Cheng³

et al. 2022

Eur J Med Res

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“…Huang et al included influenza A virus, influenza B virus, respiratory syncytial virus, parainfluenza virus, adenovirus, SARS coronavirus, Epstein–Barr virus, measles virus, or other viruses from nasopharyngeal swabs or bronchoalveolar lavage fluid ( 20 ). Chen et al included influenza virus-induced, adenovirus-induced, syncytial virus-induced, and cytomegalovirus-induced pneumonias ( 21 ). Although their studies showed that the radiomics model was an effective predictive tool to distinguish COVID-19 from other viral pneumonias (with AUC 0.807–0.888), the different types of viral pneumonia had different CT findings, which is a potential confounding factor for further comparison.…”

Section: Discussionmentioning

confidence: 99%

“…The result showed that radiomics was a valuable tool to help radiologists distinguish COVID-19 from influenza virus pneumonia. The nomogram had been widely applied to predict clinical diseases, such as the differentiation of benign and malignant cancers, cancer recurrence, and lymph node metastasis (22)(23)(24), and it was also commonly used in COVID-19 pneumonia diagnosis (20,21). In our research, the radiomics nomogram was also developed to predict COVID-19 pneumonia and aimed to illustrate the relationship between Radscore and the risk of COVID-19 pneumonia graphically.…”

Section: Discussionmentioning

confidence: 99%

Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia

Lin

Liu

Deng

et al. 2021

Front. Public Health

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Objectives: To develop and validate a radiomics model for distinguishing coronavirus disease 2019 (COVID-19) pneumonia from influenza virus pneumonia.Materials and Methods: A radiomics model was developed on the basis of 56 patients with COVID-19 pneumonia and 90 patients with influenza virus pneumonia in this retrospective study. Radiomics features were extracted from CT images. The radiomics features were reduced by the Max-Relevance and Min-Redundancy algorithm and the least absolute shrinkage and selection operator method. The radiomics model was built using the multivariate backward stepwise logistic regression. A nomogram of the radiomics model was established, and the decision curve showed the clinical usefulness of the radiomics nomogram.Results: The radiomics features, consisting of nine selected features, were significantly different between COVID-19 pneumonia and influenza virus pneumonia in both training and validation data sets. The receiver operator characteristic curve of the radiomics model showed good discrimination in the training sample [area under the receiver operating characteristic curve (AUC), 0.909; 95% confidence interval (CI), 0.859–0.958] and in the validation sample (AUC, 0.911; 95% CI, 0.753–1.000). The nomogram was established and had good calibration. Decision curve analysis showed that the radiomics nomogram was clinically useful.Conclusions: The radiomics model has good performance for distinguishing COVID-19 pneumonia from influenza virus pneumonia and may aid in the diagnosis of COVID-19 pneumonia.

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“…This work was supported by the Coronavirus disease 2019 (COVID-19) emergency plan project of Anqing (2020Z1003). compared with the negative consequence of an unnecessary treatment [24]. The calculation of relative harm was performed by (Pt/(1-Pt)), Pt is the threshold probability, where the expected bene t of treatment is equal to the expected bene t of avoiding treatment.…”

Section: Ethics Approval and Consent To Participatementioning

confidence: 99%

“…The use of multi-task Unet network, which could segment the lesion or lung abnormalities related to COVID-19 automatically, increased the potential value of the radiomics nomogram in evaluating the clinical condition of patients with COVID-19.Previous studies [18,19,24] have demonstrated that CT-based radiomics as a superior tool for screening potential new cases of COVID-19, and had a good prediction on discriminating COVID-19 and non-COVID-19 pneumonia or other types of viral pneumonia. Mei et al[24] used CT-based radiomics achieved an AUC of 0.92 and had equal sensitivity as compared to a senior radiologist when applied to a group of 279 cases, CT-based radiomics can be severed as a rapid method for screening COVID-19 patients. Huang et al[19] summarized 154 patients with viral pneumonia (including 65 cases of in uenza pneumonia and 89 cases of COVID-19) to develop a CT-based radiomics model, the results showed radiomics model had a satisfactory performance in distinguishing in uenza pneumonia and COVID-19.…”

mentioning

confidence: 99%

CT-based Radiomic Nomogram for Predicting the Severity of Patients With COVID-19

Shi

Cheng³

et al. 2021

Preprint

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Background: The coronavirus disease 2019 (COVID-19) is a pandemic now, and the severe COVID-19 determines the management and treatment, even prognosis. We aim to develop and validate a radiomics nomogram for identifying severe patients with COVID-19. To develop and validate a radiomics nomogram for identifying severe patients with 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 4 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 severe patients with COVID-19 for better guiding a prompt management and treatment.

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A COVID-19 risk score combining chest CT radiomics and clinical characteristics to differentiate COVID-19 pneumonia from other viral pneumonias

Cited by 14 publications

References 40 publications

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

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

Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia

CT-based Radiomic Nomogram for Predicting the Severity of Patients With COVID-19

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