Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia
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 pneumo...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2021-06-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2021.663965/full |
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| author | Liaoyi Lin Jinjin Liu Qingshan Deng Na Li Jingye Pan Houzhang Sun Shichao Quan |
| author_facet | Liaoyi Lin Jinjin Liu Qingshan Deng Na Li Jingye Pan Houzhang Sun Shichao Quan |
| author_sort | Liaoyi Lin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T02:07:15Z |
| format | Article |
| id | doaj.art-e86d0a59b89a4e40811ca5c21d8cb015 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-12-22T02:07:15Z |
| publishDate | 2021-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-e86d0a59b89a4e40811ca5c21d8cb0152022-12-21T18:42:30ZengFrontiers Media S.A.Frontiers in Public Health2296-25652021-06-01910.3389/fpubh.2021.663965663965Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus PneumoniaLiaoyi Lin0Jinjin Liu1Qingshan Deng2Na Li3Jingye Pan4Houzhang Sun5Shichao Quan6Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of Intensive Care Unit, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaDepartment of General Medicine, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, ChinaObjectives: 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.https://www.frontiersin.org/articles/10.3389/fpubh.2021.663965/fullCOVID-19influenzanomogramradiomicscomputed tomography |
| spellingShingle | Liaoyi Lin Jinjin Liu Qingshan Deng Na Li Jingye Pan Houzhang Sun Shichao Quan Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia Frontiers in Public Health COVID-19 influenza nomogram radiomics computed tomography |
| title | Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia |
| title_full | Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia |
| title_fullStr | Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia |
| title_full_unstemmed | Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia |
| title_short | Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus Pneumonia |
| title_sort | radiomics is effective for distinguishing coronavirus disease 2019 pneumonia from influenza virus pneumonia |
| topic | COVID-19 influenza nomogram radiomics computed tomography |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2021.663965/full |
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