{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:16:02Z","timestamp":1771665362243,"version":"3.50.1"},"reference-count":26,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T00:00:00Z","timestamp":1623715200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:p><jats:bold>Objectives:<\/jats:bold> To develop and validate a radiomics model for distinguishing coronavirus disease 2019 (COVID-19) pneumonia from influenza virus pneumonia.<\/jats:p><jats:p><jats:bold>Materials and Methods:<\/jats:bold> 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.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> 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\u20130.958] and in the validation sample (AUC, 0.911; 95% CI, 0.753\u20131.000). The nomogram was established and had good calibration. Decision curve analysis showed that the radiomics nomogram was clinically useful.<\/jats:p><jats:p><jats:bold>Conclusions:<\/jats:bold> 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.<\/jats:p>","DOI":"10.3389\/fpubh.2021.663965","type":"journal-article","created":{"date-parts":[[2021,6,15]],"date-time":"2021-06-15T04:53:40Z","timestamp":1623732820000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Radiomics Is Effective for Distinguishing Coronavirus Disease 2019 Pneumonia From Influenza Virus 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