{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,16]],"date-time":"2026-05-16T06:12:45Z","timestamp":1778911965397,"version":"3.51.4"},"reference-count":49,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,5,29]],"date-time":"2021-05-29T00:00:00Z","timestamp":1622246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Introduction: Coronavirus disease 2019 (COVID-19) led to a global pandemic. Although reverse transcription polymerase chain reaction (RT-PCR) of viral nucleic acid is the gold standard for COVID-19 diagnosis, its sensitivity was found to not be high enough in many reports. As radiomics-based diagnosis research has recently emerged, we aimed to use computerized tomography (CT)-based radiomics models to differentiate COVID-19 pneumonia from other viral pneumonia infections. Materials and methods: This study was performed according to the preferred reporting items for systematic review and meta-analysis diagnostic test accuracy studies (PRISMA-DTA) guidelines. The Pubmed, Cochrane, and Embase databases were searched. The pooled sensitivity and pooled specificity were calculated. A summary receiver operating characteristic (sROC) curve was constructed. The study quality was evaluated based on the radiomics quality score. Results: A total of 10,300 patients were involved in this meta-analysis. The radiomics quality score ranged from 13 to 16 (maximum score: 36). The pooled sensitivity was 0.885 (95% CI: 0.818\u20130.929), and the pooled specificity was 0.811 (95% CI: 0.667\u20130.902). The pooled AUC was 906. Conclusion: Our meta-analysis showed that CT-based radiomics feature models can successfully differentiate COVID-19 from other viral pneumonias.<\/jats:p>","DOI":"10.3390\/diagnostics11060991","type":"journal-article","created":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T00:22:15Z","timestamp":1622420535000},"page":"991","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8886-8239","authenticated-orcid":false,"given":"Yung-Shuo","family":"Kao","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, China Medical University Hospital, Taichung 404, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0197-7377","authenticated-orcid":false,"given":"Kun-Te","family":"Lin","sequence":"additional","affiliation":[{"name":"Department of Emergency Medicine, Changhua Christian Hospital, Changhua 500, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,5,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1518","DOI":"10.1002\/jmv.25727","article-title":"Development and clinical application of a rapid IgM-IgG combined antibody test for SARS-CoV-2 infection diagnosis","volume":"92","author":"Li","year":"2020","journal-title":"J. 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