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10.3390/diagnostics11060991

http://scihub22266oqcxt.onion/10.3390/diagnostics11060991
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34072573!8229671!34072573
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suck abstract from ncbi


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pmid34072573      Diagnostics+(Basel) 2021 ; 11 (6): ä
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  • A Meta-Analysis of Computerized Tomography-Based Radiomics for the Diagnosis of COVID-19 and Viral Pneumonia #MMPMID34072573
  • Kao YS; Lin KT
  • Diagnostics (Basel) 2021[May]; 11 (6): ä PMID34072573show ga
  • 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: 0.818-0.929), and the pooled specificity was 0.811 (95% CI: 0.667-0.902). 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.
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