Literature DB >> 33713401

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

Zuhua Chen1,2, Xiadong Li3,4, Jiawei Li5, Shirong Zhang4, Pengfei Zhou3, Xin Yu3, Yao Ren3, Jiahao Wang3, Lidan Zhang3, Yunjiang Li1, Baoliang Wu1, Yanchun Hou1, Ke Zhang3, Rongjun Tang3, Yongguang Liu1, Zhongxian Ding4, Bin Yang4, Qinghua Deng3, Qin Lin6, Ke Nie7, Zhaobin Cai1,2, Shenglin Ma3,4, Yu Kuang8.   

Abstract

With the continued transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) throughout the world, identification of highly suspected COVID-19 patients remains an urgent priority. In this study, we developed and validated COVID-19 risk scores to identify patients with COVID-19. In this study, for patient-wise analysis, three signatures, including the risk score using radiomic features only, the risk score using clinical factors only, and the risk score combining radiomic features and clinical variables, show an excellent performance in differentiating COVID-19 from other viral-induced pneumonias in the validation set. For lesion-wise analysis, the risk score using three radiomic features only also achieved an excellent AUC value. In contrast, the performance of 130 radiologists based on the chest CT images alone without the clinical characteristics included was moderate as compared to the risk scores developed. The risk scores depicting the correlation of CT radiomics and clinical factors with COVID-19 could be used to accurately identify patients with COVID-19, which would have clinically translatable diagnostic and therapeutic implications from a precision medicine perspective.

Entities:  

Keywords:  COVID-19; chest CT; coronavirus disease 2019; nomogram; radiomics; severe acute respiratory syndrome coronavirus 2

Year:  2021        PMID: 33713401     DOI: 10.18632/aging.202735

Source DB:  PubMed          Journal:  Aging (Albany NY)        ISSN: 1945-4589            Impact factor:   5.682


  3 in total

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

Authors:  Hengfeng Shi; Zhihua Xu; Weiqun Ao; Jian Wang; Guohua Cheng; Hongli Ji; Linyang He; Juan Zhu; Hanjin Hu; Zongyu Xie
Journal:  Eur J Med Res       Date:  2022-01-25       Impact factor: 2.175

2.  Multicenter study evaluating one multiplex RT-PCR assay to detect SARS-CoV-2, influenza A/B, and respiratory syncytia virus using the LabTurbo AIO open platform: epidemiological features, automated sample-to-result, and high-throughput testing.

Authors:  Hsing-Yi Chung; Ming-Jr Jian; Chih-Kai Chang; Jung-Chung Lin; Kuo-Ming Yeh; Ya-Sung Yang; Chien-Wen Chen; Shan-Shan Hsieh; Sheng-Hui Tang; Cherng-Lih Perng; Feng-Yee Chang; Kuo-Sheng Hung; En-Sung Chen; Mei-Hsiu Yang; Hung-Sheng Shang
Journal:  Aging (Albany NY)       Date:  2021-12-12       Impact factor: 5.682

3.  A CT-based radiomics nomogram for the differentiation of pulmonary cystic echinococcosis from pulmonary abscess.

Authors:  Yan Li; Yaohui Yu; Qian Liu; Haicheng Qi; Shan Li; Juan Xin; Yan Xing
Journal:  Parasitol Res       Date:  2022-10-01       Impact factor: 2.383

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.