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

Autor: Ke Nie, Yunjiang Li, Yongguang Liu, Shirong Zhang, Rongjun Tang, Qinghua Deng, Yu Kuang, Zhongxian Ding, Zuhua Chen, Shenglin Ma, Xin Yu, Yanchun Hou, Qin Lin, Ke Zhang, Baoliang Wu, Lidan Zhang, Bin Yang, Yao Ren, Zhaobin Cai, Pengfei Zhou, Jiawei Li, Xiadong Li, Jiahao Wang
Rok vydání: 2021
Předmět:
Zdroj: Aging (Albany NY)
ISSN: 1945-4589
DOI: 10.18632/aging.202735
Popis: 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.
Databáze: OpenAIRE