Journal article

CT-based radiomics for predicting the rapid progression of coronavirus disease 2019 (COVID-19) pneumonia lesions

Bin Zhang, Ma-yi-di-Li Ni-jia-Ti, Ruike Yan, Nan An, Lv Chen, Shuyi Liu, Luyan Chen, Qiuying Chen, Minmin Li, Zhuozhi Chen, Jingjing You, Yuhao Dong, Zhiyuan Xiong, Shuixing Zhang

BRITISH JOURNAL OF RADIOLOGY | BRITISH INST RADIOLOGY | Published : 2021

Abstract

OBJECTIVES: To develop and validate a radiomic model to predict the rapid progression (defined as volume growth of pneumonia lesions > 50% within seven days) in patients with coronavirus disease 2019 (COVID-19). METHODS: Patients with laboratory-confirmed COVID-19 who underwent longitudinal chest CT between January 01 and February 18, 2020 were included. A total of 1316 radiomic features were extracted from the lung parenchyma window for each CT. The least absolute shrinkage and selection operator (LASSO), Relief, Las Vegas Wrapper (LVW), L1-norm-Support Vector Machine (L1-norm-SVM), and recursive feature elimination (RFE) were applied to select the features that associated with rapid progre..

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University of Melbourne Researchers