Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study

Xiaolong Qi; Zicheng Jiang; QIAN YU; Chuxiao Shao; Hongguang Zhang; Hongmei Yue; Baoyi Ma; Yuancheng Wang; Chuan Liu; Xiangpan Meng; Shan Huang; Jitao Wang; Dan Xu; Junqiang Lei; Guanghang Xie; Huihong Huang; Jie Yang; Jiansong Ji; Hongqiu Pan; Shengqiang Zou; Shenghong Ju

Though there were slightly differences in CT scan parameters among centers, key features included in models were second-order, and focused on distribution, correlation and variance in gray level intensities, which described the relationship between voxels and hold quantitative information on the spatial heterogeneity of pneumonia lesions. 11, 12 Compared with first-order features, second-order features were not sensitive to absolute value and thus more robust. Moreover, the models showed satisfied AUCs more than 90% on both training and test process, which indicated that the models could be applied in a general situation.