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

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

Abstract Objectives To develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection. Design Cross-sectional Setting Multicenter Participants A total of 52 patients with laboratory-confirmed SARS-CoV-2 infection and their initial CT images were enrolled from 5 designated hospitals in Ankang, Lishui, Zhenjiang, Lanzhou, and Linxia between January 23, 2020 and February 8, 2020. As of February 20, patients remained in hospital or with non-findings in CT were excluded. Therefore, 31 patients with 72 lesion segments were included in the final analysis. Intervention CT radiomics models based on logistic regression (LR) and random forest (RF) were developed on features extracted from pneumonia lesions in training and inter-validation datasets. The predictive performance was further evaluated in test dataset on lung lobe- and patients-level. Main outcomes Short-term hospital stay (≤10 days) and long-term hospital stay (>10 days). Results The CT radiomics models based on 6 second-order features were effective in discriminating short- and long-term hospital stay in patients with pneumonia associated with SARS-CoV-2 infection, with areas under the curves of 0.97 (95%CI 0.83-1.0) and 0.92 (95%CI 0.67-1.0) by LR and RF, respectively, in the test dataset. The LR model showed a sensitivity and specificity of 1.0 and 0.89, and the RF model showed similar performance with sensitivity and specificity of 0.75 and 1.0 in test dataset. Conclusions The machine learning-based CT radiomics models showed feasibility and accuracy for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection.