Hongmei Yue1,2,3, Qian Yu4, Chuan Liu5, Yifei Huang5, Zicheng Jiang6, Chuxiao Shao7, Hongguang Zhang8, Baoyi Ma9, Yuancheng Wang4, Guanghang Xie5, Haijun Zhang1, Xiaoguo Li1, Ning Kang1, Xiangpan Meng4, Shan Huang4, Dan Xu1, Junqiang Lei1, Huihong Huang6, Jie Yang7, Jiansong Ji7, Hongqiu Pan8, Shengqiang Zou8, Shenghong Ju4, Xiaolong Qi1. 1. CHESS-COVID-19 Group, CHESS Center, The First Hospital of Lanzhou University, Lanzhou, China. 2. Department of Respiratory Medicine, The First Hospital of Lanzhou University, Lanzhou, China. 3. Department of Pulmonary and Critical Care Medicine, The First Hospital of Lanzhou University, Lanzhou, China. 4. Department of Radiology, Jiangsu Key Laboratory of Molecular and Functional Imaging, Zhongda Hospital, Medical School, Southeast University, Nanjing, China. 5. Department of Gastroenterology, Zhujiang Hospital, Southern Medical University, Guangzhou, China. 6. CHESS-COVID-19 Group, Ankang Central Hospital, Ankang, China. 7. CHESS-COVID-19 Group, Zhejiang University Lishui Hospital & Lishui Central Hospital, Lishui, China. 8. Department of Infectious Diseases and Critical Care Medicine, The Affiliated Third Hospital of Jiangsu University, Zhenjiang, China. 9. Department of Respiratory Medicine, The People's Hospital of Linxia Hui Prefecture, Linxia, China.
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) has become a global challenge since the December 2019. The hospital stay is one of the prognostic indicators, and its predicting model based on CT radiomics features is important for assessing the patients' clinical outcome. The study aimed to develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with COVID-19 pneumonia. METHODS: This retrospective, multicenter study enrolled patients with laboratory-confirmed SARS-CoV-2 infection and their initial CT images from 5 designated hospitals in Ankang, Lishui, Lanzhou, Linxia, and Zhenjiang between January 23, 2020 and February 8, 2020. Patients were classified into short-term (≤10 days) and long-term hospital stay (>10 days). CT radiomics models based on logistic regression (LR) and random forest (RF) were developed on features from pneumonia lesions in first four centers. The predictive performance was evaluated in fifth center (test dataset) on lung lobe- and patients-level. RESULTS: A total of 52 patients were enrolled from designated hospitals. As of February 20, 21 patients remained in hospital or with non-findings in CT were excluded. Therefore, 31 patients with 72 lesion segments were included in analysis. The CT radiomics models based on 6 second-order features were effective in discriminating short- and long-term hospital stay in patients with COVID-19 pneumonia, 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 test. The LR and RF model showed a sensitivity and specificity of 1.0 and 0.89, 0.75 and 1.0 in test respectively. As of February 28, a prospective cohort of six discharged patients were all correctly recognized as long-term stay using RF and LR models. CONCLUSIONS: The machine learning-based CT radiomics features and models showed feasibility and accuracy for predicting hospital stay in patients with COVID-19 pneumonia. 2020 Annals of Translational Medicine. All rights reserved.
BACKGROUND: The coronavirus disease 2019 (COVID-19) has become a global challenge since the December 2019. The hospital stay is one of the prognostic indicators, and its predicting model based on CT radiomics features is important for assessing the patients' clinical outcome. The study aimed to develop and test machine learning-based CT radiomics models for predicting hospital stay in patients with COVID-19 pneumonia. METHODS: This retrospective, multicenter study enrolled patients with laboratory-confirmed SARS-CoV-2 infection and their initial CT images from 5 designated hospitals in Ankang, Lishui, Lanzhou, Linxia, and Zhenjiang between January 23, 2020 and February 8, 2020. Patients were classified into short-term (≤10 days) and long-term hospital stay (>10 days). CT radiomics models based on logistic regression (LR) and random forest (RF) were developed on features from pneumonia lesions in first four centers. The predictive performance was evaluated in fifth center (test dataset) on lung lobe- and patients-level. RESULTS: A total of 52 patients were enrolled from designated hospitals. As of February 20, 21 patients remained in hospital or with non-findings in CT were excluded. Therefore, 31 patients with 72 lesion segments were included in analysis. The CT radiomics models based on 6 second-order features were effective in discriminating short- and long-term hospital stay in patients with COVID-19 pneumonia, 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 test. The LR and RF model showed a sensitivity and specificity of 1.0 and 0.89, 0.75 and 1.0 in test respectively. As of February 28, a prospective cohort of six discharged patients were all correctly recognized as long-term stay using RF and LR models. CONCLUSIONS: The machine learning-based CT radiomics features and models showed feasibility and accuracy for predicting hospital stay in patients with COVID-19 pneumonia. 2020 Annals of Translational Medicine. All rights reserved.
Authors: Joost J M van Griethuysen; Andriy Fedorov; Chintan Parmar; Ahmed Hosny; Nicole Aucoin; Vivek Narayan; Regina G H Beets-Tan; Jean-Christophe Fillion-Robin; Steve Pieper; Hugo J W L Aerts Journal: Cancer Res Date: 2017-11-01 Impact factor: 12.701
Authors: Nabil Elshafeey; Aikaterini Kotrotsou; Ahmed Hassan; Nancy Elshafei; Islam Hassan; Sara Ahmed; Srishti Abrol; Anand Agarwal; Kamel El Salek; Samuel Bergamaschi; Jay Acharya; Fanny E Moron; Meng Law; Gregory N Fuller; Jason T Huse; Pascal O Zinn; Rivka R Colen Journal: Nat Commun Date: 2019-07-18 Impact factor: 14.919