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

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

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study
Creator	Yang, Jie Xu, Dan Huang, Shan Pan, Hongqiu Qi, Xiaolong Liu, Chuan Zhang, Hongguang Wang, Jitao Yue, Hongmei Analysis, Shengqiang Huang, Huihong Ji, Jiansong Jiang, Zicheng Ju, Ju, Shenghong Lei, Junqiang Ma, Baoyi Meng, Xiangpan Or, Shenghong Shao, Chuxiao Yu, Qian Wang, Yuancheng Xie, Guanghang Zou, Shengqiang
Source	MedRxiv
abstract	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.
has issue date	2020-03-03(xsd:dateTime)
bibo:doi	10.1101/2020.02.29.20029603
has license	medrxiv
sha1sum (hex)	b8bb4db131a25b1bbb30d4205b16dc9ef988d22f
schema:url	https://doi.org/10.1101/2020.02.29.20029603
resource representing a document's title	Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: A multicenter study
resource representing a document's body	covid:b8bb4db131a25b1bbb30d4205b16dc9ef988d22f#body_text
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