Radiomics analysis on COVID-19(PDF)
《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]
- Issue:
- 2020年第4期
- Page:
- 463-467
- Research Field:
- 医学影像物理
- Publishing date:
Info
- Title:
- Radiomics analysis on COVID-19
- Author(s):
- LIU Faming1; JIANG Guihua2; YANG Ning2; WEI Xiaoquan1; HUANG Xiaoxing1; GUAN Qin1
- 1. Department of Radiation, Xiaochang First People’s Hospital, Xiaogan 432900, China; 2. Department of Imaging, Guangdong Second Provincial General Hospital, Guangzhou 510317, China
- Keywords:
- Keywords: COVID-19; pneumonia; radiomics; texture feature; histogram feature; support vector machine
- PACS:
- R814.42；R563.1
- DOI:
- DOI:10.3969/j.issn.1005-202X.2020.04.012
- Abstract:
- Abstract: A classification method based on radiomics features of chest CT images is proposed to identify patients with COVID-19 and those with other pneumonias. The chest CT images of 90 patients with COVID-19 and 90 patients with other pneumonias are collected in the study, and the regions of interest of pneumonia are manually outlined. Then radiomics is used to extract the texture features and histogram features of the lesion regions, thereby obtaining the first-order radiomics feature vector of each sample. Finally, the texture features and histogram features are taken as inputs to construct a linear support vector machine model for classifying patients with COVID-19 and patients with other pneumonias. Ten-fold cross-validation is conducted 20 times for training and testing. For patients with COVID-19, correlation analysis (multiple comparison correction-Bonferroni correction, p<0.05/7) is also carried out to determine whether the textural features and histogram features are correlated with laboratory indexes of blood. The results show that the proposed method has excellent classification performances, with a classification accuracy up to 87.56%, a sensitivity of 82.78%, a specificity of 92.33% and an area under receiver operating characteristic curve of 0.939, which proves that the radiomics features of the two groups are highly distinguishable and that the proposed model can effectively identify and diagnose patients with COVID-19 and patients with other pneumonias. The correlation analysis results reveal that some texture features are positively correlated with white blood cell, neutrophils and C-reactive protein, and that there are some other texture features negatively relative with blood oxygen and neutrophils.
Last Update: 2020-04-29