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Value of radiomics model based on enhanced computed tomography in risk grade prediction of gastrointestinal stromal tumors

  • Chu, Hairui1
  • Pang, Peipei2
  • He, Jian1
  • Zhang, Desheng1
  • Zhang, Mei3
  • Qiu, Yingying3
  • Li, Xiaofen3
  • Lei, Pinggui4
  • Fan, Bing5
  • Xu, Rongchun3
  • 1 Affiliated Central Hospital Huzhou University, Huzhou, 313000, China , Huzhou (China)
  • 2 GE Healthcare, Hangzhou, 310000, China , Hangzhou (China)
  • 3 Jiangxi Provincial People’s Hospital Affiliated to Nanchang University, Nanchang, 330006, China , Nanchang (China)
  • 4 The Affiliated Hospital of Guizhou Medical University, Guiyang, 550000, China , Guiyang (China)
  • 5 Huizhou Municipal Central Hospital, Huizhou, 516000, China , Huizhou (China)
Published Article
Scientific Reports
Springer Nature
Publication Date
Jun 08, 2021
DOI: 10.1038/s41598-021-91508-5
Springer Nature


To explore the application of computed tomography (CT)-enhanced radiomics for the risk-grade prediction of gastrointestinal stromal tumors (GIST). GIST patients (n = 292) confirmed by surgery or endoscopic pathology during June 2013–2019 were reviewed and categorized into low-grade (very low to low risk) and high-grade (medium to high risk) groups. The tumor region of interest (ROI) was depicted layer by layer on each patient’s enhanced CT venous phase images using the ITK-SNAP. The texture features were extracted using the Analysis Kit (AK) and then randomly divided into the training (n = 205) and test (n = 87) groups in a ratio of 7:3. After dimension reduction by the least absolute shrinkage and the selection operator algorithm (LASSO), a prediction model was constructed using the logistic regression method. The clinical data of the two groups were statistically analyzed, and the multivariate regression prediction model was constructed by using statistically significant features. The ROC curve was applied to evaluate the prediction performance of the proposed model. A radiomics-prediction model was constructed based on 10 characteristic parameters selected from 396 quantitative feature parameters extracted from the CT images. The proposed radiomics model exhibited effective risk-grade prediction of GIST. For the training group, the area under curve (AUC), sensitivity, specificity, and accuracy rate were 0.793 (95%CI: 0.733–0.854), 83.3%, 64.3%, and 72.7%, respectively; the corresponding values for the test group were 0.791 (95%CI: 0.696–0.886), 84.2%, 69.3%, and 75.9%, respectively. There were significant differences in age (t value: − 3.133, P = 0.008), maximum tumor diameter (Z value: − 12.163, P = 0.000) and tumor morphology (χ2 value:10.409, P = 0.001) between the two groups, which were used to establish a clinical prediction model. The area under the receiver operating characteristic curve of the clinical model was 0.718 (95%CI: 0.659–0.776). The proposed CT-enhanced radiomics model exhibited better accuracy and effective performance than the clinical model, which can be used for the assessment of risk grades of GIST.

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