Radiomics models based on enhanced CT for identifying renal clear cell carcinoma and non-clear cell carcinoma
10.13929/j.1003-3289.201901099
- Author:
Ping WANG
1
Author Information
1. CT-MRI Division, Affiliated Hospital of Hebei University
- Publication Type:Journal Article
- Keywords:
Kidney neoplasms;
Radiomics;
Tomography, X-ray computed
- From:
Chinese Journal of Medical Imaging Technology
2019;35(11):1689-1692
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To establish radiomics models based on enhanced CT, and to explore the value of the models for distinguishing renal clear cell carcinoma (ccRCC) and non-clear cell renal cell carcinoma (non-ccRCC). Methods: Totally 147 patients with ccRCC and 32 patients with non-ccRCC were randomly divided into training set (n=125) and testing set (n=54). Enhanced CT data were imported into ITK-SNAP software, and ROI was manually delineated to obtain 16 features. Random Forest (RF) model and Logistic Regression (LR) model based on features were established, respectively. ROC curve was used to observe the diagnostic efficiency of the models for ccRCC. Results: In the training set, RF model diagnosed ccRCC with AUC of 0.96 (P<0.05) specificity of 1.00, and sensitivity of 0.83, while LR model diagnosed ccRCC with AUC of 0.96 (P<0.05), specificity of 1.00, and sensitivity of 0.83. In the testing set, RF model diagnosed ccRCC with AUC of 0.96 (P<0.05), specificity of 1.00, and sensitivity of 0.89, while LR model diagnosed ccRCC with AUC of 0.88(P<0.05), specificity of 0.90, and sensitivity of 0.77. Conclusion: Radiomics models based on enhanced CT can be used for identifying ccRCC from non-ccRCC. RF model has higher diagnostic value than LR model.
