The value of contrast-enhanced CT radiomics model in differentiating renal oncocytoma from chromophobe renal cell carcinoma
10.3969/j.issn.1002-1671.2025.03.021
- VernacularTitle:CT增强影像组学模型鉴别肾脏嗜酸细胞腺瘤与肾脏嫌色细胞癌的价值
- Author:
Ke LI
1
;
Yibing SHI
;
Xianxian LIANG
;
Hengliang ZHAO
;
Di GUO
Author Information
1. 徐州市中心医院影像科,江苏 徐州 221009
- Publication Type:Journal Article
- Keywords:
radiomics;
machine learning;
chromophobe renal cell carcinoma;
renal oncocytoma
- From:
Journal of Practical Radiology
2025;41(3):452-456
- CountryChina
- Language:Chinese
-
Abstract:
Objective To investigate the value of machine learning models based on contrast-enhanced CT radiomics in differentia-ting renal oncocytoma(RO)from chromophobe renal cell carcinoma(chRCC).Methods A total of 65 patients with RO and chRCC confirmed by pathology with complete clinical and imaging data were analyzed retrospectively.The patients were randomly divided into training set(n=45)and test set(n=20)according to a ratio of 7︰3.The tumor boundaries were delineated on the preoperative CT images using 3D Slicer software,and radiomics features were extracted using the Radiomics plugin.Univariate analysis,recursive fea-ture elimination(RFE),least absolute shrinkage and selection operator(LASSO)algorithms were used to select the best radiomics features.Three machine learning models were constructed on the training set and the grid search method was used to select the best combination of hyperparameters.The receiver operating characteristic(ROC)curve,calibration curve and decision curve were used to evaluate the performance of each machine learning model on the training set and test set.Results Random forest model,logistic regres-sion model and support vector machine model can better identify RO and chRCC.In the training set,the area under the curve(AUC)of random forest model and support vector machine model were 0.950[95%confidence interval(CI)0.901-0.998]and 0.955(95%CI 0.908-1.000),respectively,which were higher than the AUC of logistic regression model 0.882(95%CI 0.806-0.956).Statistical differences were found by DeLong test(P<0.05);In the test set,the AUC of random forest model,logistic regression model and support vector machine model were 0.876(95%CI 0.758-0.993),0.883(95%CI 0.768-0.997)and 0.883(95%CI 0.768-0.997),respectively.There was no significant statistical difference in the AUC of each model by DeLong test(P>0.05).The decision curve showed that all three models had significant net clinical benefits.Conclusion The machine learning model based on contrast-enhanced CT radiomics can effectively distinguish RO from chRCC.
