Abstract
PurposeThis study was to investigate the role of different radiomics models with enhanced computed tomography (CT) scan in differentiating low from high grade renal clear cell carcinomas.Materials and MethodsCT data of 190 cases with pathologically confirmed renal cell carcinomas were collected and divided into the training set and testing set according to different time periods, with 122 cases in the training set and 68 cases in the testing set. The region of interest (ROI) was delineated layer by layer.ResultsA total of 402 radiomics features were extracted for analysis. Six of the radiomic parameters were deemed very valuable by univariate analysis, rank sum test, LASSO cross validation and correlation analysis. From these six features, multivariate logistic regression model, support vector machine (SVM), and decision tree model were established for analysis. The performance of each model was evaluated by AUC value on the ROC curve and decision curve analysis (DCA). Among the three prediction models, the SVM model showed a high predictive efficiency. The AUC values of the training set and the testing set were 0.84 and 0.83, respectively, which were significantly higher than those of the decision tree model and the multivariate logistic regression model. The DCA revealed a better predictive performance in the SVM model that possessed the highest degree of coincidence.ConclusionRadiomics analysis using the SVM radiomics model has highly efficiency in discriminating high- and low-grade clear cell renal cell carcinomas.
Highlights
Clear cell renal cell carcinoma accounts for 70% of renal cancers [1]
Multivariate logistic regression model, support vector machine (SVM), and decision tree model were established for analysis
The performance of each model was evaluated by area under the operating curve (AUC) value on the receiver operating characteristics (ROC) curve and decision curve analysis (DCA)
Summary
Clear cell renal cell carcinoma (ccRCC) accounts for 70% of renal cancers [1]. Since the long-term survival of clear cell carcinoma patients correlates negatively to the Fuhrman grading [2,3,4], it is crucial to accurately grade clear cell carcinoma of the kidney as early as possible. In three logistic regression models of radiomics based on non-texture features, texture fraction and non-texture feature combined with texture fraction for identifying high- and low-grade ccRCCs [13], the area under the operating curve (AUC) values in the three models were 0.826, 0.878, and 0.843 for the training set and 0.671, 0.771, and 0.780 for the testing set, respectively. Some image features like tumor size (TS) and permeability surface-area product (PS) were helpful in differentiating high- from low-grade ccRCCs based on conventional CT studies, with the AUC of TS and PS of 0.7 [14]. Gene fragments and radiomics can be combined to establish a two-group model for differentiating ccRCC from non-clear cell RCC (non-ccRCC), with the AUC of the training set and testing set being 0.969 and 0.900, respectively [15]. In this study, three models including logistic regression, decision tree and support vector machine (SVM) were established and compared for ccRCC grading performance
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
