Original Article
Renal cell carcinoma: preoperative evaluate the grade of histological malignancy using volumetric histogram analysis derived from magnetic resonance diffusion kurtosis imaging
Abstract
Background: To investigate the value of histogram analysis of magnetic resonance (MR) diffusion kurtosis imaging (DKI) in the assessment of renal cell carcinoma (RCC) grading before surgery.
Methods: A total of 73 RCC patients who had undergone preoperative MR imaging and DKI were classified into either a low- grade group or a high-grade group. Parametric DKI maps of each tumor were obtained using in-house software, and histogram metrics between the two groups were analyzed. Receiver operating characteristic (ROC) curve analysis was used for obtaining the optimum diagnostic thresholds, the area under the ROC curve (AUC), sensitivity, specificity and accuracy of the parameters.
Results: Significant differences were observed in 3 metrics of ADC histogram parameters and 8 metrics of DKI histogram parameters (P<0.05). ROC curve analyses showed that Kapp mean had the highest diagnostic efficacy in differentiating RCC grades. The AUC, sensitivity, and specificity of the Kapp mean were 0.889, 87.9% and 80%, respectively.
Conclusions: DKI histogram parameters can effectively distinguish high- and low- grade RCC. Kapp mean is the best parameter to distinguish RCC grades.
Methods: A total of 73 RCC patients who had undergone preoperative MR imaging and DKI were classified into either a low- grade group or a high-grade group. Parametric DKI maps of each tumor were obtained using in-house software, and histogram metrics between the two groups were analyzed. Receiver operating characteristic (ROC) curve analysis was used for obtaining the optimum diagnostic thresholds, the area under the ROC curve (AUC), sensitivity, specificity and accuracy of the parameters.
Results: Significant differences were observed in 3 metrics of ADC histogram parameters and 8 metrics of DKI histogram parameters (P<0.05). ROC curve analyses showed that Kapp mean had the highest diagnostic efficacy in differentiating RCC grades. The AUC, sensitivity, and specificity of the Kapp mean were 0.889, 87.9% and 80%, respectively.
Conclusions: DKI histogram parameters can effectively distinguish high- and low- grade RCC. Kapp mean is the best parameter to distinguish RCC grades.
