The renal artery-aorta angle associated with renal artery plaque: a retrospective analysis based on CT.

PURPOSE: To investigate the relationship between renal artery anatomical configuration and renal artery plaque (RAP) based on 320-row CT. METHODS: The abdominal contrast-enhanced CT data from 210 patients was retrospectively analyzed. Among 210 patients, there were 118 patients with RAP and 92 patients with no RAP. The anatomical parameters between lesion group and control group were compared and analyzed by using t-test, χ2-test and logistic regression analysis. RESULTS: (1) There were statistical differences on age, hypertension, diabetes, hypertriglyceridemia and hypercholesterolemia between lesion group and control group. (2) The differences on the distribution and type and of RAP between lesion group and control group were statistically significant. The most common position was the proximal, and the most common type was calcified plaque. (3)There were significant statistical differences on the proximal diameter of renal artery and renal artery-aorta angle A between lesion group and control group. The differences on the other anatomical factors between two groups were not statistically significant. (4) The result of logistic regression analysis showed that right RAP was related to age, hypertension and right renal artery angle A (the AUC of ROC = 0.82), and left RAP was related to high serum cholesterol, age and left renal artery angle A(the AUC of ROC = 0.83). (5) The RAP was associated with renal artery-aorta angle A, but the differences on distribution, type stability of RAP between R1 (L1) group and R2 (L2) group were not statistically significant. CONCLUSIONS: The RAP was associated with age, hypertension, hypercholesterolemia and renal artery-aorta angle A. Adults which had the greater renal artery-aorta angle A and the other above risk factors may be at increased risk for RAP.

A CT-Based Radiomics Approach for the Differential Diagnosis of Sarcomatoid and Clear Cell Renal Cell Carcinoma.

This study was aimed at building a computed tomography- (CT-) based radiomics approach for the differentiation of sarcomatoid renal cell carcinoma (SRCC) and clear cell renal cell carcinoma (CCRCC). It involved 29 SRCC and 99 CCRCC patient cases, and to each case, 1029 features were collected from each of the corticomedullary phase (CMP) and nephrographic phase (NP) image. Then, features were selected by using the least absolute shrinkage and selection operator regression method and the selected features of the two phases were explored to build three radiomics approaches for SRCC and CCRCC classification. Meanwhile, subjective CT findings were filtered by univariate analysis to construct a radiomics model and further selected by Akaike information criterion for integrating with the selected image features to build the fifth model. Finally, the radiomics models utilized the multivariate logistic regression method for classification and the performance was assessed with receiver operating characteristic curve (ROC) and DeLong test. The radiomics models based on the CMP, the NP, the CMP and NP, the subjective findings, and the combined features achieved the AUC (area under the curve) value of 0.772, 0.938, 0.966, 0.792, and 0.974, respectively. Significant difference was found in AUC values between each of the CMP radiomics model (0.0001 ≤ p ≤ 0.0051) and the subjective findings model (0.0006 ≤ p ≤ 0.0079) and each of the NP radiomics model, the CMP and NP radiomics model, and the combined model. Sarcomatoid change is a common pathway of dedifferentiation likely occurring in all subtypes of renal cell carcinoma, and the CT-based radiomics approaches in this study show the potential for SRCC from CCRCC differentiation.

Clear cell renal cell carcinoma: CT-based radiomics features for the prediction of Fuhrman grade.

OBJECTIVES: To discriminate low grade (Fuhrman I/II) and high grade (Fuhrman III/IV) clear cell renal cell carcinoma (CCRCC) by using CT-based radiomic features. METHODS: 161 and 99 patients diagnosed with low and high grade CCRCCs from January 2011 to May 2018 were enrolled in this study. 1029 radiomic features were extracted from corticomedullary (CMP), and nephrographic phase (NP) CT images of all patients. We used interclass correlation coefficient (ICC) and the least absolute shrinkage and selection operator (LASSO) regression method to select features, then the selected features were constructed three classification models (CMP, NP and with their combination) to discriminate high and low grades CCRCC. These three models were built by logistic regression method using 5-fold cross validation strategy, evaluated with receiver operating characteristics curve (ROC) and compared using DeLong test. RESULTS: We found 11 and 24 CMP and NP features were independently significantly associated with the Fuhrman grades. The model of CMP, NP and Combined model using radiomic feature set showed diagnostic accuracy of 0.719 (AUC [area under the curve], 0.766; 95% CI [confidence interval]: 0.709-0.816; sensitivity, 0.602; specificity, 0.838), 0.738 (AUC, 0.818; 95% CI:0.765-0.838; sensitivity, 0.693; specificity, 0.838), 0.777(AUC, 0.822; 95% CI: 0.769-0.866; sensitivity, 0.677; specificity, 0.839). There were significant differences in AUC between CMP model and Combined model (P = 0.0208), meanwhile, the differences between CMP model and NP model, NP model and Combined model reached no significant (P = 0.0844, 0.7915). CONCLUSIONS: Radiomic features could be used as biomarker for the preoperative evaluation of the CCRCC Fuhrman grades.