Diffusion-weighted and diffusion kurtosis imaging analysis of microstructural differences in clear cell renal cell carcinoma: a comparative study.

OBJECTIVE: To quantitatively compare the diagnostic values of conventional diffusion-weighted imaging (DWI) and diffusion kurtosis imaging (DKI) analysis of microstructural differences for clear cell renal cell carcinoma (CRCC). METHODS: A total of 108 patients with pathologically confirmed CRCC, including 38 Grade I, 37 Grade II, 18 Grade III and 15 Grade IV, were enrolled and divided into groups according to tumor grade [low grade (Ⅰ+Ⅱ, n = 75) and high grade (Ⅲ+Ⅳ, n = 33)]. Apparent diffusion coefficient (ADC), mean diffusivity (MD), mean kurtosis (MK), kurtosis anisotropy (KA) and radial kurtosis (RK) were performed. RESULTS: Both the ADC (r = -0.803) and MD (-0.867) values showed a negative correlation with tumor grading (p < 0.05) and MK (r = 0.812), KA (0.816) and RK (0.853) values a positive correlation with tumor grading (p < 0.05). Mean FA values showed no significant differences among CRCC grades (p > 0.05). ROC curve analyses showed that MD values had the highest diagnostic efficacy in differentiating low/high and Ⅱ/Ⅲ tumor grading. MD values gave AUC: 0.937 (0.896); sensitivity: 92.0% (86.5%); specificity: 78.8% (77.8%) and accuracy: 90.7% (87.3%). ADC performed worse than MD, MK, KA or RK (all p < 0.05) during pair-wise comparisons of ROC curves to show diagnostic efficacy. CONCLUSION: DKI analysis performs better than ADC in differentiating CRCC grading. ADVANCES IN KNOWLEDGE: Both the ADC and MD values correlated negatively with CRCC grading.The MK, KA and RK values correlated positively with CRCC grading.MD values had the highest diagnostic efficacy in differentiating low/high and Ⅱ/Ⅲ CRCC grading.

Combination of Hematoma Volume and Perihematoma Radiomics Analysis on Baseline CT Scan Predicts the Growth of Perihematomal Edema.

PURPOSE: The aim is to explore the potential value of CT-based radiomics in predicting perihematomal edema (PHE) volumes after acute intracerebral hemorrhage (ICH) from admission to 24 h. METHODS: A total of 231 patients newly diagnosed with acute ICH at two institutes were analyzed retrospectively. The patients were randomly divided into training (N = 117) and internal validation cohort (N = 45) from institute 1 with a ratio of 7:3. According to radiomics features extracted from baseline CT, the radiomics signatures were constructed. Multiple logistic regression analysis was used for clinical radiological factors and then the nomogram model was generated to predict the extent of PHE according to the optimal radiomics signature and the clinical radiological factors. The receiver operating characteristic (ROC) curve was used to evaluate the discrimination performance. The calibration curve and Hosmer-Lemeshow test were used to evaluate the consistency between the predicted and actual probability. The support vector regression (SVR) model was constructed to predict the overall value of follow-up PHE. The performance of the models was evaluated on the internal and independent validation cohorts. RESULTS: The perihematoma 5 mm radiomics signature (AUC: 0.875) showed good ability to discriminate the small relative PHE(rPHE) from large rPHE volumes, comparing to intrahematoma radiomics signature (AUC: 0.711) or perihematoma 10 mm radiomics signature (AUC: 0.692) on the training cohort. The AUC of the combined nomogram model was 0.922 for the training cohort, 0.945 and 0.902 for the internal and independent validation cohorts, respectively. The calibration curves and Hosmer-Lemeshow test of the nomogram model suggested that the predictive performance and actual outcome were in favorable agreement. The SVR model also predicted the overall value of follow-up rPHE (root mean squared error, 0.60 and 0.45; Pearson correlation coefficient, 0.73 and 0.68; P < 0.001). CONCLUSION: Among patients with acute ICH, the established nomogram and SVR model with favorable performance can offer a noninvasive tool for the prediction of PHE after ICH.

Comparative study of conventional ROI-based and volumetric histogram analysis derived from CT enhancement in differentiating malignant and benign renal tumors.

OBJECTIVE: To quantitatively compare the diagnostic values of conventional region of interest (ROI)-based and volumetric histogram analysis derived from CT enhancement in differentiating malignant and benign renal tumors. METHODS: A total of 230 patients with pathologically confirmed renal tumors who had undergone CT enhancement were classified into clear cell renal cell carcinoma (ccRCC) (n = 133), non-ccRCC (n = 56), and benign renal tumor(n = 41) group. Parametric CT enhancement of each tumor from volumetric histogram were obtained using in-house software, including 10th percentile, 25th percentile, median, 75th percentile, 90th percentile, mean, standard deviation, as well as skewness, kurtosis and entropy, and histogram metrics among these groups were analyzed. ROI-based enhancement density was also analyzed. RESULTS: The entropy and SD values of ccRCCs were higher than those of non-ccRCCs and benign renal tumors (p < 0.05). The 10th percentile, 25th percentile, median, 75th percentile, 90th percentile and mean values of ccRCCs were lower than those of benign renal tumors, however, higher than those of non-ccRCCs (p < 0.05). The ROI-based enhancement density of non-ccRCCs were lower than those of ccRCCs and benign renal tumors(p < 0.05). Receiver operating characteristic (ROC) curve analyses showed that entropy and mean values had the highest diagnostic efficacy in differentiating ccRCCs/non-ccRCCs and benign renal tumors. ROC curve analyses showed that mean values had the highest diagnostic efficacy in differentiating ccRCCs and non-ccRCCs. In terms of pairwise comparisons of ROC curves and diagnostic efficacy, ROI-based CT enhancement density was worse than volumetric histogram analysis (p < 0.05). CONCLUSION: Volumetric histogram analysis parameters can effectively distinguish malignant and benign renal tumors. ADVANCES IN KNOWLEDGE: 1. Entropy and mean values had the highest diagnostic efficacy in differentiating ccRCCs/ non-ccRCCs and benign renal tumors.2. Mean values had the highest diagnostic efficacy in differentiating ccRCCs and non-ccRCCs.3.Volumetric histogram analysis had better performance than ROI-based enhancement density.