Objective Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) histograms were used to investigate whether their parameters can distinguish between benign and malignant parotid gland tumors and further differentiate tumor subgroups. Materials and methods A total of 117 patients (32 malignant and 85 benign) who had undergone DCE-MRI for pretreatment evaluation were retrospectively included. Histogram parameters including mean, median, entropy, skewness, kurtosis and 10th, 90th percentiles were calculated from time to peak (TTP) (s), wash in rate (WIR) (l/s), wash out rate (WOR) (l/s), and maximum relative enhancement (MRE) (%) mono-exponential models. The Mann–Whitney U test was used to compare the differences between the benign and malignant groups. The diagnostic value of each significant parameter was determined on Receiver operating characteristic (ROC) analysis. Multivariate stepwise logistic regression analysis was used to identify the independent predictors of the different tumor groups. Results For both the benign and malignant groups and the comparisons among the subgroups, the parameters of TTP and MRE showed better performance among the various parameters. WOR can be used as an indicator to distinguish Warthin’s tumors from other tumors. Warthin’s tumors showed significantly lower values on 10th MRE and significantly higher values on skewness TTP and 10th WOR, and the combination of 10th MRE, skewness TTP and 10th WOR showed optimal diagnostic performance (AUC, 0.971) and provided 93.12% sensitivity and 96.70% specificity. After Warthin’s tumors were removed from among the benign tumors, malignant parotid tumors showed significantly lower values on the 10th TTP (AUC, 0.847; sensitivity 90.62%; specificity 69.09%; P < 0.05) and higher values on skewness MRE (AUC, 0.777; sensitivity 71.87%; specificity 76.36%; P < 0.05). Conclusion DCE-MRI histogram parameters, especially TTP and MRE parameters, show promise as effective indicators for identifying and classifying parotid tumors. Entropy TTP and kurtosis MRE were found to be independent differentiating variables for malignant parotid gland tumors. The 10th WOR can be used as an indicator to distinguish Warthin’s tumors from other tumors.

Background Preoperative imaging assessment of venous malformations (VMs) and prediction of foam sclerotherapy efficacy might be achievable by DCE‐MRI but elaborate quantitive analysis was absent. Purpose To evaluate the value of DCE‐MRI in predicting the effectiveness of foam sclerotherapy in VMs. Study Type Retrospective. Population Fifty‐five patients (M:F = 17:38; mean age ± SD, 15.4 ± 13.0 years) with VMs. Field Strength/Sequence Three Tesla MRI with 3D T1‐weighted volume interpolated body examination. Assessment Patients who underwent pretreatment DCE‐MRI were divided into “effective” and “ineffective” groups according to the response to foam sclerotherapy. Clinical characteristics and morphologic features were assessed. The semiquantitative parameters, such as maximum intensity time ratio (MITR), enhancement ratio (ER), and Slope, were obtained from ROI and volume of interest (VOI). The quartile and mean values of these parameters were acquired from VOI, while mean values denoted as Mean# were acquired from ROI. Establishment of two predictive models was based on ROI and VOI respectively. Model 1 was based on morphologic parameters and ROI semiquantitative parameters, while model 2 was based on morphologic parameters and VOI semiquantitative parameters. Statistical Analysis Mann–Whitney U‐test, Cohen's kappa, multivariate logistic regression analysis (backward stepwise), and ROC analyses. Results The lesion classification, presence of phlebolith, semiquantitative parameters of VOI (quartile and mean of MITR), and semiquantitative parameters of ROI (Slopemean#, MITRmean#) were significantly different between two groups. Lesion classification (P = 0.002) and MITRmean# (P = 0.027) were independent predictors for poor efficacy in model 1 as determined by multivariate binary logistic regression analysis. For model 2, lesion classification (P = 0.006) and MITR25 (P = 0.001) were independent predictors. The predictive model based on VOI (AUC = 0.961) performed better than that based on ROI (AUC = 0.909) in predicting therapeutic response. Data Conclusion DCE‐MRI is promising in predicting the response to foam sclerotherapy for VMs. The whole lesion VOI‐based model showed better performance and could instruct surgical approach in the future. Evidence Level 3 Technical Efficacy Stage 4