Value of Dynamic Contrast-Enhanced (DCE) MRI in Predicting Response to Foam Sclerotherapy of Venous Malformations

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 T-1-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 (Slope(mean)(#), 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