Objectives To identify imaging predictors for complete necrosis after uterine artery embolisation (UAE) via quantitative measurement of the signal intensity obtained from magnetic resonance imaging (MRI) of a patient with adenomyosis. Methods TheMRIs of 119 patients with uterine adenomyosis, who underwent UAE, were retrospectively evaluated. Each lesion was classified based on its location and morphology on MRI. Thickness and signal intensity were measured in each adenomyosis and in the rectus muscle on the T2-weighted sagittal plane, and the T2-weighted signal intensity ratio (T2SR) was calculated. MR parameters were then compared in patients showing complete response that achieved complete necrosis and incomplete response after UAE via univariate and multivariate analysis. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic performance of the predictor using MR parameters for differentiating the complete from the incomplete response. Results The complete necrosis rate was 66.4 % (79/119) after UAE for adenomyosis. Univariate and multivariate analysis results indicated that T2SR was associated significantly with complete necrosis (P=0.012). Symptomatic adenomyosis with T2SR above 0.475 was associated with complete necrosis after UAE (sensitivity057.0, specificity070.0, area under the ROC curve [AUC]00.643). Conclusion T2SR of adenomyosis on pre-procedural MRI can be utilised as a predictor for early therapeutic response of UAE in adenomyosis. Key Points ̇ Pre-procedural MRI helps clinicians predict early response of UAE in adenomyosis. ̇ T2SR may help predict UAE outcomes in adenomyosis. ̇ Pre-procedural MRI helps clinicians to select treatment options in adenomyosis. ̇ MR predictors can be used to counsel patients with symptomatic adenomyosis.
Bibliographical noteFunding Information:
The authors have been supported by the National Cancer Center, South Korea (grant nos. 0910140-1 and 0910140-2). Forty of the 119 patients enrolled in the present study were the same patients described in a previously published paper .
All Science Journal Classification (ASJC) codes
- Radiology Nuclear Medicine and imaging