ISSN: 1449-1907International Journal of Medical Sciences
Global reach, higher impact
Int J Med Sci 2018; 15(14):1771-1777. doi:10.7150/ijms.28687 This issue Cite
Research Paper
Development of a Model for the Prediction of Treatment Response of Uterine Leiomyomas after Uterine Artery Embolization
1. Department of Obstetrics and Gynecology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
2. Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
Abstract
Background: Uterine artery embolization (UAE) is one of the minimally-invasive alternatives to hysterectomy for treatment of uterine leiomyomas. There are various factors affecting the outcomes of UAE, but these have only been sporadically studied.
Study Objective: To identify factors associated with the efficacy of UAE for the treatment of uterine leiomyoma, and to develop a model for the prediction of treatment response of uterine leiomyomas to UAE.
Study design: A retrospective cohort study (Canadian Task Force Classification II-2)
Patients: One hundred ninety-eight patients with symptomatic uterine leiomyomas.
Intervention: UAE
Measurements and Main Results: Among 198 leiomyoma patients who were treated with UAE, 104 who underwent pelvic magnetic resonance imaging (MRI) with diffusion-weighted imaging were selected for developing prediction model. Variables that were statistically significant from the univariate analysis were: location of leiomyoma, total number of lesions, sum of leiomyomas diameters, T2 signal intensity of largest leiomyoma, and T2 leiomyoma:muscle ratio. After a logistic regression analysis, leiomyoma location and T2 signal intensity of the largest leiomyoma were found to be statistically significant variables. Using intramural myomas defined as controls, submucosal leiomyomas showed a greater response to UAE with an odds ratio of 7.6904. The odds ratio of T2 signal intensity with an increase in signal intensity of 10 was 1.093. Using these two variables, we developed a prediction model. The AUC in the prediction model was 0.833, and the AUC in the validation set was 0.791.
Conclusion: We identified that submucosal leiomyomas and those leiomyomas that show high signal intensity on T2-weighted imaging will exhibit a greater response to UAE. Prediction models are clinically helpful in selecting UAE as an appropriate treatment option for managing uterine leiomyoma.
Keywords: uterine artery embolization, leiomyoma, magnetic resonance imaging, decision modeling
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