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Published on: January 17, 2019
Outcome prediction for symptomatic patients with fibroids who underwent uterine artery embolization
Qingxia Wu1, Mina Motaghi2, Hao Tang3
1Department of Medical Imaging, Henan Key Laboratory of Neurological Imaging, Henan Provincial People's Hospital, People's Hospital of Zhengzhou University, People's Hospital of Henan University, Zhengzhou, Henan, China; Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
Purpose:
To predict the clinical outcome of symptomatic patients with uterine leiomyomas who underwent uterine artery embolization (UAE), based on clinical and radiological features.
Methods:
Patients with symptomatic uterine leiomyomas who underwent UAE from March 2010 to September 2019 were consecutively included in this retrospective study. Patients with persistent or recurrent symptoms and those who needed repeated UAE, myomectomy, or hysterectomy following the initial treatment were considered to have a poor outcome after UAE. The total and enhancing volume of the dominant leiomyoma in each location and uterine volume were obtained before and after UAE. Univariate and multivariate Cox proportional hazard analyses were used to evaluate the parameters that could predict poor outcome.
Results:
A total of 116 patients (mean age, 45 ± 5 years) were included. Forty-six patients (46/116, 39.7%) showed poor outcome. Cox regression analysis showed higher hazard of poor outcome for younger patients vs. older patients (HR: 0.92, p-value: 0.01), patients with adenomyosis vs. patients without adenomyosis (HR: 2.47, p-value < 0.01), patients with adenomyosis thickness > 2.5 cm before UAE vs. patients without adenomyosis (HR: 4.2, p-value < 0.01) and for patients with intramural fibroid enhancement volume > 440 cm3 compared to patients with no intramural fibroids (p-value: 0.06). Multivariate Cox regression analysis including age, the thickness of adenomyosis, and intramural leiomyoma volume of enhancement before UAE was chosen as the best model to predict the outcome.
Conclusions:
Pretreatment clinical and MRI features could identify patients with a higher risk for poor outcome after UAE.
