Uterine fibroids: correlations between MRI appearance and stiffness via magnetic resonance elastography

Rationale and objectives: Magnetic resonance elastography has proven to be a valuable tool in the diagnosis of liver fibrosis, breast and cervical cancer, but its application in uterine fibroids requires further characterization. The aim of the present study was to examine the relationship between uterine fibroid stiffness by MRE and MR imaging characteristics. Materials and methods: An IRB-approved, HIPAA compliant review was performed of prospectively collected pelvic MRI and 2D-MRE data in patients with symptomatic uterine fibroids (N = 102). T1 and T2 weighted pelvic MRI with gadolinium enhancement were performed. In a small patient subset, fibroid stiffness was assessed by both 2D and 3D MRE. Fibroid stiffness by modality or imaging characteristics was analyzed using one-way analysis of variance followed by Student t test. Results: Four fibroid groups were identified based on T2 appearance: Isointense (N = 7), bright (N = 6), dark with minimal heterogeneity (N = 69), and dark with substantial heterogeneity (N = 20). Mean fibroid stiffness was 4.81 +/- 2.12 kPa. Comparison of fibroid stiffness by T2 signal intensity showed that T2 bright fibroids were significantly less stiff than fibroids appearing T2 dark with minimal heterogeneity (mean stiffness difference = 2.38 kPa; p < 0.05) and T2 dark fibroids with substantial heterogeneity were significantly less stiff than T2 dark fibroids with minimal heterogeneity (mean difference = 1.25 kPa; p < 0.05). There was no significant association between fibroid stiffness and T1 signal characteristics or gadolinium enhancement. There was no significant difference in stiffness values obtained by either 2D vs. 3D MRE. Conclusions: These data suggest differences in fibroid stiffness are associated with different T2 imaging characteristics with less stiff fibroids being T2 bright and more stiff fibroids being T2 dark. Further studies are needed to determine if fibroid stiffness by MRE may serve as an imaging biomarker to help predict MR-guided treatment response.