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Published on: January 7, 2018
Magnetic Resonance Signal Intensity Ratio Measurement Before Uterine Artery Embolization: Ability to Predict Fibroid
Stevo Duvnjak1,2, Pernille Ravn3, Anders Green4
1Department of Radiology, Odense University Hospital, Sdr. Boulevard 29, 5000, Odense, Denmark. duvnjak.stevo@gmail.com.
The T2-weighted MRI signal intensity ratio between uterine fibroids and surrounding muscle can predict significant fibroid volume reduction after uterine fibroid embolization (UFE). This ratio may help select patients for UFE.
Area of Science:
- Radiology
- Interventional Radiology
- Women's Health
Background:
- Uterine fibroids are common benign tumors affecting women of reproductive age.
- Uterine fibroid embolization (UFE) is an effective minimally invasive treatment for symptomatic fibroids.
- Predicting treatment response is crucial for patient selection and management.
Purpose of the Study:
- To determine if the magnetic resonance imaging (MRI) signal intensity (SI) ratio between dominant fibroids and adjacent striated muscle can predict >50% fibroid volume reduction post-UFE.
- To evaluate the predictive value of pre-UFE SI ratios on different MRI sequences for fibroid volume changes.
Main Methods:
- Prospective study of 52 patients undergoing UFE from October 2013 to May 2016.
- Calculation of fibroid-to-iliac muscle SI ratio on T1-, T2-, and T1 post-contrast-weighted MRI sequences before UFE.
- Measurement of dominant fibroid volume before and after UFE (three-month follow-up).
Main Results:
- A significant correlation was found between the T2-weighted fibroid-to-muscle SI ratio and fibroid volume reduction (p < 0.002).
- The T2-weighted SI ratio showed a positive correlation (r=0.439, p < 0.003) and an AUC of 0.776.
- No significant correlation was observed for T1-weighted or T1 post-contrast SI ratios.
Conclusions:
- The pre-UFE fibroid-to-muscle SI ratio on T2-weighted MRI sequences is a significant predictor of fibroid volume reduction after UFE.
- This imaging biomarker may aid in selecting appropriate candidates for UFE, optimizing treatment outcomes.
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