E1332. Quantitative MRI of Uterine Fibroids for Prediction of Growth Rate
  1. Milica Medved; The University of Chicago
  2. Carla Harmath; The University of Chicago
  3. Kirti Kulkarni; The University of Chicago
  4. Kevin Hellman; Northshore University Health System; The University of Chicago
  5. Sandra Laveaux; The University of Chicago
Uterine fibroid (UF) growth rate and future morbidity cannot currently be predicted. This often leads to sub-optimal clinical management, with women being lost to followup and later presenting with severe disease that can require hospitalization, transfusions, and urgent surgical interventions. Multi-parametric quantitative MRI (mp-qMRI) in women with non-severe presentation may potentially provide a biomarker for predicted growth rate, facilitating better-informed disease management and better clinical outcomes. We analyzed the ability of putative quantitative MRI predictive factors to predict UF growth rate.

Materials and Methods:
Twelve women (median age 41 years; range: 27 - 51 years) with UFs were identified during a standard-of-care clinical examination with ultrasound and recruited to receive a baseline and follow-up MRI examination at least 1 year apart. Standard clinical pelvic MRI (3T dStream Ingenia Philips) noncontrast sequences were performed, followed by the contrast-enhanced mp-qMRI examination that included T2, T2*, and ADC mapping. An experienced radiologist outlined up to 3 largest UFs on the T2-weighted sequence and they were then manually traced on the mp-qMRI sequences. The average T2, T2*, R2*, and ADC values over the UF ROIs were calculated. UF growth rate (UFGR) was calculated as the difference in UF volume divided by the number of days elapsed between the two examinations. The volumes were estimated as the area of the UF at the largest cross-section, raised to 3/2 power. Pearson’s coefficients of correlation between T2, T2*, ADC, and UF volume and UFGR, as well as between baseline and follow-up values of these quantitative measures, were calculated. Pearson’s coefficients of correlation between T2, T2*, and ADC and UF volume were also calculated. A significance level of alpha = 0.05 was used.

Twenty-five fibroids were outlined and analyzed (median 2.5 per woman; range 1 - 3). The correlation coefficient of baseline and follow-up values of T2, T2*, and ADC were 0.86, 0.69, and 0.58, respectively (p < 0.05 for all). T2, T2*, and ADC were not correlated to UF volume. Statistically significant correlation was observed between UF volume and UFGR (r = 0.54 p < 0.05) and T2 and UFGR (r = 0.45, p = 0.05). ADC and T2* did not correlate significantly with UFGR. Multiple regression to UFGR using T2 and UF volume achieved r = 0.69 (p < 0.05).

The mp-qMRI parameter values, especially T2, showed significant stability over time despite UF growth, and did not correlate with UF volume. T2* and ADC showed lower stability and correlation to UFGR, likely due to sensitivity to susceptibility gradients in areas adjacent to bowels. T2 values and UF size could serve as imaging biomarkers for prediction of UFGR, potentially allowing for more targeted follow-up and personalized management of fibroid disease. This could improve outcomes for affected women.