E3362. Quantitative and Qualitative 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 can lead to suboptimal clinical management, with women being lost to follow-up, who later present with severe disease that can require hospitalization, transfusions, and urgent surgical interventions. Multiparametric quantitative MRI (mp-qMRI) in women with nonsevere presentation could potentially provide a biomarker for predicted growth rate, which could facilitate better-informed disease management and better clinical outcomes. We analyzed the ability of putative quantitative and qualitative MRI predictive factors to predict UF growth rate.

Materials and Methods:
Twenty-one women with UFs identified during a standard-of-care clinical examination with US were recruited and able to complete 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 and DCEMRI. 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. DCEMRI was evaluated qualitatively on fibroid morphology and enhancement pattern. 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 correlation coefficients between T2, T2*/R2*, ADC, UF volume and UFGR, as well as between baseline and follow-up values of the mp-qMRI measures, were calculated, with a significance level of alpha = 0.05. Multiple logistic regression and Receiver Operating Characteristics (ROC) analysis for prediction of fast-growing uterine fibroids (UFGR > 10 cc/year) using combinations of up to 2 significant predictors was performed.

There was a strong to moderate correlation between baseline and follow-up values for average T2 (r = 0.73, p < 0.01), ADC (r = 0.67, p < 0.01), T2* (r = 0.55, p < .01). The correlation between baseline and follow-up for R2* values was weaker (r = 0.29, p = 0.09). Multiple logistic regression to identify fast growing UF (UFGR > 10 cc/year) using baseline T2 weighted signal intensity and UF volume achieved an AUC of 0.82 (95% CI: 0.66 – 0.92) with 60% specificity at 100% sensitivity. The combination of baseline UF volume and presence of ADC rim signal performed similarly, with AUC = 0.80 (95% CI: 0.64 - 0.91) and specificity of 69% at 100% sensitivity.

The mp-qMRI parameters T2 and ADC showed significant stability over time, between baseline and follow-up images. T2 values and UF volume obtained at the time of initial fibroid diagnosis may be able to predict UFGR. Future studies should consider obtaining T2, ADC, and calculating UF volume to confirm if mp-qMRI can identify fast-growing UFs in a larger population.