ARRS 2022 Abstracts

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E1394. Quantitative MRI of Uterine Fibroids and Correlation With Fibroid Size: A Pilot Study
Authors
  1. Milica Medved; The University of Chicago
  2. Carla Harmath; The University of Chicago
  3. Kirti Kulkarni; The University of Chicago
  4. Maryellen Giger; The University of Chicago
  5. Kevin Hellman; Northshore University Health System; The University of Chicago
  6. Obianuju Madueke-Laveaux; The University of Chicago
Objective:
Uterine fibroid growth and future morbidity is currently unpredictable. This ambiguity often leads to women being lost to follow-up after initial diagnosis and only presenting when disease becomes severe, sometimes requiring hospitalization, transfusions, and urgent surgical interventions. Multi-parametric quantitative MRI (mp-qMRI) in women with non-severe presentation may provide a biomarker for predicted growth, allowing for better-informed disease management and better clinical outcomes. As a first step for identifying potential biomarkers, we analyzed the interdependence of putative predictive factors and their relationship to fibroid size.

Materials and Methods:
Fourteen women (median age 36 years; range: 24–49 years) with ultrasonographically confirmed fibroids were identified during a standard-of-care clinical examination and recruited. Standard clinical pelvic MRI (3 T dStream Ingenia Philips) noncontrast sequences were performed, followed by contrast-enhanced mp-qMRI that included T2, T2*, R2*, and ADC mapping. An experienced radiologist outlined uterine fibroids on the T2-weighted sequence; they were then manually traced on the quantitative mapping sequences. The average T2, T2*, R2*, and ADC values over the fibroid ROI were calculated. Pearson’s coefficients of correlation between fibroid size and T2, T2*, R2*, and ADC, as well as among these quantitative measures, were calculated. A cross-sectional area of 250 mm² was used as the limit between “small” and “large” fibroids. Metrics of mp-qMRI were compared for small and large fibroids using two-sided t-tests for two samples with unequal variances. To adjust for multiple comparisons, and accounting for the high correlation between R2* and T2*, the threshold for significance was set to p = 0.05 / 3 = 0.017.

Results:
Thirty-seven fibroids were outlined and analyzed (median 3 fibroids per woman; range 1–7); 38% (14/37) were “small” and 62% (23/37) were “large.” Statistically significant correlation was observed between fibroid size and T2 values (r = 0.47, p = 0.003) and fibroid size and ADC values (r = 0.47, p = 0.003). The correlation between the fibroid size and R2* (r = 0.15, p = 0.38) and T2* (r = 0.34, p = 0.037) was not significant after adjustment. Statistically significant correlations were also found between T2 and ADC (r = 0.63, p < 0.001), T2 and T2* (r = 0.55, p < 0.001), and R2* and T2* values (r = 0.79, p < 0.001). Large fibroids had significantly higher T2 (p = 0.0017) and ADC (p = 0.015) values than small fibroids.

Conclusion:
T2 and ADC values are higher in larger fibroids and correlate with fibroid size. Prospective 12–24 month follow-up exams will help establish whether the observed differences are a cause or result of more pronounced growth. As T2 and ADC correlate with water vs. collagen content in tissue – thought to influence fibroid growth – our results point to a possibility of defining imaging biomarkers predictive of fibroid growth. Such imaging biomarkers could allow more targeted follow-up and personalized management of fibroid disease, improving outcomes for affected women.