Multiparametric quantitative magnetic resonance imaging of uterine fibroids for prediction of growth rate-a pilot study.

Abstract

Uterine fibroid (UF) growth rate and future morbidity cannot be predicted. This can lead to sub-optimal clinical management, with women being lost to follow-up and later presenting with severe disease that may require hospitalization, transfusions, and urgent surgical interventions. Multi-parametric quantitative magnetic resonance imaging (MRI) could provide a biomarker to predict growth rate facilitating better-informed disease management and better clinical outcomes. We assessed the ability of putative quantitative and qualitative MRI predictive factors to predict UF growth rate. Twenty women with UFs were recruited and completed baseline and follow-up MRI exams, 1-2.5 years apart. The subjects filled out symptom severity and health-related quality of life questionnaires at each visit. A standard clinical pelvic MRI non-contrast exam was performed at each visit, followed by a contrast-enhanced multi-parametric quantitative MRI (mp-qMRI) exam with T2, T2*, and apparent diffusion coefficient (ADC) mapping and dynamic contrast-enhanced MRI. Up to 3 largest fibroids were identified and outlined on the T2-weighted sequence. Fibroid morphology and enhancement patterns were qualitatively assessed on dynamic contrast-enhanced MRI. The UFs' volumes and average T2, T2*, and ADC values were calculated. Pearson correlation coefficients were calculated between UF growth rate and T2, T2*, ADC, and baseline volume. Multiple logistic regression and receiver operating characteristic (ROC) analysis were performed to predict fast-growing UFs using combinations of up to 2 significant predictors. A significance level of alpha =0.05 was used. Forty-four fibroids in 20 women had growth rate measurement available, and 36 fibroids in 16 women had follow-up quantitative MRI available. The distribution of fibroid growth rate was skewed, with approximately 20% of the fibroids exhibiting fast growth (>10 cc/year). However, there were no significant changes in median baseline and follow-up values of symptom severity and health-related quality of life scores. There was no change in average T2, T2*, and ADC at follow-up exams and there was a moderate to strong correlation to the fibroid growth rate in baseline volume and average T2 and ADC in slow-growing fibroids (<10 cc/year). A multiple logistic regression to identify fast growing UFs (>10 cc/year) achieved an area under the curve (AUC) of 0.80 with specificity of 69% at 100% sensitivity. The mp-qMRI parameters T2, ADC, and UF volume obtained at the time of initial fibroid diagnosis may be able to predict UF growth rate. Mp-qMRI could be integrated into the management of UFs, for individualized care and improved clinical outcomes.