Diagnosis of ovarian masses with quantitative multi-parametric magnetic resonance Imaging at 3 Tesla.

University of Minnesota M.S. thesis. June 2012. Major: Clinical research. Advisor: Levi S. Downs, Jr. 1 computer file (PDF); vi, 25 pages.Purpose: To identify quantitative MRI parameters which are associated with ovarian malignancy. Materials and Methods: Women scheduled for surgical removal of a suspected ovarian mass underwent preoperative imaging of the pelvis with 3 Tesla MRI. Dynamic contrastenhanced (DCE) MRI with pharmacokinetic modeling, quantitative T2 mapping, and diffusion weighted imaging with quantitative mapping of the water diffusion parameters were performed. Regions of interest (ROIs) were drawn by a blinded radiologist and categorized as predominantly cystic or solid. Masses were histologically categorized as benign or malignant after surgery. Mean ROI values for all quantitative imaging parameters were compared between benign and malignant masses using generalized estimating equations. In addition, we compared the classification accuracy using a combination of histogram characteristics from T2 map ROIs to the classification accuracy for the ROI mean alone. Results: 34 women were included in the study (12 malignant, 22 benign). We observed significant differences in several DCE-MRI parameters between solid benign masses and solid malignant masses. Toft’s rate constant (kep) was most significant, with malignant masses being significantly higher than benign masses. Quantitative T2 values (p=0.003) and signal intensity on T2 weighted imaging (p=0.008) were also significantly higher in solid ROIs of malignant masses. A linear combination of the mean, standard deviation, skewness and kurtosis of T2 within solid regions provided better classification accuracy than the mean of T2 alone. Conclusion: Quantitative parameters from DCE-MRI and T2 mapping can independently differentiate benign from malignant ovarian masses