Multiparametric MRI for differentiation of borderline ovarian tumors from stage I malignant epithelial ovarian tumors using multivariate logistic regression analysis.

Objective: To assess the value of contrast-enhanced MRI, apparent diffusion coefficient (ADC) measurement, and CA-125 measurement for differentiating borderline ovarian tumors (BOTs) from stage I malignant epithelial ovarian tumors (MEOTs).

Material And Methods: This retrospective study included 43 patients with BOTs and 43 patients with stage I MEOTs who underwent contrast-enhanced MRI with DWI and CA-125 analysis. Two radiologists evaluated the MRI findings in consensus. Univariate and multivariate analyses were performed to detect the best predictor variables for MEOTs.

Results: Mixed cystic/solid and predominantly solid appearances, as well as thickened irregular septa, were more frequent in MEOTs. A papillary architecture and internal branching (PA&IB) pattern was more frequent in BOTs. MEOTs had thicker walls and septa, larger solid components, and higher CA-125 values. The mean ADC value of solid components (ADC) and minimum ADC value of whole lesions (ADC) were lower in MEOTs. Multivariate analysis revealed that ADC and maximum diameter of the solid components were independent indicators of MEOTs with an AUC, sensitivity, and specificity of 0.86, 81%, and 84%, respectively.

Conclusion: ADC and maximum diameter of solid components were useful for differentiating BOTs from MEOTs.