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 (ADCmean) and minimum ADC value of whole lesions (ADCmin) were lower in MEOTs. Multivariate analysis revealed that ADCmin 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: ADCmin and maximum diameter of solid components were useful for differentiating BOTs from MEOTs.

A predictive diagnostic model using multiparametric MRI for differentiating uterine carcinosarcoma from carcinoma of the uterine corpus.

PURPOSE: To construct a diagnostic model for differentiating carcinosarcoma from carcinoma of the uterus. MATERIALS AND METHODS: Twenty-six patients with carcinosarcomas and 26 with uterine corpus carcinomas constituted a derivation cohort. The following nine MRI features of the tumors were evaluated: inhomogeneity, predominant signal intensity, presence of hyper- and hypointense areas, conspicuity of tumor margin, cervical canal extension on T2WI, presence of hyperintense areas on T1WI, contrast defect area volume percentage, and degree of enhancement. Two predictive models-with and without contrast-were constructed using multivariate logistic regression analysis. Fifteen other patients with carcinosarcomas and 30 patients with carcinomas constituted a validation cohort. The sensitivity and specificity of each model for the validation cohort were calculated. RESULTS: Inhomogeneity, predominant signal intensity on T2WI, and presence of hyperintense areas on T1WI were significant predictors in the unenhanced-MRI-based model. Presence of hyperintensity on T1WI, contrast defect area volume percentage, and degree of enhancement were significant predictors in the enhanced-MRI-based model. The sensitivity/specificity of unenhanced MRI were 87/73 and 87/70% according to reviewer 1 and 2, respectively. The sensitivity/specificity of the enhanced-MRI-based model were 87/70% according to both reviewers. CONCLUSIONS: Our diagnostic models can differentiate carcinosarcoma from carcinoma of the uterus with high sensitivity and moderate specificity.