Solidified mucinous tumor of the ovary presenting characteristic MRI finding

Evaluation of Selected MRI Parameters in the Differentiation of Mucinous Ovarian Carcinomas and Metastatic Ovarian Tumors

Introduction: Analysis of selected MRI parameters in initial MRI for the characterization of ovarian masses enables differentiation between mucinous ovarian carcinoma and metastatic ovarian tumors. Material and Methods: A prospective analysis of contrast-enhanced MRI of patients with suspected ovarian masses diagnosed in ultrasound and CT examination. Morphological criteria, such as the size of lesion, bilateral location, presence of "mille-feuille sign", so-called Seidman criteria, as well as the diffusion weighted imaging and dynamic contrast enhancement of each lesion, were evaluated. Patients were allocated into two groups; the first group contained patients with mucinous ovarian cancer, and the second group contained patients with metastatic ovarian tumors. Results: A total of 35 patients were enrolled into the study. Median age was 49 in the first group and 59 in the second group of patients (p = 0.04). In the first group, all patients (100%) had unilateral lesions, and in the second group, 94% had bilateral tumors (p < 0.000001). In the first group, a tumor size equal or greater than 10 cm was present in 80% of patients, and the same was true for 21% of patients in the second group. The mille-feuille sign was present in 30% of patients from the first group and in 64% of patients from the second group. In the first group of patients, TTP was 410 and Perf.Max Enhancement was 141; in the second group, they were, respectively, 154 and 167 (p = 0.0001 and p = 0.5). Median ADC values in the first group were significantly higher in the first group than in the second group (p < 0.01). Conclusions: Significant differences in TTP and ADC values as well as in Seidman criteria enable reliable differentiation between the analyzed groups of tumors.

Characterization of Primary Mucinous Ovarian Cancer by Diffusion-Weighted and Dynamic Contrast Enhancement MRI in Comparison with Serous Ovarian Cancer

(1) Background. The purpose of this study is to evaluate the diagnostic accuracy of a quantitative analysis of diffusion-weighted imaging (DWI) and dynamic contrast enhanced (DCE) MRI of mucinous ovarian cancer (MOC). It also aims to differentiate between low grade serous carcinoma (LGSC), high-grade serous carcinoma (HGSC) and MOC in primary tumors. (2) Materials and Methods. Sixty-six patients with histologically confirmed primary epithelial ovarian cancer (EOC) were included in the study. Patients were divided into three groups: MOC, LGSC and HGSC. In the preoperative DWI and DCE MRI, selected parameters were measured: apparent diffusion coefficients (ADC), time to peak (TTP), and perfusion maximum enhancement (Perf. Max. En.). ROI comprised a small circle placed in the solid part of the primary tumor. The Shapiro-Wilk test was used to test whether the variable had a normal distribution. The Kruskal-Wallis ANOVA test was used to determine the p-value needed to compare the median values of interval variables. (3) Results. The highest median ADC values were found in MOC, followed by LGSC, and the lowest in HGSC. All differences were statistically significant (p < 0.000001). This was also confirmed by the ROC curve analysis for MOC and HGSC, showing that ADC had excellent diagnostic accuracy in differentiating between MOC and HGSC (p < 0.001). In the type I EOCs, i.e., MOC and LGSC, ADC has less differential value (p = 0.032), and TTP can be considered the most valuable parameter for diagnostic accuracy (p < 0.001). (4) Conclusions. DWI and DCE appear to be very good diagnostic tools in differentiating between serous carcinomas (LGSC, HGSC) and MOC. Significant differences in median ADC values between MOC and LGSC compared with those between MOC and HGSC indicate the usefulness of DWI in differentiating between less and more aggressive types of EOC, not only among the most common serous carcinomas. ROC curve analysis showed that ADC had excellent diagnostic accuracy in differentiating between MOC and HGSC. In contrast, TTP showed the greatest value for differentiating between LGSC and MOC.

MRI of Borderline Epithelial Ovarian Tumors: Pathologic Correlation and Diagnostic Challenges

Borderline epithelial ovarian tumors are a distinct pathologic entity characterized by increased epithelial proliferation and nuclear atypia, but without frank stromal invasion. Borderline tumor (BT) is now considered to represent an intermediate phase in the stepwise progression from benign to malignant ovarian epithelial tumor. Since BTs commonly manifest at early stages in women of reproductive age and are associated with a good prognosis, making the correct diagnosis is important in determining whether a patient is a candidate for fertility-sparing surgery. There are six histologic BT subtypes (serous, mucinous, seromucinous, endometrioid, clear cell, and Brenner), and each has different MRI features, reflecting their unique histologic architectures. Radiologists should be aware of the MRI features that can suggest BTs. These features include a hyperintense papillary architecture with hypointense internal branching, which can be observed with serous and seromucinous BTs on T2-weighted images; aggregates of microcysts that have hypointensity on T2-weighted images and reticular enhancement on contrast-enhanced T2-weighted images, which can be seen with mucinous BTs; and moderately high signal intensity on diffusion-weighted images along with relatively high apparent diffusion coefficient values, which can be observed regardless of the histologic subtype. Nevertheless, because the imaging features of BTs overlap with those of many benign lesions (eg, cystadenoma and cystadenofibroma, decidualized endometriosis, and polypoid endometriosis) and malignant tumors (ovarian cancers and metastases), histologic confirmation is required for the final diagnosis. Special emphasis is placed on the MRI features of BTs, pathologic correlation, and the challenges related to diagnosis. ^©RSNA, 2022.