Abstract

In mucinous ovarian tumors, preoperative prediction of histological subgroup is important for treatment approach. Therefore, we aimed to determine salient magnetic resonance imaging (MRI) findings and estimate optimal cut off values for quantitative features in differential diagnosis of benign, borderline and malignant mucinous ovarian tumors. Between January 2011 and December 2021, preoperative MRI scans of 50 patients with mucinous ovarian tumors (n = 54) were evaluated retrospectively. MRI findings [size, signal intensity, contrast pattern, features of loculation, wall, septa and mural nodule (MN), diffusion restriction] were investigated. There were benign, borderline, and malignant groups based on histopathological results. The relationship between radiological and histopathological results was analyzed by performing Kruskal Wallis test, Pearson's chi-squared test, receiver operating characteristic analysis. In our study, there were 54 mucinous ovarian tumors in 50 patients. Of 54, 33 were benign, 13 borderline and eight malignant tumors. In comparison of three groups, tumor size, number of loculation, number and frequency of MN were higher and apparent diffusion coefficient (ADC) value were lower in malignant group (p < 0.05). Septa thickness was lower with optimal cut off value of 2.45mm in benign group compared to borderline and malignant groups [sensitivity: 79%, specificity: 75%, AUC (Area under the curve): 0.861] (p < 0.05). T2-weighted (T2-w) signal intensity ratio (SIR) of MN was higher in borderline compared to malignant group, with a cut-off value of 3.9 (sensitivity: 85%, specificity: 83%, AUC: 0.943) (p < 0.05). Ascites was also significant in malignant group (p < 0.05). T2-w SIR of MN with a cut off value of 3.9 is beneficial for differential diagnosis. By awareness of some salient MRI findings (size, septa thickness, number of loculation, number and T2-w SIR of MN, ADC value and ascites), preoperative prediction of histological subgroup of mucinous tumors for appropriate treatment planning is possible.

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