Semi-quantitative contrast-enhanced MR analysis of indeterminate ovarian tumours: when to say malignancy?

Abstract

Objective: To evaluate the ability of dynamic post-contrast sequence to specify indeterminate ovarian masses with inconclusive MR features of malignancy. Since management is dramatically different, special focus on the ability to differentiate borderline from invasive malignancy was considered. Methods: 150 ovarian masses were detected by pelvic ultrasound in 124 patients. Masses had been considered for dynamic post-contrast MRI. We expressed the kinetic parameters (i.e. enhancement amplitude, time peak of maximal uptake and maximal slope) in the form of maximum relative enhancement percentage (MRE%), time of maximal peak of contrast uptake (T max ) and slope enhancement ratio (SER) curves. Histological findings were the gold standard of reference. Results: Malignant ovarian masses showed higher MRE% than benign and borderline masses (p , 0.001). T max was shorter for malignant than benign (p , 0.01) and borderline (p , 0.001) ovarian masses. SER curves were the most suggestive of malignancy with a specificity and accuracy of 85.7% and 84.7%, respectively. Conclusion: Dynamic contrast-enhanced MRI could be a specific sequence to differentiate ovarian masses with indeterminate MR morphology with a special discrimination for low potential from invasive ovarian malignancy. Advances in knowledge: The study evaluated the diagnostic performance of the individual parameters of dynamic post-contrast MR sequence in evaluating ovarian masses. Management divert between benign, borderline and invasive malignant masses; our work presented a cut-off value for the peak of contrast uptake of 120%, which helped in the differentiation between benign and malignant tumours; the SER curves with Type III (early washout) pattern that was indicative of invasive malignancy was more specific than borderline malignancy.

The value of dynamic contrast–enhanced MRI in characterizing complex ovarian tumors

“…TIC type showed a significant difference between malignant and borderline tumors, with a sensitivity, specificity, and accuracy of 83%, 75%, and 82%, respectively. Unfortunately, the three semi-quantitative parameters performed poorly and did not contribute to a differentiation of these two groups, which was inconsistent with some previous studies [9, 10, 13, 14]. The results were probably due to the diversity of samples and histopathological complexity of the tumors.…”

“…In theory, the EA was higher in malignant than in borderline tumors and higher in borderline tumors than in benign tumors, which was confirmed by several studies [9, 10, 13, 14]. However, our study showed that the mean EA of malignant tumors was slightly lower than in borderline tumors and higher than in benign tumors (267.4 ± 86.2 vs. 269.3 ± 70.0 and 220.2 ± 90.5) ( P  > 0.05), which was not in accordance with previous studies [9, 10, 13, 14]. Therefore, further work needs to be done to verify our present findings.…”

“…For instance, mucinous borderline tumors have a lower EA and MS values and a higher THR value than other types of borderline tumors (195.9 ± 58.9 vs. 293.8 ± 57.3; 7.1 ± 5.9 vs. 8.9 ± 2.3; 42.3 ± 28.1 vs. 35.7 ± 8.8), respectively ( P  = 0.011, 0.586, 0.674). Only 4 (25%) mucinous borderline tumors were included in our study, versus 46%–50% of tumors in the studies of Thomassin-Naggara and Mansour [9, 14]. …”

“…The latest study including all histopathological types of ovarian masses showed that semi-quantitative DCE-MRI could discriminate malignant from borderline and benign tumors [14]. The studies that include only epithelial ovarian tumors showed similar results [9, 10].…”

“…Previous studies have shown the advantages and the availability of semi-quantitative DCE-MRI in characterizing ovarian tumors [9–14]. The latest study including all histopathological types of ovarian masses showed that semi-quantitative DCE-MRI could discriminate malignant from borderline and benign tumors [14].…”