Diagnostic Performance of MR Imaging-based Features and Texture Analysis in the Differential Diagnosis of Ovarian Thecomas/Fibrothecomas and Uterine Fibroids in the Adnexal Area.

RATIONALE AND OBJECTIVES

To investigate the value of MRI-based features and texture analysis (TA) in the differential diagnosis between ovarian thecomas/fibrothecomas (OTCA/f-TCAs) and uterine fibroids in the adnexal area (UF-iaas).

MATERIALS AND METHODS

This retrospective study included 16 OTCA/f-TCA and 37 UF-iaa patients who underwent conventional MRI and DWI between August 2014 and September 2018. Three-dimensional TA was performed with T2-weighted MRI. The clinical, MRI-based and texture features were compared between OTCA/f-TCAs and UF-iaas. Multivariate logistic regression analysis was used for filtering the independent discriminative features and constructing the discriminating model. ROCs were generated to analyse MRI-based features, texture features and their combination for discriminating between the two diseases.

RESULTS

Six imaging-based features (ipsilateral ovary detection, arterial period enhancement, lesion components, peripheral cysts, "whorl signs", mean ADCs) and six texture features (Histogram-energy, Histogram-entropy, Histogram-kurtosis, GLCM-energy, GLCM-entropy, and Haralick correlation) were significantly different between OTCA/f-TCAs and UF-iaas (p < 0.05). Multivariate analysis of the MRI-based features revealed that arterial period enhancement (OR = 0.104), peripheral cysts (OR = 16.513), and whorl signs (OR = 0.029) were independent features for discriminating between OTCA/f-TCAs and UF-iaas (p < 0.05). Multivariate analysis of the texture features showed that Histogram-energy and GLCM-energy were independent features for discriminating between OTCA/f-TCAs and UF-iaas (p < 0.05). The area under the curve of imaging-based diagnosis was 0.85, and the combination of imaging-based diagnosis and TA improved the area under the curve to 0.87, with higher accuracy, specificity and sensitivity of 86%, 92%, and 84%, respectively (p < 0.05).

CONCLUSIONS

MRI-based features can be useful in differentiating OTCA/f-TCAs from UF-iaas. Furthermore, combining imaging-based diagnosis and TA can improve diagnostic performance.