The MRI Spectrum of Gynecological Pelvic Masses

Gynecological pelvic masses comprise a wide spectrum of benign and malignant conditions. Accurate preoperative characterization is crucial for optimal management. Although ultrasonography is the initial imaging modality, magnetic resonance imaging (MRI) provides superior soft-tissue characterization and multiplanar assessment.

To evaluate the role of MRI in the characterization and differentiation of gynecological pelvic masses, using histopathology as the gold standard wherever available.

This observational study included 60 patients with gynecological pelvic masses detected clinically or on ultrasonography who subsequently underwent MRI pelvis. MRI sequences included T1-weighted, T2-weighted, diffusion-weighted, and contrast-enhanced imaging. Lesions were assessed for site of origin, morphology, internal characteristics, and extent, and MRI findings were correlated with histopathological diagnoses.

Benign lesions constituted the majority of cases. Leiomyoma was the most common pathology, observed in 22 patients (36.7%), followed by adenomyosis in 12 patients (20.0%) and ovarian cysts (simple or hemorrhagic) in 11 patients (18.3%). Other benign lesions included dermoid cysts and tubal pathologies such as hydrosalpinx. Malignant lesions were identified in a smaller proportion of patients, with ovarian carcinoma being the most frequent malignancy (four cases, 6.7%), followed by endometrial carcinoma (three cases, 5.0%) and cervical carcinoma (one case, 1.7%). MRI correctly identified all ovarian lesions, including ovarian carcinomas, cysts, and dermoid cysts, as well as all cases of endometrial and cervical carcinoma. Overall, MRI demonstrated a sensitivity of 100%, specificity of 96.2%, and diagnostic accuracy of 96.7% in differentiating benign from malignant lesions.

MRI is a highly accurate and reliable imaging modality for the evaluation of gynecological pelvic masses, offering excellent lesion characterization and differentiation, thereby significantly aiding clinical decision-making and preoperative planning.