Cureus · 2026
The MRI Spectrum of Gynecological Pelvic Masses.
СтатьяНазвание и аннотация: Перевод в очереди
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Chaudhary Shipra, Shukla Ashish K, Modi Bhoomi, Gupta Manisha, Mall Sachi, Singh Ranjeet
Аннотация
BACKGROUND: 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.
OBJECTIVE: To evaluate the role of MRI in the characterization and differentiation of gynecological pelvic masses, using histopathology as the gold standard wherever available.
MATERIALS AND METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
OBJECTIVE: To evaluate the role of MRI in the characterization and differentiation of gynecological pelvic masses, using histopathology as the gold standard wherever available.
MATERIALS AND METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.