The Role of Ovarian–Reporting and Data System MRI Classification (MRI O-RADS) in the Evaluation of Ovarian Lesions | ||||
| The Egyptian Journal of Hospital Medicine | ||||
| Article 35, Volume 100, Issue 1, July 2025, Page 2769-2778 PDF (541.88 K) | ||||
| DOI: 10.21608/ejhm.2025.438118 | ||||
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| Abstract | ||||
| Background: Adnexal masses are a common occurrence in gynecological practice and differentiating between benign and malignant lesions is crucial for effective treatment. Magnetic resonance imaging (MRI) offers superior soft-tissue characterization compared to ultrasound. The Ovarian-Adnexal Reporting and Data System for MRI (O-RADS MRI) is a standardized approach launched to enhance risk categorization of adnexal masses. Its utility in daily clinical practice to predict malignancy is being increasingly recognized. Objectives: This study aimed to evaluate the diagnostic accuracy of O-RADS MRI classification in assessing ovarian lesions and correlate MRI findings with final histopathological results or after follow-up. Patients and methods: This prospective study included 50 female patients with ovarian lesions who were referred to the Department of Diagnostic and Interventional Radiology and Medical Imaging at Menoufia University Hospitals by the Department of Gynecology. The study was conducted over a one-year period starting in November 2023. Results: The mean age of patients was 44.04 ±17.51 years. Pelvic pain was the most common presenting symptom (70%), and the most common lesion was large solid portions without dark-dark features (40%). O-RADS MRI scores were distributed as follows: score 2 (14%), score 3 (18%), score 4 (38%), and score 5 (30%). Final pathological outcomes revealed 68% malignant and 32% benign lesions. ROC analysis yielded a sensitivity of 94%, specificity of 87%, accuracy of 92%, PPV of 88%, and NPV of 94% in predicting malignancy, area under the curve (ROC) is 0.949. Conclusions: It could be concluded that O-RADS MRI scoring system is a reliable tool with high diagnostic accuracy in differentiating malignant from benign ovarian masses. Its implementation can enhance decision-making and improve patient management. | ||||
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