Role of Imaging Techniques in Ovarian Cancer Diagnosis: Current Approaches and Future Directions

Background: Ovarian cancer is a leading gynecological malignancy with high global mortality. Early and accurate diagnosis is essential for effective management, yet many ovarian masses remain indeterminate after initial assessment. Transvaginal ultrasound is the first-line imaging tool, but up to 20% of cases yield inconclusive results, complicating treatment decisions. Methods: This review summarizes current diagnostic approaches for ovarian masses, with a focus on advanced imaging and emerging technologies. Key areas include the role of contrast-enhanced MRI, the O-RADS MRI scoring system, and the integration of Artificial Intelligence (AI) into imaging workflows. Results: Contrast-enhanced MRI offers high diagnostic accuracy (83–93%) for indeterminate ovarian masses. The O-RADS MRI Score shows a sensitivity of 93% and specificity of 91% in malignancy risk assessment. New classification systems have been proposed to further improve diagnostic performance. AI methods, particularly machine learning and deep learning applied to imaging data, show promise in enhancing diagnostic precision, though most require further clinical validation. Conclusions: Advanced imaging techniques and AI are reshaping the diagnostic landscape of ovarian cancer. While current tools like MRI and O-RADS improve accuracy, ongoing research into novel models and AI applications suggests further gains are possible. Clinical validation and expert oversight remain essential for their integration into routine practice.