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

January 4, 2026pubmed logopapers

Authors

D'Amario A,Ambrosini R,Gullino A,Grazioli L

Affiliations (2)

  • Department of Diagnostic Imaging, ASST Spedali Civili di Brescia, P.le Spedali Civili, 1, 25123 Brescia, Italy.
  • Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, ASST Spedali Civili di Brescia, Università degli Studi di Brescia, P.le Spedali Civili, 1, 25123 Brescia, Italy.

Abstract

<b>Background</b>: Ovarian cancer is a leading gynecological malignancy with high global mortality. Early and accurate diagnosis is critical for optimal management; however, a considerable portion of ovarian masses remain indeterminate after initial evaluation. Although transvaginal ultrasound is the first-line imaging tool, up to 30% of cases yield inconclusive findings, complicating treatment decisions. <b>Methods</b>: This review summarizes current diagnostic strategies for ovarian masses, with an emphasis on advanced imaging and emerging technologies. Topics include the diagnostic utility of contrast-enhanced MRI, the application of the O-RADS MRI scoring system, and the integration of Artificial Intelligence (AI) into imaging workflows. <b>Results</b>: Contrast-enhanced MRI offers high diagnostic accuracy (83-93%) for characterizing indeterminate ovarian masses. The O-RADS MRI Score offers a reported sensitivity of 93% and specificity of 91% for malignancy risk assessment. Additionally, new classification systems have been proposed to further improve diagnostic performance. AI-based approaches, particularly machine learning and deep learning applied to imaging data, show potential in improving diagnostic precision; however, most techniques require further clinical validation before widespread adoption. <b>Conclusions</b>: Advanced imaging techniques and AI-driven methods are reshaping the diagnostic landscape of ovarian cancer. While current tools like MRI and O-RADS enhance accuracy, ongoing research into novel models and AI applications suggests further improvements are possible. Clinical validation and expert oversight remain essential for their integration into routine practice.

Topics

Journal ArticleReview

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