Sinapsos Podcast | Oncology

E4 - Imaging in Ovarian Cancer

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E4   |  14 min   |   Latest   |  Publication Link

  • Podcast based on: D’Amario, A.; Ambrosini, R.; Gullino, A.; Grazioli, L. Role of Imaging Techniques in Ovarian Cancer Diagnosis: Current Approaches and Future Directions. Cancers 2026, 18, 173. https://doi.org/10.3390/cancers18010173
    • Type: Review  |  Publication date: 04 January 2026

  • Summary: Ovarian cancer is a leading cause of death among gynecological malignancies. Standard ultrasound scans may not be conclusive, especially when ovarian masses are difficult to classify. This review highlights recent advances aimed at reducing diagnostic uncertainty. Contrast-enhanced MRI has demonstrated high accuracy in differentiating benign from malignant lesions, and the O-RADS MRI scoring system provides structured risk assessment with strong sensitivity and specificity. New classification methods are also being developed to further support clinical decision-making. In addition, artificial intelligence (AI) approaches, including machine learning and deep learning, are being tested to improve diagnostic precision by analyzing complex imaging data. Overall, the integration of advanced imaging with AI has the potential to substantially improve the evaluation and management of women with suspected ovarian cancer.

  • Keywords: ovarian cancer; Ultrasound (US); Computed Tomography (CT); Magnetic Resonance Imaging (MRI); O-RADS MRI Score; Artificial Intelligence (AI); radiomics

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    This podcast provides a synthetically generated voice summary and discussion of scientific publications. The views expressed do not represent the views of the original authors, journals, or publishers. This podcast uses AI-assisted summaries, so it may or may not introduce inaccuracies or omit important details. Listeners are strongly encouraged to consult the original publications or sources for full context and accuracy. This podcast is for educational and informational purposes only and does not constitute clinical advice, medical guidance, or recommendations. The creators of this podcast are not liable for any errors, omissions, or outcomes resulting from the use of the information provided.

     

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