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Updated: Jul 19, 2026

Murine Model for Non-invasive Imaging to Detect and Monitor Ovarian Cancer Recurrence
Published on: November 2, 2014
Early detection of ovarian cancer: new technologies in pursuit of a disease that is neither common nor rare
Abstract:
Elimination of cancer in the 21st Century is likely to depend not only on more effective individualized treatment, but also upon earlier detection and prevention of different malignancies. Screening strategies for ovarian cancer have centered on the serum tumor marker CA 125, transvaginal sonography (TVS), or sequential use of the two modalities. A single determination of CA 125 is neither sufficiently sensitive nor specific to be used as an initial stage in screening. Specificity can be improved by monitoring CA 125 over time with an algorithm that estimates risk of ovarian cancer. Sensitivity of CA125 can be improved by use of multiple markers in combination. Gene expression array analysis, proteomics and lipomics are being utilized to identify markers that can be used in combination with CA 125 to detect >95% of early stage ovarian cancers. To maintain high specificity, values for different markers are being combined using novel approaches of neural network analysis and mixed multivariate analysis. Sequential use of multiple markers and TVS could provide a cost-effective strategy to detect a disease of intermediate prevalence.
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