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Abstract
Background: Ovarian cancer remains one of the most lethal gynecologic malignancies, particularly in low-resource settings where early detection is challenging. CA-125 is the most widely used biomarker in evaluating suspected ovarian cancer, yet its interpretation is influenced by demographic factors such as age and menopausal status.
Objective: To assess the association between serum CA-125 levels, age, and menopausal status in women clinically suspected of having ovarian cancer, and to evaluate the diagnostic performance of CA-125 in this population.
Methods: This retrospective study included 200 women aged 31–65 years who presented with suspected ovarian pathology at Bahawalpur Victoria Hospital. Medical records were reviewed for age, menopausal status, CA-125 levels, risk categorization, and confirmed ovarian cancer diagnosis. CA-125 was measured using CLIA, with >35 U/mL considered elevated. Statistical analyses included Chi-square tests, ANOVA, and logistic regression, with significance set at p < 0.05.
Results: Elevated CA-125 was observed in 70.5% of participants. Postmenopausal women had significantly higher CA-125 levels and were more frequently categorized as high-risk (p < 0.001). CA-125 showed strong associations with both high-risk category (p < 0.001) and confirmed ovarian cancer (χ² = 104.375, p < 0.001); all cancer-positive patients had elevated CA-125. CA-125 levels increased progressively with age, with the highest values observed in the 51–60 and >60 years groups (p < 0.001). Logistic regression identified elevated CA-125 as the strongest predictor of suspected ovarian cancer, while menopausal status and age showed no significant independent effect.
Conclusion: CA-125 remains a valuable biomarker for evaluating suspected ovarian cancer, particularly among postmenopausal and older women. However, age- and menopause-related variations in CA-125 highlight the need for context-specific interpretation. These findings provide important local reference data and support the development of refined diagnostic strategies for resource-limited settings.