Rethinking ovarian cancer early detection: risk-adapted strategies, diagnostic pathways, and emerging biomarkers

Abstract

Ovarian cancer early detection remains a major challenge because most patients present with advanced disease, whereas population-wide screening in average-risk women has not shown a clear mortality benefit. This narrative review examines how early detection should be redefined in light of evolving disease biology, risk heterogeneity, and clinical pathway constraints. Recent evidence supports a shift from universal screening toward risk-adapted strategies that integrate hereditary susceptibility, epidemiologic and clinical risk factors, symptom-triggered triage, standardized imaging, and selected molecular biomarkers. Established tools such as CA125, HE4, O-RADS, and IOTA-based models are most valuable when embedded in structured diagnostic pathways rather than used as isolated tests. Emerging approaches, including cfDNA methylation assays, cervicovaginal molecular sampling, extracellular vesicle platforms, multimodal artificial intelligence, radiomics, and atlas-guided biomarker discovery, show promise but remain investigational and require external validation, intended-use evaluation, pathway integration, and implementation readiness. Overall, ovarian cancer early detection is moving toward a more biologically informed and clinically actionable framework in which technical innovation is judged by incremental clinical utility. The most credible future strategy is not broader testing, but smarter testing through risk-adapted design and pathway-defined deployment.