Early Diagnosis of Ovarian Cancer: A Comprehensive Review of the Advances, Challenges, and Future Directions

Ovarian cancer (OC), the seventh most common cancer in women and the most lethal gynecological malignancy, is a significant global health challenge, with >324,000 new cases and >200,000 deaths being reported annually. OC is characterized by late-stage diagnosis, a poor prognosis, and 5-year survival rates ranging from 93% (early stage) to 20% (advanced stage). Despite advances in genomics and proteomics, effective early-stage diagnostic tools and population-wide screening strategies remain elusive, contributing to high mortality rates. The complex pathogenesis of OC involves diverse histological subtypes and genetic predispositions, including BRCA1/2 mutations; notably, a considerable proportion of OC cases have a hereditary component. Current diagnostic modalities, including imaging techniques (transvaginal ultrasound, computed/positron emission tomography, and magnetic resonance imaging) and biomarkers (CA-125 and human epididymis protein 4), with varying degrees of sensitivity and specificity, have limited efficacy in detecting early-stage OC. Emerging technologies, such as liquid biopsy, multiomics, and artificial intelligence (AI)-assisted diagnostics, may enhance early detection. Liquid biopsies using circulating tumor DNA and microRNAs are popular minimally invasive diagnostic tools. Integrated multiomics has advanced biomarker discovery. AI algorithms have improved imaging interpretation and risk prediction. Novel screening methods including organoids and multiplex panels are being explored to overcome current diagnostic limitations. This review highlights the critical need for continued research and innovation to enhance early diagnosis, reduce mortality, and improve patient outcomes in OC and posits personalized medicine, integrated emerging technologies, and targeted global initiatives and collaborative efforts, which address care access disparities and promote cost-effective, scalable screening strategies, as potential tools to combat OC.