New Strategies For Detecting Early Stage Of Ovarian Cancer

Abstract

Ovarian cancer remains one of the most lethal gynecological malignancies worldwide due to its late diagnosis and poor prognosis. Detecting ovarian cancer at an early stage significantly improves survival rates, yet current screening methods are insufficient for widespread clinical adoption. Recent advancements in molecular biology, imaging techniques, and liquid biopsy have provided new avenues for the early detection of ovarian cancer. This review discusses emerging strategies, including biomarkers from blood, urine, and tissue, advanced imaging modalities, artificial intelligence-driven diagnostic tools, and multi-omics approaches. The role of CA-125, HE4, microRNAs, and circulating tumor DNA is highlighted as promising biomarkers. Cutting-edge imaging technologies such as enhanced transvaginal ultrasound, MRI, and PET-CT offer improved accuracy for early-stage detection. Additionally, integrating artificial intelligence and machine learning enables better risk stratification and diagnostic precision. Multi-omics approaches, combining genomics, proteomics, and metabolomics, hold great promise for identifying novel signatures of early ovarian cancer. Challenges such as limited specificity, accessibility, and validation of new tools are also addressed, with an emphasis on future directions to improve screening methodologies. This comprehensive review aims to consolidate current knowledge and provide a pathway toward implementing reliable and effective early detection strategies for ovarian cancer.