OVARIAN CANCER: DIAGNOSTIC STRATEGIES AND EARLY DETECTION - de Macedo, Ryan Rafael Barros; Vieira, Carolina Sena; Colassio, Amanda Peron; Baltazar Júnior, Orlando; Estrela, Sillas Abrantes; Bolzan, Amanda Camilla Schmidt; Brandão, Victória Cristina Magalhães; Silva, Maria Isabele dos Santos; Silva, Vitor de Castro; Marchesan, Luis Alexandre Lago; Palma, Júlia Coelho da Fonseca; Matias, Maryah Edwarda Natan Rodrigues (Seven Editora, 2026)
Ovarian cancer (OC) is one of the leading causes of mortality from gynecological neoplasms, largely due to late diagnosis and the high biological heterogeneity of tumors. Traditional screening methods, such as transvaginal ultrasound combined with serum CA-125, have significant limitations in sensitivity and specificity for early-stage detection. In this context, recent advances in molecular biomarkers, radiomics, and artificial intelligence (AI) have expanded the possibilities for diagnosis, prognosis, and monitoring. This study critically reviews current and emerging diagnostic strategies for ovarian cancer, emphasizing the integration of AI-based tools and multiomics approaches, important tools that stand out as a promising frontier for precision medicine in ovarian cancer, with the potential to improve early detection, guide therapeutic decisions, and positively impact patient survival. DOI: https://doi.org/10.56238/sevened2026.002-008