Organoids and AI-integrated models in ovarian cancer research: the future of personalized therapy

Abstract

Ovarian cancer (OC) is the deadliest gynecological malignancy which continues to impose a significant challenge in the field of oncology research due to its rapid metastasis, inter and intra-tumor heterogeneity, and treatment resistance. Personalized medicine for OC is still under development where patient-derived organoids (PDO) represent as a promising platform intended towards patient-specific disease modelling, biomarker discovery and therapeutic response assessment. These PDOs generate elaborate, complicated and high dimensional data traversing imaging, advanced omics and pharmacological analyses. Artificial intelligence (AI) and machine learning (ML) approaches allow refined prognostic stratification of patients through improved target delineation and response prediction when integrated with bioinformatics and conventional statistics. This narrative review discusses the foundations laid on the organoid generation and AI-enabled evaluation of ovarian cancer PDOs. The successful translation of AI-guided strategies into clinical oncology will depend on rigorous multi-center validation, harmonized methodologies, and prospective clinical studies demonstrating improvements in patient outcomes and real-world applicability.