Radiomics and radiogenomics in ovarian cancer: a review with a focus on ultrasound applications

Ovarian cancer (OC) is a leading cause of gynecologic cancer mortality, poses significant diagnostic challenges due to its inter- and intra-tumoral heterogeneity. Conventional qualitative imaging often fails to capture such complexity, whereas radiomics is specifically designed to address it. By integrating imaging features with clinical, genomic, or proteomic data, these approaches reveal sub-visual heterogeneity and enhance diagnostic accuracy. Ultrasound-based radiomics and radiogenomics further extend its clinical utility. Virtual biopsy techniques, such as radiomic habitat maps fused with ultrasound, enable real-time, multi-site sampling to predict prognosis, peritoneal metastasis, and recurrence. This review synthesizes recent advancements in ultrasound-based radiomics and radiogenomics for OC, focusing on their clinical utility in improving diagnostic accuracy, prognostic stratification, and personalized therapeutic strategies. Despite progress, challenges such as insufficient standardization and limited model interpretability persist. Future efforts should prioritize AI-augmented analytical pipelines, multicenter prospective validation, and biomarker discovery to bridge imaging phenotypes with precision oncology framework in OC management.