Integrating ultrasound-CT-MR for preoperative multi-task prediction in ovarian cancer: achieving diagnostic parity...

Ovarian cancer, with high mortality, demands accurate preoperative assessment to guide individualized treatment. It typically requires ultrasound, CT, and MRI interpreted by multidisciplinary teams (MDTs) in complex cases. We developed OVUCM, a multi-task AI system that integrates multi-modalities via intermediate fusion using radiomics, machine learning, and 5×4 nested cross-validation. Trained on 1742 patients from a cancer center, OVUCM predicted five clinical tasks with AUCs of 0.847-0.929: benign vs. non-benign, borderline vs. malignant, non-epithelial vs. epithelial, FIGO stages I-II vs. III-IV, and non-HGSOC vs. HGSOC. External validation in 150 patients from two general hospitals confirmed generalizability (AUCs: 0.833-0.974). The system achieved diagnostic parity with MDT consensus in four tasks and outperformed it in one, while consistently surpassing at least one independent gynecologist across all tasks. By emulating MDT-level interpretation, OVUCM bridges the gap between single-modality tools and comprehensive clinical decision-making, offering a scalable solution in resource-limited settings.