Integrating ultrasound-CT-MR for preoperative multi-task prediction in ovarian cancer: achieving diagnostic parity with multidisciplinary team consensus.
Authors
Affiliations (14)
Affiliations (14)
- Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
- National Regional Medical Center for Obstetrics and Gynecology, Zhejiang Provincial Clinical Research Center for Gynecological Diseases, Hangzhou, China.
- Research Center for Scientific Data Hub, Zhejiang Lab, Hangzhou, China.
- Department of Ultrasound, the Fourth Affiliated Hospital, Zhejiang University School of Medcine, Yiwu, China.
- School of Clinical Medicine, Hangzhou Normal University, Hangzhou, China.
- Department of Ultrasound Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China. [email protected].
- Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China. [email protected].
- National Regional Medical Center for Obstetrics and Gynecology, Zhejiang Provincial Clinical Research Center for Gynecological Diseases, Hangzhou, China. [email protected].
- Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China. [email protected].
- School of Intelligent Science and Technology, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China. [email protected].
- Women's Hospital, Zhejiang University School of Medicine, Hangzhou, China. [email protected].
- National Regional Medical Center for Obstetrics and Gynecology, Zhejiang Provincial Clinical Research Center for Gynecological Diseases, Hangzhou, China. [email protected].
- Zhejiang Key Laboratory of Precision Diagnosis and Therapy for Major Gynecological Diseases, Hangzhou, China. [email protected].
Abstract
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.