ArticleNPJ digital medicine2026
Integrating ultrasound-CT-MR for preoperative multi-task prediction in ovarian cancer: achieving diagnostic parity with multidisciplinary team consensus.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- A review of ovarian-adnexal reporting and data system and radiomics in ovarian cancer: focus on ultrasound applications.Frontiers in oncology · 2026Review
- Multimodal AI in high-grade serous ovarian cancer: integrated prediction and clinical decision-making.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
21 authors.
Funding
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.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.