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<p>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.</p>

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Integrating ultrasound-CT-MR for preoperative multi-task prediction in ovarian cancer: achieving diagnostic parity with multidisciplinary team consensus

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月15日

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  • Jingjing Yu,
  • Peijun Hu,
  • Ruixia Dai,
  • Xiaomin Liu,
  • Shanshan Zhang,
  • Yangdi Xu,
  • Yiwei Gao,
  • Bingjian Lu,
  • Wenqian Wang,
  • Yizhen Niu,
  • Xiaochen Wang,
  • Shihao Xu,
  • Weibin Wang,
  • Qianqian Qi,
  • Fan Liu,
  • Yang Zhang,
  • Sihui Hu,
  • Yu Tian,
  • Weiguo Lu,
  • Jingsong Li,
  • Jiale Qin

摘要

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.