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<p>Accurate ovarian cancer screening and diagnosis are critical for patient survival. We present UMORSS, an AI-assisted diagnostic system integrating ultrasound (US) imaging and clinical data with uncertainty quantification for precise ovarian cancer risk assessment. Developed and evaluated using a multicentre dataset (7352 patients, 7594 lesions, 9281 US images), UMORSS employs a two-phase approach: Phase I rapidly triages low-risk lesions via initial US analysis, and Phase II provides uncertainty-aware multimodal analysis for complex cases. Phase I accurately identified 68.7% of physiological cysts and 13.8% of benign tumours as low-risk, with zero false negatives, and Phase II achieved an AUC of 0.955 (internal testing) and 0.926 (external validation). Furthermore, a prospective reader study (<i>n</i> = 284 cases, six radiologists) demonstrated that UMORSS as a human-AI collaborative tool increased radiologists’ average AUC by 10.58% and sensitivity by 22.48%. UMORSS shows strong potential to streamline clinical workflow, optimize resource allocation, and standardize ovarian cancer diagnosis.</p>

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A multimodal uncertainty-aware AI system optimizes ovarian cancer risk assessment workflow

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月16日

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  • Xiaodong Wang,
  • Xiaohui Lv,
  • Jingwen Wang,
  • Lulu Zou,
  • Zijun Chen,
  • Ruiyu Zhao,
  • Lisha Zhao,
  • Min Zhao,
  • Xinlei Zhang,
  • Boan Zhang,
  • Jiahao Zhang,
  • Yiteng Zhu,
  • Xin Shi,
  • Yan’e Gao,
  • Minghui Liu,
  • Lirong Ai,
  • Liming Wang,
  • Xiyang Liu,
  • Hong Yang

摘要

Accurate ovarian cancer screening and diagnosis are critical for patient survival. We present UMORSS, an AI-assisted diagnostic system integrating ultrasound (US) imaging and clinical data with uncertainty quantification for precise ovarian cancer risk assessment. Developed and evaluated using a multicentre dataset (7352 patients, 7594 lesions, 9281 US images), UMORSS employs a two-phase approach: Phase I rapidly triages low-risk lesions via initial US analysis, and Phase II provides uncertainty-aware multimodal analysis for complex cases. Phase I accurately identified 68.7% of physiological cysts and 13.8% of benign tumours as low-risk, with zero false negatives, and Phase II achieved an AUC of 0.955 (internal testing) and 0.926 (external validation). Furthermore, a prospective reader study (n = 284 cases, six radiologists) demonstrated that UMORSS as a human-AI collaborative tool increased radiologists’ average AUC by 10.58% and sensitivity by 22.48%. UMORSS shows strong potential to streamline clinical workflow, optimize resource allocation, and standardize ovarian cancer diagnosis.