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<p>Accurate triage of ovarian masses is facilitated in many centers by the guideline-endorsed, extensively validated IOTA-ADNEX risk model, yet the model still relies on manual measurements of key tumor features. We developed ADNEX-AI, a multi-task deep-learning system that automatically segments four ADNEX ultrasound predictors – lesion, locules, solid tissue, papillary projections – and outputs their quantitative values. The network was trained on 816 annotated images from 369 consecutive women recruited at 11 centers (43% malignancies) and prospectively evaluated on a temporally separate cohort of 1088 patients scanned at 10 of those centers (8008 images; 35% malignancies). ADNEX-AI discriminated benign from malignant tumors with an AUC of 0.930 (95% CI 0.913-0.943), less than but close to examiner-derived ADNEX (0.945; 0.930-0.957; P = 0.004) while delivering better calibration and markedly lower inter-center variability. By removing manual caliper work yet preserving full interpretability, ADNEX-AI could extend high-quality ovarian-cancer risk stratification to clinics that lack specialized ultrasound expertise.</p>

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ADNEX-AI: automated extraction of ultrasound predictors for interpretable ovarian cancer risk stratification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月11日

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  • Axel Geysels,
  • Giulia Garofalo,
  • Stefan Timmerman,
  • Jolien Ceusters,
  • Francesca Buonomo,
  • Daniela Fischerová,
  • Antonia Carla Testa,
  • Francesca Moro,
  • Povilas Sladkevicius,
  • Ligita Jokubkiene,
  • Caroline Van Holsbeke,
  • Marek Kudla,
  • Artur Czekierdowski,
  • Elisabeth Epstein,
  • Yvette Groszmann,
  • Matthew Blaschko,
  • Tom Bourne,
  • Bart De Moor,
  • Lil Valentin,
  • Ben Van Calster,
  • Dirk Timmerman,
  • Wouter Froyman

摘要

Accurate triage of ovarian masses is facilitated in many centers by the guideline-endorsed, extensively validated IOTA-ADNEX risk model, yet the model still relies on manual measurements of key tumor features. We developed ADNEX-AI, a multi-task deep-learning system that automatically segments four ADNEX ultrasound predictors – lesion, locules, solid tissue, papillary projections – and outputs their quantitative values. The network was trained on 816 annotated images from 369 consecutive women recruited at 11 centers (43% malignancies) and prospectively evaluated on a temporally separate cohort of 1088 patients scanned at 10 of those centers (8008 images; 35% malignancies). ADNEX-AI discriminated benign from malignant tumors with an AUC of 0.930 (95% CI 0.913-0.943), less than but close to examiner-derived ADNEX (0.945; 0.930-0.957; P = 0.004) while delivering better calibration and markedly lower inter-center variability. By removing manual caliper work yet preserving full interpretability, ADNEX-AI could extend high-quality ovarian-cancer risk stratification to clinics that lack specialized ultrasound expertise.