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<p>Breast cancer diagnosis using magnetic resonance imaging remains limited by high false-positive rates and substantial inter-reader variability, especially for lesions classified as Breast Imaging Reporting and Data System (BI-RADS) category 4, often leading to unnecessary biopsies. Here we show that the BI-RADS 4 Lesions Analysis System (BL4AS), an artificial intelligence system powered by foundation models and leveraging the rich spatiotemporal information of dynamic contrast-enhanced MRI, addresses these diagnostic challenges. Developed on a multicenter dataset of 2,803 lesions from 2,686 female patients, BL4AS demonstrates robust performance with areas under the curve of 0.892-0.930 and significantly outperforms radiologists in specificity (0.889 versus 0.491). BL4AS-assisted interpretation significantly improves diagnostic accuracy for both senior and junior radiologists, reducing inter-reader variability by 24.5% and decreasing false-positive rates by 27.3%. BL4AS further stratifies lesions into subcategories (4 A, 4B and 4 C) for refined risk assessment, offering a practical tool for precision breast cancer management.</p>

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An interpretable AI system reduces false-positive MRI diagnoses by stratifying high-risk breast lesions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月2日

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  • Yanting Liang,
  • Zhitao Wei,
  • Yi Dai,
  • Xiaobo Chen,
  • Siyao Du,
  • Chinting Wong,
  • Zeyan Xu,
  • Weibo Gao,
  • Chu Han,
  • Kexin Chen,
  • Ke Han,
  • Jiayi Liao,
  • Yuelang Zhang,
  • Lina Zhang,
  • Zaiyi Liu,
  • Yan Zhang,
  • Ying Wang,
  • Changhong Liang,
  • Zhenwei Shi

摘要

Breast cancer diagnosis using magnetic resonance imaging remains limited by high false-positive rates and substantial inter-reader variability, especially for lesions classified as Breast Imaging Reporting and Data System (BI-RADS) category 4, often leading to unnecessary biopsies. Here we show that the BI-RADS 4 Lesions Analysis System (BL4AS), an artificial intelligence system powered by foundation models and leveraging the rich spatiotemporal information of dynamic contrast-enhanced MRI, addresses these diagnostic challenges. Developed on a multicenter dataset of 2,803 lesions from 2,686 female patients, BL4AS demonstrates robust performance with areas under the curve of 0.892-0.930 and significantly outperforms radiologists in specificity (0.889 versus 0.491). BL4AS-assisted interpretation significantly improves diagnostic accuracy for both senior and junior radiologists, reducing inter-reader variability by 24.5% and decreasing false-positive rates by 27.3%. BL4AS further stratifies lesions into subcategories (4 A, 4B and 4 C) for refined risk assessment, offering a practical tool for precision breast cancer management.