Nature communications · 2026
An interpretable AI system reduces false-positive MRI diagnoses by stratifying high-risk breast lesions.
СтатьяМногоцентровое исследованиеНазвание и аннотация: Перевод в очереди
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Liang Yanting, Wei Zhitao, Dai Yi, Chen Xiaobo, Du Siyao, Wong Chinting, Xu Zeyan, Gao Weibo, Han Chu, Chen Kexin, Han Ke, Liao Jiayi, Zhang Yuelang, Zhang Lina, Liu Zaiyi, Zhang Yan, Wang Ying, Liang Changhong, Shi Zhenwei
Аннотация
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.