An interpretable AI system reduces false-positive MRI diagnoses by stratifying high-risk breast lesions.
Authors
Affiliations (13)
Affiliations (13)
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China.
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China.
- Department of Medical Imaging, Peking University Shenzhen Hospital, Shenzhen, China.
- Department of Radiology, The First Hospital of China Medical University, Shenyang, China.
- Department of Radiology, The Third Affiliated Hospital of Kunming Medical University, Kunming, China.
- Department of Radiology, The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
- Department of Radiology, Guangdong Women and Children Hospital, Guangzhou, Guangdong, China. [email protected].
- Department of Radiology, Women and Children's Hospital, Southern University of Science and Technology, Guangzhou, Guangdong, China. [email protected].
- Department of Medical Ultrasonics, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China. [email protected].
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. [email protected].
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China. [email protected].
- Department of Radiology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China. [email protected].
- Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application, Guangzhou, China. [email protected].
Abstract
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.