Dynamic Contrast-Enhanced Magnetic Reso nance Imaging (DCE-MRI) is pivotal in breast cancer diag nosis, yet radiologists face challenges in interpreting its complex data due to the lack of robust automated tools. Current lesion diagnosis systems struggle with limited datasets and insufficient integration of domain knowledge. To overcome these limitations, we propose Breast Lesion Analysis with DomainExpertise(BLADE),anoveldiagnosis framework that synergizes deep learning with clinical ex pertise. BLADE leverages a pre-trained vertical foundation model (optimized via Momentum Contrast on 2.1 million MRI slices) as its encoder, ensuring robust feature extraction. Crucially, the system incorporates prior multi-phasic hemodynamic knowledge to emulate radiologists' diagnos tic reasoning and introduces a Breast Imaging Reporting and Data System (BI-RADS)-based constraint during training to align predictions with clinical standards. Extensive experiments demonstrate that BLADE outperforms state of-the-art methods, achieving an Area Under the Curve (AUC) of 0.9228 and 0.9553 on two external test datasets, respectively. Notably, BLADE significantly enhances clin ical workflow; when used as an assistive tool, BLADE improves diagnostic accuracy by 14.31%, surpassing stan daloneperformanceofclinicians. This workbridgesthegap between AI-driven analysis and clinical practice in breast MRI interpretation. The source code is available at https: //github.com/GDPHMediaLab/BLADE.
Wei et al. (Thu,) studied this question.