Abstract:

Breast cancer stands as the most prevalent malignancy and the primary cause of cancer-related mortality among women globally. The lymph node status is not only pivotal for accurate clinical staging of breast cancer but also significantly associated with patients’ prognosis. Magnetic resonance imaging (MRI) has advantages in evaluating lymph nodes status and the response effect of neoadjuvant therapy, serving as a valuable complement to other imaging modalities. Standardized scoring systems, such as Node Reporting and Data System (Node-RADS), integrate key features including lymph node size, margin characteristics, and enhancement patterns, effectively minimizing interobserver variability in evaluation. MRI radiomics, by extracting quantitative features at high throughput, converts medical images into mineable and analyzable data. Further integrating MRI radiomics, clinicopathological features and molecular subtype information to construct multi-omics models, can effectively predict axillary lymph node metastasis, thereby providing a biological basis for personalized treatment. Artificial intelligence (AI) leverages extensive search algorithms and parameter spaces to generate predictive models. AI-driven MRI analysis has proven effective in predicting lymph node metastasis and treatment responses. In the evaluation of neoadjuvant chemotherapy, the fully automated-integrated system based on deep learning (FAIS-DL) system, which combines multi-region dynamic contrast enhanced-MRI (DCE-MRI) and clinical data, can efficiently predict axillary pathological complete response. This innovation has substantially reduced the rate of unnecessary axillary lymph node dissection (ALND) from 47.9% to 6.8%. This article reviewed the prediction of lymph node status in breast cancer by MRI at different developmental stages, with the aim of enhancing the understanding of clinicians and radiologists regarding the application of MRI in the assessment of lymph node status in breast cancer and evaluating the efficacy of neoadjuvant therapy, and providing assistance for the construction of a model for accurately predicting lymph node status in breast cancer.

Key words: Breast cancer, Neoadjuvant therapy, Lymph nodes, Magnetic resonance imaging, Radiomics, Artificial intelligence