Whole-tumor histogram models based on quantitative maps from SyMRI for predicting axillary lymph node status in invasive ductal breast cancer

Motivation: Breast cancer is closely associated with ALN status, influencing prognosis. Sentinel lymph node (SLN) biopsy, a common ALN staging method, has limitations. Goal(s): This study aimed to explore a non-invasive predictive approach for ALN status in IDC patients using SyMRI images and histogram analysis. Approach: We included 212 patients, and compared the performance of SyMRI histogram models in differentiating N0 and N+ groups (further divided into N1 and N2-3). Results: Combining quantitative map features with clinical data achieved the highest diagnostic accuracy. Additionally, specific histogram features were found to differ significantly between N1 and N2-3 groups. Conventional parameters were less discriminative. Impact: We demonstrated efficacy of histogram analysis of SyMRI as a non-invasive method for predicting ALN status. Model combining SyMRI quantitative maps and clinical features yielded satisfactory performance, highlighting the potential of our proposed model in ALN management.