MRI Radiomics and Biological Correlations for Predicting Axillary Lymph Node Burden in Early-Stage Breast Cancer

Abstract Background and aims Preoperative prediction of axillary lymph node (ALN) burden in patients with early-stage breast cancer is pivotal for individualised treatment. This study aimed to develop a MRI radiomics model for evaluating the ALN burden in early-stage breast cancer and to provide biological interpretability to predictions by integrating radiogenomic data. Methods This study retrospectively analyzed 1211 patients with early-stage breast cancer from four centers, supplemented by data from The Cancer Imaging Archive (TCIA) and Duke University (DUKE). MRI radiomic features were extracted from dynamic contrast-enhanced MRI images and an ALN burden-related radscore was constructed by the backpropagation neural network algorithm. Clinical and combined models were developed, integrating ALN-related clinical variables and radscore. The Kaplan–Meier curve and log-rank test were used to assess the prognostic differences between the predicted high- and low-ALN burden groups in both Center I and DUKE cohorts. Gene set enrichment and immune infiltration analyses based on transcriptomic TCIA and TCIA Breast Cancer dataset were used to investigate the biological significance of the ALN-related radscore. Results The MRI radiomics model demonstrated an area under the curve of 0.781–0.809 in three validation cohorts. The predicted high-risk population demonstrated a poorer prognosis (log-rank P< 0.05 in both cohorts). Radiogenomic analysis revealed migration pathway upregulation and cell differentiation pathway downregulation in the high radscore groups. Immune infiltration analysis confirmed the ability of radiological features to reflect the heterogeneity of the tumor microenvironment. Conclusions The MRI radiomics model effectively predicted the ALN burden and prognosis of early-stage breast cancer. Moreover, radiogenomic analysis revealed key cellular and immune patterns associated with the radscore.