Kinetic and radiomic features on DCE-MRI as a predictor for axillary lymph node metastasis burden in T1 and T2 stage breast cancer

Yuanyuan Liu¹Chunhua Wang¹Ruirui Meng¹Hongbing Luo¹Xiaoyu Chen¹Shaoyu Wang²Jing Ren¹Peng Zhou¹Xin Zhang^1*

• ¹Sichuan Cancer Hospital and Institute, Chengdu, China
• ²Siemens Healthineers China, Shanghai, China

Background: Magnetic resonance imaging (MRI) is increasingly used to evaluate axillary lymph node (ALN) status in breast cancer. However, the correlation between MRI features of the primary tumor and the ALN metastasis (ALNM) burden remains poorly understood. This study aimed to develop a noninvasive MRI-based model to preoperatively distinguish between low (≤ 2 nodes) and high (> 2 nodes) ALNM burden in T1 and T2 stage breast cancer. Methods: This retrospective single-center study included 185 patients, categorized by ALNM burden ( ≤ 2 nodes [n = 149] or > 2 nodes [n = 36]). The kinetic and radiomic features were extracted from segmented whole tumor on dynamic contrast enhanced MRI (DCE-MRI). We employed a forward-stepwise feature selection method based on the ANOVA F-score from the training cohort. Features were iteratively added to a logistic regression model according to F-value. The final model, trained on the entire training set, was evaluated on an independent test cohort. Results: The model incorporated 6 kinetic and 3 radiomic features, demonstrating moderate predictive performance. The model achieved an area under the curve (AUC) of 0.705 in the test cohort. It showed a sensitivity of 72.7% and a specificity of 77.8%. The negative predictive value (NPV) was 92.1%. Conclusion: The kinetic and radiomic features from DCE-MRI showed potential for predicting ALNM burden (≤ 2 or > 2 nodes) in T1 and T2 stage breast cancer. The high NPV particularly supported their utility as a noninvasive tool to identify candidates for less invasive axillary procedures.