Xin Zhang

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 non-invasive 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 the segmented whole tumor on dynamic contrast-enhanced MRI (DCE-MRI). A forward-stepwise feature selection method was employed based on the ANOVA F-score from the training cohort. Features were added according to F-values and logistical regression model was built iteratively. The final model, trained on the entire training set, was evaluated on the independent test cohort. Results: The model incorporated five kinetic and three radiomic features, demonstrating moderate predictive performance. The model achieved an area under the receiver operating characteristic 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 non-invasive tool to identify candidates for less invasive axillary procedures.

Background: Accurate size and stage estimation is important to monitor tumor response and plan further treatment in breast cancer patients undergoing neoadjuvant chemotherapy. To evaluate the accuracy of imaging findings [ultrasound (US) and magnetic resonance imaging (MRI)] for tumor size and stage estimations in early breast cancer patients and to elucidate the factors influencing tumor stage assessment. Methods: We retrospectively enrolled consecutive women having pathologically confirmed breast cancer (stage T1/T2, 199 patients and 201 lesions) and preoperative records available for both US and MRI. The concordance between imaging-determined and pathological tumor size and stage was explored. The McNemar’s test was conducted to compare the concordance between imaging-determined tumor size and imaging-determined tumor stage. Multivariate logistic regression was used to analyze the factors that influenced the accuracy. Results: The concordance between US-determined and pathological tumor size (71.1%) was comparable to MRI–pathology concordance (72.6%). MRI–determined stage concordance (73.6%) was comparable to US-determined stage concordance (69.2%). Tumors with a larger pathological size, were more likely to be underestimated by US or MRI in terms of tumor size and stage (all P < 0.05). Conclusion: Tumor size and tumor stage concordance did not significantly differ between US and MRI in early breast cancer patients; US could be the first choice for tumor size estimation and tumor staging.