Combining Node-RADS with ADC can improve diagnostic performance for lymph node metastasis in breast cancer

To evaluate the diagnostic value of Node Reporting and Data System (Node-RADS) and apparent diffusion coefficient (ADC) values for identifying axillary lymph node metastasis (ALNM) in breast cancer, and to construct and validate a predictive model for ALNM evaluation.

The Node-RADS scores for axillary lymph nodes (ALN) were retrospectively assessed. The ADC values of the corresponding lymph nodes (LN) and the primary tumors were measured to calculate the calibrated ADC (cADC) and relative ADC (rADC) values. A predictive model was developed based on the factors associated with ALNM that were identified in the univariate and multivariate analyses. The model was subsequently validated on an internal and external validation dataset. The diagnostic performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC). Cohen's Kappa analysis was used to evaluate inter-reader agreement.

Seven hundred eighty-seven female breast cancer patients from Center 1 (mean age, 52.01 years ± 9.42) and 63 from Center 2 (mean age, 53.21 years ± 11.32) were included. Node-RADS exhibited good diagnostic performance in distinguishing ALNM, with a score greater than 2 being the optimal cutoff value. The model incorporating Node-RADS and cADC showed excellent predictive ability, achieving AUC values of 0.807 (95% CI: 0.751, 0.856) and 0.801 (95% CI: 0.681, 0.891) in the internal and external validation sets, respectively.

Node-RADS provides a reliable method for the standardized assessment of ALNM. The combination of Node-RADS with ADC improves the diagnostic performance for ALNM in breast cancer.

Question Axillary lymph node status significantly influences treatment strategies for breast cancer, yet there remains a lack of consensus regarding their radiological evaluation. Findings Both Node-RADS and ADC values are effective for distinguishing lymph node metastasis, and their combination improves the diagnostic performance for ALNM. Clinical relevance The predictive model that integrates Node-RADS and cADC serves as a simple and practical tool to assist clinicians in formulating personalized treatment strategies.