Nomograms Integrating MRI-derived Apparent Diffusion Coefficient and Clinicopathologic Features for Prediction of Axillary Lymph Node Metastasis in Breast Cancer.

Purpose To develop three nomograms integrating apparent diffusion coefficients (ADCs) derived from diffusion-weighted imaging to predict the status of pretreatment axillary lymph nodes (ALNs) (task 1), nonsentinel lymph nodes (task 2), and ALNs after neoadjuvant chemotherapy treatment (task 3) in patients with breast cancer. Materials and Methods Pretreatment MRI scans, including diffusion-weighted images, were retrospectively acquired from patients with breast cancer at multiple centers from May 2019 to May 2023. ADC values and clinicopathologic features were measured. Uni- and multivariable logistic regression analyses were performed to identify independent predictors of ALN metastasis. These predictors were incorporated into nomogram models for each of the three tasks. Model performance was assessed with area under the receiver operating characteristic curve (AUC) analysis in training and two external testing datasets. Results The study included 961 female patients (mean age ± SD, 50 years ± 10) with breast cancer from three hospitals. In the three tasks, the ADC values of the ALN metastasis groups were lower than those of the nonmetastasis groups (all < .05). The nomogram models combining ADC values and clinicopathologic features demonstrated high predictive performance for each task in the training cohort (task 1: AUC, 0.90; task 2: AUC, 0.74; task 3: AUC, 0.75), external testing cohort 1 (task 1: AUC, 0.86; task 3: AUC, 0.82), and external testing cohort 2 (task 1: AUC, 0.90; task 3: AUC, 0.84). Conclusion Nomograms incorporating ADCs and clinicopathologic features demonstrated good performance in predicting ALN metastasis in patients with breast cancer. Breast, MR-Functional Imaging, MR-Diffusion Weighted Imaging, Apparent Diffusion Coefficient, Axillary Lymph Node Metastasis, Nonsentinel Lymph Node Metastasis, Neoadjuvant Chemotherapy, Nonogram © RSNA, 2025.