Developing and Evaluating a Nomogram Model Predicting Axillary Lymph Node Metastasis of Triple-Negative Breast Cancer Based on Multimodal Imaging Characteristics.

Rationale and objectives: Breast cancer is the most frequently diagnosed cancer among women worldwide, with axillary lymph nodes being common sites of metastasis, particularly triple-negative breast cancer (TNBC), which is the subtype with the poorest prognosis. This study aimed to develop a nomogram model to predict axillary lymph node metastasis (ALNM) in TNBC patients based on mammography (MG), multimodal ultrasound (US), and clinical pathological characteristics.

Patients and methods: A retrospective study was performed on 291 patients diagnosed with TNBC from two centers. Patients from the Center 1 were randomly divided into a training cohort (n = 159) and a internal test cohort (n = 68) using a 7:3 ratio, while patients from the Center 2 served as an external test cohort. Each group was further divided into an ALNM group and a non-ALNM group based on the presence or absence of ALNM. Predictors were selected via least absolute shrinkage and selection operator (LASSO) regression and multivariable logistic analysis. The predictive performance of the nomogram model was evaluated by the receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA).

Results: Notable predictors included MG_reported_margin, MG_reported_suspicious malignant calcifications, MG_reported_abnormal ALN, elastography score, and US_reported_abnormal ALN. The area under the receiver operating characteristics curve (AUC) value of the nomogram model was 0.931 (95%CI: 0.890-0.973) for the training cohort, AUC=0.929 (95%CI: 0.871-0.986) for the internal test cohort and AUC=0.891 (95%CI: 0.794-0.987) for the external test cohort. Calibration curves and DCA both suggested that the nomogram exhibited favorable calibration and clinical utility.

Conclusion: The predictive model combined with multimodal US and MG characteristics developed in this study is highly accurate, serves as a powerful tool for clinical assessment, and shows promise for predicting ALNM in patients with TNBC.