Identifying low-risk breast cancer patients for axillary biopsy exemption: a multimodal preoperative predictive model

As the most prevalent female malignancy worldwide, breast cancer frequently involves axillary lymph node metastasis (ALNM), which critically affects therapeutic algorithms. Current guidelines mandate preoperative ultrasound-guided axillary biopsy for suspicious lymph nodes, potentially exposing some low-risk patients with negative results to invasive risks. To optimize the utilization of biopsy, this study established a multimodal predictive framework that preoperatively assesses axillary lymph node (ALN) status, thereby triaging candidates for ultrasound-guided axillary biopsy.

We conducted a retrospective single-center analysis of 703 breast cancer patients who underwent ultrasound-guided axillary biopsy with subsequent definitive surgery at the First Affiliated Hospital of Xi'an Jiaotong University (07/2020-05/2023). Following rigorous application of the inclusion/exclusion criteria, 439 eligible patients were randomized into training (n = 307, 69.9%) and validation (n = 132, 30.1%) cohorts. Axillary surgical pathology served as the reference standard for categorizing lymph node status. Multivariable predictors identified through the least absolute shrinkage and selection operator (LASSO) and logistic regression informed the construction of a clinically deployable nomogram. Model discrimination was quantified via receiver operating characteristic (ROC) analysis with area under the curve (AUC) calculations. The optimal threshold was determined using the maximum Youden index.

LASSO, univariate, and multivariate logistic regression analyses revealed that estrogen receptor (ER) status (P = 0.007), ALN cortical-medullary boundary (P = 0.012), ALN cortical thickness (P < 0.001), short-axis diameter (P = 0.032), and the BI-RADS category on magnetic resonance imaging (MRI) (P = 0.021) were independent predictors of non-ALNM. A nomogram was constructed based on these factors. The multimodal model demonstrated excellent discrimination with AUCs of 0.955 (95% CI 0.926-0.983) and 0.905 (95% CI 0.832-0.978) for the training and validation cohorts, respectively. The model achieved a maximum Youden index of 0.7789 with an optimal threshold of 0.3958.

Our multimodal predictive model integrates clinicopathological profiles with imaging biomarkers (ultrasound and magnetic resonance imaging). This model holds promise for preoperative axillary risk stratification in breast cancer patients, thereby identifying candidates suitable for axillary biopsy exemption, while its application serves as a reference for personalized and refined axillary management.