To develop and validate a machine learning-based predictive model for identifying high axillary nodal burden (≥ pN2) in SLNB-positive breast cancer patients, integrating preoperative, intraoperative, and pathological features to support clinical decision-making when ALND is omitted. A retrospective study was conducted at a single institution between 2020 and 2024, enrolling 956 breast cancer patients who underwent both SLNB and ALND. Clinicopathological variables (preoperative imaging, intraoperative SLN findings, postoperative pathology) were collected. The cohort was randomly split into a training set (70%, n = 670) and a validation set (30%, n = 286). Features were selected via univariate analysis (p < 0.05), LASSO regression (λ1se criterion), and the Boruta algorithm. Nine machine learning models were trained and evaluated using AUC, accuracy, sensitivity, specificity, and F1-score. SHAP analysis was used to interpret the optimal model, and a clinical nomogram was developed based on logistic regression. The prevalence of high nodal burden (≥ pN2) was 15.3% (146/956). Seven key predictors were identified: Neurological invasion、Lymph vascular invasion、Imaging abnormal nodes、Lymph nodes size、Tumor size、Negative SLN、Positive SLN. Logistic Regression (validation AUC: 0.850, accuracy: 0.857, F1-score: 0.602) and Support Vector Machine (validation AUC: 0.857, accuracy: 0.853, F1-score: 0.596) demonstrated the most stable performance across datasets. A nomogram was developed for intuitive clinical use. The proposed machine learning model accurately predicts high nodal burden in SLNB-positive breast cancer patients, facilitating individualized adjuvant therapy planning and avoiding unnecessary ALND.
Li et al. (Thu,) studied this question.
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