Predicting the axillary lymph node tumor burden in breast cancer patients using ultrasonic radiomics nomogram model

Assessing axillary lymph node (ALN) tumor burden (low burden: < 3 positive ALNs; high burden: ≥ 3 positive ALNs) preoperatively is essential for guiding treatment strategies. This study aimed to develop a radiomics-based nomogram by integrating clinical data, serologic markers, ultrasound imaging features, and ultrasound-derived radiomics features to predict axillary lymph node metastatic burden in breast cancer.

A study was conducted on 234 breast cancer patients. Univariate and multivariate logistic regression analyses were used to identify independent risk factors from ultrasound imaging and clinical pathology, constructing a clinical model. Radiomics features were extracted from ultrasound images, and the best features were selected using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm to construct the Radiomics score. The Radiomics nomogram model was built by combining the Radiomics score and independent risk factors from the clinical model. The performance of the clinical model, radiomics model, and combined model in predicting axillary lymph node tumor burden was evaluated. Model performance was assessed by discrimination, calibration curves, and decision curves.

Results showed that US-reported ALN status and CA153 were independent risk factors for high ALN tumor burden. The radiomics nomogram demonstrated good calibration and discrimination, with an area under the ROC curve of 0.815 (95% CI, 0.755-0.876) for the training set and 0.808 (95% CI, 0.678-0.938) for the testing set. Furthermore, compared to the clinical model and radiomics model, The differences in AUC between the nomogram model and the clinical model, as well as between the nomogram model and the radiomics model, were not statistically significant (nomogram model vs. clinical model: P = 0.2078; nomogram model vs. radiomics model: P = 0.4161). But the nomogram model provided greater net benefit for all patients in the probability threshold range of 0.05-0.70.

This study highlights the potential of an ultrasound-based radiomics nomogram as a robust and non-invasive predictive tool for evaluating ALN tumor burden, offering valuable guidance for personalized treatment planning in breast cancer.