Enhancing the Prediction of Axillary Lymph Node Metastasis in Breast Cancer through Habitat-Based Radiomics and...

To develop a machine learning model integrating habitat-based radiomics and voting algorithms for predicting axillary lymph node metastasis (ALNM) in breast cancer using B-mode and contrast-enhanced ultrasound images.

This retrospective study included 246 T1/T2 stage breast cancer patients (246 lesions) from Fujian Cancer Hospital (2016.04-2022.12). Lesions were randomly divided into training (n = 197) and testing (n = 49) datasets. A Gaussian Mixture Model partitioned B-mode ultrasound images into three subregions. Radiomics features, including shape features, first-order features, and texture features, were extracted from whole-tumor (B-mode and CEUS) and subregional (B-mode) ROIs. Multiple classifiers were applied to evaluate the model's diagnostic performance. Voting algorithms were used to integrate habitat-based radiomics, traditional radiomics, and clinical information for model optimization. Diagnostic performance was assessed via accuracy, sensitivity, specificity, and F1-score.

Feature extraction yielded 899 features per ROI, including tumor subregions, enhancing prediction robustness. On the testing set, the combined habitat-based model (Habitat-CEUS-Clinical model) achieved an accuracy of 87.76% (95% CI: 0.775, 0.944) and a false positive rate of 7.41% (95% CI: 0.019, 0.202) using hard voting, outperforming single models by 12.25% in terms of accuracy. In comparison, the traditional approach (Whole-CEUS-Clinical model) reached an accuracy of 79.59% (95% CI: 0.678, 0.884). The difference between the Habitat-CEUS-Clinical model and the Whole-CEUS-Clinical model was statistically significant (p < 0.05).

Habitat-based radiomics captures tumor heterogeneity more effectively than conventional methods. Dual-modality ultrasound combined with voting algorithms significantly improves ALNM prediction, providing a reliable foundation for computer-aided preoperative planning in breast cancer.