Construction of a risk prediction model for axillary lymph node metastasis in breast cancer based on gray-scale ultrasound and clinical pathological features.

Abstract

This study aimed to develop a model to predict the risk of axillary lymph node (ALN) metastasis in breast cancer patients, using gray-scale ultrasound and clinical pathological features. A retrospective analysis of 212 breast cancer patients who met the inclusion criteria from January 2011 to December 2021 was carried out. Clinical and pathological characteristics, including age, tumor size, pathological type, molecular subtype, estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2), and proliferation cell nuclear antigen (Ki-67), were examined. Preoperative ultrasound examinations were performed, and ultrasound radiomics features of breast cancer lesions were extracted using Pyradiomics software. The data was divided into training (70%) and testing (30%) sets. A predictive model for axillary lymph node metastasis (ALNM) was established by combining clinical and ultrasound features. The diagnostic performance of the model was evaluated using receiver operating characteristic (ROC) curves and five-fold cross-validation. The rate of lymph node metastasis was 41.51%. Using LASSO algorithm, 17 features linked to ALN metastasis were extracted from a comprehensive databank of 8 clinical features and 1314 ultrasound radiomic attributes. Of these, four were clinical-pathological features (tumor size, tumor type, age, and expression levels of the Ki-67 protein), and 13 were radiomic features. And the following features exhibited both high weights and correlation coefficients: tumor size (R=0.29, weight=0.071), tumor type (R=-0.24, weight=-0.048), wavelet-LH_glcm_Imc1 (R=0.28, weight=0.029363), wavelet-LH_glszm_SZNUN (R=-0.20, weight=-0.028507), and squareroot_ firstorder_ Minimum (R= -0.25, weight= -0.059). The ROC area under the curve for the model in the training and testing sets was 0.882 (95% CI: 0.830-0.935) and 0.853 (95% CI: 0.762-0.945), respectively. The predictive model demonstrated a sensitivity of 87.5% on the training set and 79.2% on the test set, with corresponding specificities of 75.0% and 77.5%, accuracy of 80.4% and 78.1%, respectively. When evaluated using 5-fold cross-validation, the model achieved an average test set area under the curve (AUC) of 0.799 and a training set AUC of 0.852. The clinical-radiomic model has the potential to predict axillary lymph node metastasis in breast cancer.