Deep learning from ultrasound images of breast cancer sentinel lymph nodes to predict metastasis

Abstract Objectives This study aims to develop a non-invasive assessment in breast cancer sentinel lymph node (SLN) using deep learning. Materials and methods Continuously retrospective patients with breast cancer who have undergone both contrast-enhanced ultrasound (CEUS) and two-dimensional ultrasound (TDUS) for sentinel lymph node examination. Those patients were randomly divided into training set, validation set, and internal test set in a ratio of 8:1:1. A Re-parameterization Visual Geometry Group-Convolutional Block Attention Module (RepVGG-CBAM) model was constructed based on the RepVGG network, embedding the CBAM attention mechanism. The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic performance. Results In the test set, the AUC were experts in TDUS, CEUS, and combination ultrasound (CBUS), model in TDUS, CEUS and CBUS were 0.794, 0.806, 0.774, 0.861, 0.851, 0.842 respectively. The difference in AUC between Experts in TDUS (0.794) and Model in TDUS (0.861) was statistically significant (p = 0.043). The difference in AUC between Experts in TDUS (0.794) and Model in CEUS (0.851) was statistically significant (p<0.01). The difference in AUC between Experts in CBUS (0.774) and Model in TDUS (0.861) was statistically significant (p = 0.007). The difference in AUC between Experts in CBUS (0.774) and Model in CEUS (0.851) was statistically significant (p<0.001). Conclusions An algorithm model was developed to determine the SLN metastasis status of breast cancer patients.