@article{Gao2026, 
author = {Yuanjing Gao and Zihan Niu and Yanwen Luo and Mengyuan Zhou and Mengsu Xiao and Yuxin Jiang and Qingli Zhu},
title = {Research Progress on Ultrasound Radiomics in Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer},
year = {2026},
journal = {Advanced Ultrasound in Diagnosis and Therapy},
volume = {10},
number = {2},
pages = {79-89},
keywords = {Lymphatic metastasis, Radiomics, Deep learning, Ultrasonography},
url = {https://www.sciopen.com/article/10.26599/AUDT.2026.250066},
doi = {10.26599/AUDT.2026.250066},
abstract = {Axillary lymph node (ALN) metastasis is a critical factor influencing prognosis and treatment strategies in breast cancer patients. However, traditional methods—ranging from physical examination to ultrasound—often lack the precision required for clinical decision-making. In recent years, ultrasound radiomics and deep learning have emerged as promising solutions, leveraging high-throughput quantitative features from ultrasound images to enhance detection accuracy. This review explores the development and application of radiomics and deep learning across multiple ultrasound modalities (grayscale, elastography, and contrast-enhanced ultrasound), as well as in multimodal imaging approaches that integrate ultrasound with MRI and PET/CT, underscoring the benefits of incorporating clinicopathological variables to boost predictive performance. These studies provide a vital foundation for personalized treatment and precision medicine in breast cancer management.}
}