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Review Article | Open Access

Research Progress on Ultrasound Radiomics in Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer

Yuanjing GaoaZihan NiuaYanwen LuobMengyuan ZhouaMengsu XiaoaYuxin JiangaQingli Zhua( )
Department of Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
Department of Ultrasound, Zhongshan Hospital, Fudan University
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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.

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Advanced Ultrasound in Diagnosis and Therapy
Pages 79-89

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Cite this article:
Gao Y, Niu Z, Luo Y, et al. Research Progress on Ultrasound Radiomics in Preoperative Prediction of Axillary Lymph Node Metastasis in Breast Cancer. Advanced Ultrasound in Diagnosis and Therapy, 2026, 10(2): 79-89. https://doi.org/10.26599/AUDT.2026.250066

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Received: 21 July 2025
Revised: 08 September 2025
Accepted: 20 December 2025
Published: 01 July 2026
2576-2508/© AUDT 2026

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed