Overview of Multimodal Radiomics and Deep Learning in the Prediction of Axillary Lymph Node Status in Breast Cancer.

Journal: Academic radiology
Published Date: Aug 18, 2025

Abstract

Breast cancer is the most prevalent malignancy in women, with the status of axillary lymph nodes being a pivotal factor in treatment decision-making and prognostic evaluation. With the integration of deep learning algorithms, radiomics has become a transformative tool with increasingly extensive applications across multimodality, particularly in oncological imaging. Recent studies of radiomics and deep learning have demonstrated considerable potential for noninvasive diagnosis and prediction in breast cancer through multimodalities (mammography, ultrasonography, MRI and PET/CT), specifically for predicting axillary lymph node status. Although significant progress has been achieved in radiomics-based prediction of axillary lymph node metastasis in breast cancer, several methodological and technical challenges remain to be addressed. The comprehensive review incorporates a detailed analysis of radiomics workflow and model construction strategies. The objective of this review is to synthesize and evaluate current research findings, thereby providing valuable references for precision diagnosis and assessment of axillary lymph node metastasis in breast cancer, while promoting development and advancement in this evolving field.

Authors

  • Xuemei Zhao
    Translational Molecular Biomarkers, MRL, Merck & Co., Kenilworth, NJ.
  • Mandi Wang
    Department of Radiology, Shenzhen People's Hospital (The First Affiliated Hospital, Southern University of Science and Technology; The Second Clinical Medical College, Jinan University), Shenzhen, Guangdong, China (M.W.).
  • Youcai Wei
    95944 Unit of the Chinese People's Liberation Army, Suizhou, China (Y.W.).
  • Zhijiao Lu
    Department of Radiology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, Guangdong, China (Z.L.).
  • Yuqing Peng
    Department of Radiology, Shenshan Medical Center, Memorial Hospital of Sun Yat-Sen University, Shanwei, Guangdong, China (Y.P.).
  • Xiu Cheng
    Department of MR, The People's Hospital of Baoan Shenzhen, Shenzhen, Guangdong, China (X.Z., X.C., J.S.).
  • Jianxun Song
    Department of Microbial Pathogenesis and Immunology, Texas A&M University Health Science Center, Bryan, Texas.

Keywords

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