Research progress on the prediction of axillary lymph node metastasis in breast cancer by radiomics using CT and MRI
SHU Yunyan
YU Wenjing
ZHANG Zequn
WANG Qianqian
JIANG Xingyue
LIU Xinjiang
Abstract：Breast cancer ranks among the most prevalent malignancies in women globally,with consistently high incidence and mortality rates.Accurate assessment of axillary lymph node(ALN)status critically informs clinical decision-making and prognosis prediction.Although ALN dissection and sentinel lymph node biopsy remain the diagnostic gold standard,both procedures carry inherent limitations including surgical invasiveness and false-negative rates.Consequently,precise preoperative prediction of ALN status remains an unmet clinical need.Techniques such as mammography,CT,and breast MRI have advanced non-invasive evaluation.Radiomics and deep learning methodologies are now integral to ALN metastasis research in breast cancer.Notably,radiomics and deep learning models based on CT and MRI demonstrate robust performance in detecting ALN metastasis,achieving significant AUC values.This article systematically reviews recent advances in CT and MRI radiomics for predicting axillary lymph node metastasis in breast cancer.
Keywords：breast canceraxillary lymph node metastasismagnetic resonance imagingcomputed tomographyradiomicsdeep learning
Publication Date：2025-11-20
Online Publishing Date：2025-12-16(First online date of this platform, not the publication date of the document)
Pages：6( 1421-1426 )