AUTHOR=Sha Yongliang , Ge Song , Wang Yiqiu , Cai Shilong , Wang Chengyi , Zhuang Huijie , Shi Jin , He Shiqing , Sun Xia , Ma Li , Guo Hao , Cheng Hui 

TITLE=Ultrasound-based radiomics combined with B3GALT4 level to predict sentinel lymph node metastasis in primary breast cancer

JOURNAL=Frontiers in Oncology

VOLUME=Volume 15 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2025.1570493

DOI=10.3389/fonc.2025.1570493

ISSN=2234-943X

ABSTRACT=ObjectiveTo evaluate the value of the clinical model for predicting axillary lymph node metastasis (ALNM) of breast cancer before operation by integrating ultrasound (US) and β-1,3-galactosyltransferase-4 (B3GALT4) expression level of the primary tumor.MethodsA total of 135 breast cancer patients who underwent US examination and axillary lymph nodes dissection (ALND) were enrolled. They were randomly divided into a training group (95 cases) and a verification group (40 cases). The ultrasound imaging characteristics of the primary tumor were extracted from each region of interest (ROI), and the Spearman correlation coefficient, least absolute shrinkage and selection operator (LASSO), and the minimum redundancy maximum relevance (mRMR) were used for feature selection. The radiomics model was constructed by eighteen machine-learning techniques. B3GALT4 expression level of the primary tumor was analyzed using quantitative real-time polymerase chain reaction (qRT-PCR). A clinical model was constructed based on B3GALT4 mRNA level. Further, a nomogram was established by integrating B3GALT4 and the radiomics signature. The effectiveness of each model was evaluated by receiver operating characteristic (ROC) curve, Hosmer-Lemeshow test, calibration curve, and decision curve analyses (DCA).ResultsA total of 1562 radiomics features were extracted, and 30 features were selected. The SVM model had the highest AUC values of 0.937 and 0.932 in the training and validation sets. The AUC of the radiomics model was 0.937 (95% CI: 0.885-0.989) in the training cohort and 0.932 (95% CI: 0.860-1.000) in the external validation cohort, respectively. The levels of B3GALT4 mRNA were significantly different between the ALNM and non-ALNM groups (P<0.001). The clinical model achieved a higher AUC (training group, 0.904; validation group, 0.887). The nomogram performed well in both the training set (AUC = 0.991) and the validation set (AUC = 0.975). The nomogram had satisfactory clinical utility.ConclusionThe nomogram constructed by ultrasound features and B3GALT4 of the primary tumor can be used as an effective tool for individualized prediction of ALNM in breast cancer.