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

Yongliang Sha¹Song Ge¹Yiqiu Wang¹Shilong Cai¹Wang Chengyi²Huijie Zhuang¹Jin Shi¹Shiqing He¹Xia Sun¹Li Ma¹Hao Guo¹Hui Cheng^1*

• ¹Xuzhou Central Hospital, Xuzhou, Jiangsu Province, China
• ²Jining Medical University, Jining, Shandong, China

Objective: To 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.: A 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). Results A 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.The 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.