Breast MRI imaging features combined with serological indices in predicting high burden of axillary lymphatic metastases in breast cancer
10.3760/cma.j.cn112149-20250121-00047
- VernacularTitle:乳腺MRI影像特征联合血清学指标预测乳腺癌腋窝淋巴结转移高负荷
- Author:
Xuanxuan DONG
1
;
Jun LU
1
;
Xiang TAN
1
;
Lin ZHANG
1
Author Information
1. 石河子大学第一附属医院医学影像中心，石河子 832000
- Publication Type:Journal Article
- Keywords:
Breast neoplasms;
Magnetic resonance imaging;
Axillary lymph node burden;
Serum CA153
- From:
Chinese Journal of Radiology
2025;59(9):1037-1045
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of breast MRI imaging features combined with serological indicators in predicting the metastatic burden of axillary lymph nodes (ALN) in breast cancer.Methods:This cross-sectional study retrospectively enrolled 146 female patients diagnosed with breast cancer at the First Affiliated Hospital of Shihezi University from January 2020 to November 2024. Patients′ pre-treatment clinical data, serological indices, breast MRI image features, and post-surgical pathologic features were analyzed. Patients were divided into low-burden (<3 metastatic lymph nodes) group and high-burden (≥3 metastatic lymph nodes) group based on pathological ALN confirmation. Group comparisons of clinical variables were analyzed using independent samples t-tests, Mann-Whitney U tests, or χ2 tests. Indicators with statistically significant differences were included in a multivariable logistic regression analysis to screen for independent influences predicting high ALN load and construct multiple logistic regression models. The performance of these models was evaluated using receiver operating characteristic curves and area under the curve (AUC), while net clinical benefit was assessed using decision curve analysis (DCA). Results:Significant differences were observed between low and high ALN burden groups in carcinoembryonic antigen levels, CA153 levels, tumor diameter, margins, enhancement characteristics, number of peritumoral thick blood vessels (TBVs), MRI-reported ALN loading status (MRI-ALN), and lymphovascular invasion status ( P<0.05). Multivariable logistic regression analysis showed that serum CA153 level ( OR=1.056, 95% CI 1.007-1.108, P=0.024), tumor margins ( OR=3.977, 95% CI 1.561-10.131, P=0.004), TBVs ( OR=3.058, 95% CI 1.217-7.684, P=0.017), and MRI-ALN ( OR=9.424, 95% CI 3.531-25.155, P<0.001) were independent risk factors predicting high ALN load in breast cancer ( P<0.05). The logistic regression model incorporating these four risk factors yielded optimal predictive performance for high ALN burden in breast cancer (AUC=0.854). DCA demonstrated optimal net clinical benefit within the threshold probability range of 13.3% to 72.7%. Conclusions:Tumor margins, TBVs, MRI-ALN, and CA153 levels are significantly associated with high ALN metastatic burden in breast cancer. Constructing a predictive model incorporating these features can significantly improve the accuracy of identifying high ALN burden.
