Preoperative comprehensive risk estimation for axillary lymph node metastasis in breast cancer: development and verification of a network-based prediction model

Abstract

To prevent the overaggressive treatment of axillary lymph nodes (ALNs) in breast cancer, it is necessary to develop a convenient analysis method that accurately and comprehensively reflects whether ALNs are metastatic or nonmetastatic. We retrospectively analyzed data from patients who underwent surgery for breast cancer at the Weifang Hospital of Traditional Chinese Medicine between January 2019 and June 2023. Binary logistic regression analysis was used to predict the metastasis status of ALNs. The developmental data set included 531 patients (January 2019-June 2023). The validation set included separate data points (n = 178, January 2019-June 2023). Multivariate analysis revealed that positive findings on breast physical examination, ultrasound grades of ALNs, lymphovascular invasion, and Her-2 status had significant predictive value for metastatic ALNs. Based on these findings, a 5-grade risk scoring system and 3-level management recommendations were developed. The risk of metastasis ranged from 11.25 to 93.46%, which was positively correlated with an increase in risk grade. The areas under the curve of the development and validation sets were 0.895 and 0.865, respectively. Ultimately, a convenient, accurate and comprehensive web-based predictive model was constructed using various breast cancer clinical, imaging and pathological criteria to stratify ALNs according to the metastasis probability.

Keywords: Axillary lymph node status; Breast cancer; Metastasis probability; Network model; Preoperative estimation.

Fig. 1

The receiver operating characteristic (ROC) curve of the research. (a) Performance of the MSKCC model applied to the verification group (n = 613). The area under the ROC curve was 0.817, and the 95% confidence interval (95% CI) was 0.784–0.851, p < 0.001. (b) Performance of the new prediction model based on the development set (n = 531). The area under the ROC curve was 0.895, and the 95% confidence interval (95% CI) was 0.867–0.923, p < 0.001. (c) Performance of the novel scoring system based on the risk score (n = 709). The area under the ROC curve was 0.887, and the 95% confidence interval (95% CI) was 0.862–0.912, p < 0.001. (d) Performance of the validation set (n = 178). The area under the ROC curve was 0.865, and the 95% confidence interval (95% CI) was 0.810–0.920, p < 0.001.

Fig. 2

Online risk calculation system. An online resource is available at https://pay-test.coalapay.cn/Coala/thyroid/BCD_EN.html for the easy calculation and summation of the metastasis risk stratification of ALNs using the clinical data characteristics of breast cancer.