Preoperative comprehensive risk estimation for axillary lymph node metastasis in breast cancer: development and verification of a network-based prediction model - Baoqi Sun; Guangdong Shao; Mingming Shi; Zenggang Sun; Xiaolin Wang; Yining Song; Zheng Sun; Zhanjie Jin; Chunhong Xu; Guolou Li (Scientific Reports)
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.