Prediction of nonsentinel lymph node metastasis in breast cancer patients based on machine learning

Abstract

Background: Develop the best machine learning (ML) model to predict nonsentinel lymph node metastases (NSLNM) in breast cancer patients.

Methods: From June 2016 to August 2022, 1005 breast cancer patients were included in this retrospective study. Univariate and multivariate analyses were performed using logistic regression. Six ML models were introduced, and their performance was compared.

Results: NSLNM occurred in 338 (33.6%) of 1005 patients. The best ML model was XGBoost, whose average area under the curve (AUC) based on 10-fold cross-verification was 0.722. It performed better than the nomogram, which was based on logistic regression (AUC: 0.764 vs. 0.706).

Conclusions: The ML model XGBoost can well predict NSLNM in breast cancer patients.

Keywords: Breast cancer; Machine learning; Nomogram; Nonsentinel lymph node metastasis.