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.

SUBMITTER: Xiu Y 

PROVIDER: S-EPMC10416453 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature