ArticleWorld journal of surgical oncology2023
Prediction of nonsentinel lymph node metastasis in breast cancer patients based on machine learning.
Article in World journal of surgical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Development and interpretation of machine learning-based prognostic models for predicting high-risk prognostic pathological components in pulmonary nodules: integrating clinical features, serum tumor marker and imaging features.Journal of cancer research and clinical oncology · 2025Article
- Development of fully automated deep-learning-based approach for prediction of sentinel lymph node metastasis in breast cancer patients using ultrasound imaging.Frontiers in oncology · 2025Article
- Predictive modeling of breast cancer-related lymphedema using machine learning algorithms.Gland surgery · 2024Article
- Machine learning algorithms able to predict the prognosis of gastric cancer patients treated with immune checkpoint inhibitors.World journal of gastroenterology · 2024Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDevelop the best machine learning (ML) model to predict nonsentinel lymph node metastases (NSLNM) in breast cancer patients.
methodsFrom 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.
resultsNSLNM 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).
conclusionsThe ML model XGBoost can well predict NSLNM in breast cancer patients.
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