ArticleCancers2023
Applying Explainable Machine Learning Models for Detection of Breast Cancer Lymph Node Metastasis in Patients Eligible for Neoadjuvant Treatment.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Leveraging immune and clinicopathological profiles with machine learning to predict axillary lymph node metastasis in breast cancer patients.Breast cancer research : BCR · 2026Observational
- Deep learning on routine full-breast mammograms enhances lymph node metastasis prediction in early breast cancer.NPJ digital medicine · 2025Article
- Predictive modeling of breast cancer-related lymphedema using machine learning algorithms.Gland surgery · 2024Article
- Identification of sentinel lymph node macrometastasis in breast cancer by deep learning based on clinicopathological characteristics.Scientific reports · 2024Article
- Explainable artificial intelligence in breast cancer detection and risk prediction: A systematic scoping review.Cancer innovation · 2024Article
- Advances in the application of computational pathology in diagnosis, immunomicroenvironment recognition, and immunotherapy evaluation of breast cancer: a narrative review.Journal of cancer research and clinical oncology · 2023Review
- Editorial for the Special Issue "Breast Cancer-Therapeutic Challenges, Research Strategies and Novel Diagnostics".Cancers · 2023Article
- Review
- Predicting Radiation Esophagitis in Patients Undergoing Synchronous Boost Radiotherapy Post-Breast-Conserving Surgery.Dose-response : a publication of International Hormesis SocietyArticle
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Authors and funding
12 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundDue to recent changes in breast cancer treatment strategy, significantly more patients are treated with neoadjuvant systemic therapy (NST). Radiological methods do not precisely determine axillary lymph node status, with up to 30% of patients being misdiagnosed. Hence, supplementary methods for lymph node status assessment are needed. This study aimed to apply and evaluate machine learning models on clinicopathological data, with a focus on patients meeting NST criteria, for lymph node metastasis prediction.
methodsFrom the total breast cancer patient data (
resultsIn the NST-criteria group, random forest achieved the highest performance (AUC: 0.793 [0.713, 0.865]), while in the total study population, XGBoost performed the best (AUC: 0.762 [0.726, 0.795]). Shapley values identified tumor size, Ki-67, and patient age as the most important predictors.
conclusionTree-based models achieve a good performance in assessing lymph node status. Such models can lead to more accurate disease stage prediction and consecutively better treatment selection, especially for NST patients where radiological and clinical findings are often the only way of lymph node assessment.
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