ORIGINAL RESEARCH article
Front. Oncol.
Sec. Breast Cancer
Volume 15 - 2025 | doi: 10.3389/fonc.2025.1619642
This article is part of the Research TopicAdvancing Breast Cancer Care Through Transparent AI and Federated Learning: Integrating Radiological, Histopathological, and Clinical Data for Diagnosis, Recurrence Prediction, and SurvivorshipView all 12 articles
Predicting Axillary Lymph Node Metastasis in Breast Cancer Using Ultrasound and Machine Learning with SHAP
Provisionally accepted
- Women and Children’s Hospital, School of Medicine, Xiamen University, Xiamen, China
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Background: Accurate preoperative prediction of axillary lymph node (ALN) metastasis in breast cancer is crucial for surgical planning and reducing morbidity. Conventional ultrasound and Doppler methods are limited by subjectivity, while existing machine learning (ML) models often lack interpretability and multi-center validation. Aim: To evaluate 11 ML algorithms and develop a validated model integrating ultrasound and Doppler features for ALN metastasis prediction, using SHapley Additive exPlanations (SHAP) for interpretability. Methods: This retrospective dual-center study included 303 patients from Xiamen (internal cohorts: 212 training, 91 validation) and 102 from Longyan (external validation). Features were extracted from preoperative ultrasound and Doppler images. Recursive feature elimination (RFE) and SHAP selected key predictors. Gradient Boosting was identified as optimal and compared to clinicopathological scores (Logical, Tumor, Tenon). Performance was assessed via AUC, calibration, decision curve analysis (DCA), and a web calculator was developed. Results: Five features—tumor diameter, cortex-to-hilum ratio, lymph node systolic/diastolic ratio, peak systolic velocity, and end-diastolic velocity—were selected. The combined model achieved AUCs of 0.981 (training), 0.975 (internal validation), and 0.987 (external validation), outperforming scores (AUCs 0.517–0.700). It showed superior calibration (Brier scores 0.045–0.061) and net benefit in DCA. Conclusion: The Gradient Boosting model with SHAP provides accurate, interpretable ALN metastasis prediction, supporting noninvasive risk stratification and personalized breast cancer management. Keywords: Breast cancer; Axillary lymph node metastasis; Ultrasound; Doppler ultrasound; Machine learning; SHapley Additive exPlanations
Keywords: breast cancer, Axillary lymph node metastasis, ultrasound, Doppler ultrasound, machine learning, Shapley additive explanations
Received: 28 Apr 2025; Accepted: 15 Sep 2025.
Copyright: © 2025 Bai, Zhong, Wu, Lin, Zhou and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Xiaohong Zhong, feitianlu.fpm@163.com
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