An explainable predictive machine learning model for axillary lymph node metastasis in breast cancer based on multimodal data: A retrospective single-center study
The Naïve Bayes model demonstrated robust performance and interpretability in predicting ALNM. As a non-invasive and explainable tool, it provides clinical value for risk stratification, accurate diagnosis, and the development of individualized treatment strategies.
Authors
- Yunfeng Wu (ORCID: https://orcid.org/0000-0002-8059-0842)
- Qing Xia (ORCID: https://orcid.org/0000-0003-4373-2722)
- Ying Che (ORCID: https://orcid.org/0009-0008-0113-2984)
- Haining Yu (ORCID: https://orcid.org/0000-0003-3448-7956)
- Yuxi Liu (ORCID: https://orcid.org/0000-0002-6105-8594)
- Hao He (ORCID: https://orcid.org/0000-0002-8918-4023)
Institutions
- Weifang Medical University (CN)
- Dalian Medical University (CN)
- First Affiliated Hospital of Dalian Medical University (CN)
Publication Details
- Journal
- Journal of Translational Medicine
- Published
- 2025-08-11
- DOI
- https://doi.org/10.1186/s12967-025-06686-x
- Citations
- 2
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 4.96