Development of fully automated deep-learning-based approach for prediction of sentinel lymph node metastasis in breast cancer patients using ultrasound imaging
Our novel predictive model exhibited superior performance compared to both senior and junior radiologists in predicting SLN metastasis. Its capability for automatic segmentation and prediction highlights its potential for clinical applications.
Authors
- Jing Wu (ORCID: https://orcid.org/0000-0003-2793-0032)
- Taixia Liu
- Wei Wei (ORCID: https://orcid.org/0000-0003-0847-4799)
- Xiaobo Chen (ORCID: https://orcid.org/0000-0001-8755-6199)
- Dong Liu (ORCID: https://orcid.org/0000-0001-9100-2906)
- Qingling Zhang (ORCID: https://orcid.org/0000-0002-5287-264X)
- Guojia Zhao
- Xiangming Zhu (ORCID: https://orcid.org/0000-0001-5623-3477)
Institutions
- University of Science and Technology of China (CN)
- Shandong University (CN)
- Linyi People's Hospital (CN)
- Shanghai Traditional Chinese Medicine Hospital (CN)
- First Affiliated Hospital of Wannan Medical College (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-08-28
- DOI
- https://doi.org/10.3389/fonc.2025.1592521
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00