Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model
The multimodal radiomics and deep learning models developed in this study, incorporating various machine learning algorithms, offer significant value for the preoperative prediction of ALNM in BC.
Authors
- Shixiu Wu (ORCID: https://orcid.org/0000-0003-3731-7274)
- Fuyu Guo (ORCID: https://orcid.org/0000-0003-1478-0362)
- Yangang Zhang (ORCID: https://orcid.org/0000-0002-6317-3166)
- Junrong Yan (ORCID: https://orcid.org/0000-0001-9750-7518)
- Wei Yao (ORCID: https://orcid.org/0000-0003-4672-4584)
- Xiaoqian Deng
- Xiaojun Zhang (ORCID: https://orcid.org/0000-0003-3993-0478)
- Yue Wang (ORCID: https://orcid.org/0000-0001-5918-7525)
- Yue Peng (ORCID: https://orcid.org/0000-0002-8541-0265)
- Shiwei Sun (ORCID: https://orcid.org/0000-0002-0351-3720)
Institutions
- Handan College (CN)
- Shanxi Medical University (CN)
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
- Xinzhou Teachers University (CN)
- Shanxi Academy of Medical Sciences (CN)
- Tongji Hospital (CN)
- Huazhong University of Science and Technology (CN)
- Shanxi Datong University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2024-12-13
- DOI
- https://doi.org/10.3389/fimmu.2024.1482020
- Citations
- 8
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 3.23