Bi-modal ultrasound radiomics and habitat analysis enhanced the pre-operative prediction of axillary lymph node burden in patients with early-stage breast cancer
Habitat analysis based on ultrasound might be a potential method to visualize the intratumoral heterogeneity of breast lesions. The machine learning models based on SWE radiomics with habitat analysis could enhance the ability of prediction lymph node burden in patients with early-stage breast cancer, which could be a promising approach to make clinical decisions.
Authors
- Jing Xu (ORCID: https://orcid.org/0000-0002-5168-3388)
- Qiaoling Zhong
- Aijiao Yi (ORCID: https://orcid.org/0000-0001-5348-377X)
- Zhenwen Chen (ORCID: https://orcid.org/0000-0003-4678-3437)
- Bin Wang (ORCID: https://orcid.org/0000-0001-7771-5360)
- Qi Pan (ORCID: https://orcid.org/0009-0009-9381-5826)
- Xiaoyan Ou
- Yin Wang (ORCID: https://orcid.org/0000-0002-7898-2361)
Institutions
- Shanghai University of Traditional Chinese Medicine (CN)
- Yueyang Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-05-29
- DOI
- https://doi.org/10.3389/fonc.2025.1607442
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36