An exploratory study on predicting HER2-positive expression status of breast cancer using ultrasound radiomics combined with machine learning models
Machine learning models based on ultrasound radiomics features have potential clinical value for predicting HER2-positive status in BC.
Authors
- Yangjie Xiao (ORCID: https://orcid.org/0000-0001-5471-7320)
- Yuanyuan Huang (ORCID: https://orcid.org/0000-0002-7971-6313)
- Xiaoqing Yu (ORCID: https://orcid.org/0000-0002-9461-7559)
- Feng Lu (ORCID: https://orcid.org/0000-0002-4498-190X)
- Yan Shen (ORCID: https://orcid.org/0000-0002-3204-7095)
- Xiaohong Fu (ORCID: https://orcid.org/0000-0001-7356-960X)
- Qingqing Chen (ORCID: https://orcid.org/0000-0003-2268-1938)
- Xinran Zhang (ORCID: https://orcid.org/0000-0002-8161-1422)
- Jing Hu (ORCID: https://orcid.org/0000-0001-7604-6732)
- Sha‐Sha Yuan
Institutions
- University of Shanghai for Science and Technology (CN)
- Second Military Medical University (CN)
- Shanghai University of Traditional Chinese Medicine (CN)
- Shuguang Hospital (CN)
- Shanghai Pudong New Area Gongli Hospital (CN)
- China Medical University (CN)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-10-23
- DOI
- https://doi.org/10.1371/journal.pone.0334909
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00