Predicting breast cancer response to neoadjuvant chemotherapy with ultrasound-based deep learning radiomics models —— dual-center study
This study successfully developed a fusion model for predicting early tumor response in breast cancer patients receiving NAC. The model combines deep learning methods based on early NAC ultrasound images and demonstrates high predictive performance.
Authors
- Ruoxin Li (ORCID: https://orcid.org/0000-0002-6142-5761)
- Qiao Zou (ORCID: https://orcid.org/0000-0001-6708-8906)
- Haibo Luo
- J. Liu (ORCID: https://orcid.org/0000-0003-4731-7657)
- Changchun Wang (ORCID: https://orcid.org/0000-0003-3183-2160)
- Y. Li (ORCID: https://orcid.org/0009-0001-1113-8174)
- Xiaoling Leng (ORCID: https://orcid.org/0009-0004-4076-7668)
- Zhifan Yuan
- Xia Li (ORCID: https://orcid.org/0000-0001-8665-7983)
- Junjun Chen (ORCID: https://orcid.org/0000-0001-8930-6011)
- Zhizhong He
Institutions
- Xinjiang Medical University (CN)
- Guangdong Medical College (CN)
- Tumor Hospital of Xinjiang Medical University (CN)
- Dongguan People’s Hospital (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-11-10
- DOI
- https://doi.org/10.1186/s12885-025-15148-y
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- National Natural Science Foundation of China