A foundation model for predicting outcomes of neoadjuvant chemotherapy in breast cancer
This foundation model integrating histopathological and clinical data enables accurate prediction of NAC response and long-term outcomes in breast cancer. Beyond prognostication, it offers a clinically actionable tool for identifying high-risk non-pCR patients who may benefit from intensive adjuvant chemotherapy, advancing precision oncology in the neoadjuvant setting.
Authors
- Huiqin Xu (ORCID: https://orcid.org/0000-0002-8856-3048)
- Qing Peng (ORCID: https://orcid.org/0000-0002-2238-8400)
- Zebang Zhang
- Ruichong Lin (ORCID: https://orcid.org/0000-0003-0161-5974)
- Yanqiong Liu (ORCID: https://orcid.org/0000-0001-9159-4278)
- Zhenhui Zhao
- Puxiang Lai (ORCID: https://orcid.org/0000-0003-4811-2012)
- Jingjing Han (ORCID: https://orcid.org/0000-0001-5916-3187)
- Olivia Monteiro (ORCID: https://orcid.org/0000-0003-0191-5547)
- J L Liang
- Daniel T. Baptista‐Hon (ORCID: https://orcid.org/0000-0002-5358-1035)
- Yongjian Chen (ORCID: https://orcid.org/0000-0002-2930-8450)
- Yunfang Yu (ORCID: https://orcid.org/0000-0003-2579-6220)
- Haizhu Chen (ORCID: https://orcid.org/0000-0001-9810-1050)
- Xingbin Hu (ORCID: https://orcid.org/0000-0002-3952-3373)
- Juanjuan Yong
- Kaicong Zhang
- Herui Yao (ORCID: https://orcid.org/0000-0001-5520-6469)
- Tang Li (ORCID: https://orcid.org/0000-0002-3344-7277)
- Tong Li (ORCID: https://orcid.org/0000-0003-4956-765X)
- Zehua Wang (ORCID: https://orcid.org/0000-0002-0769-8865)
- Lin Yang (ORCID: https://orcid.org/0009-0006-0454-4509)
- Kai Chen (ORCID: https://orcid.org/0000-0002-7052-4681)
- Yunjie Zeng (ORCID: https://orcid.org/0000-0003-3815-0461)
- Lijun Zheng (ORCID: https://orcid.org/0000-0002-2890-7494)
- Xiaoxi Zhang
- Zhenjun Huang (ORCID: https://orcid.org/0009-0000-1474-2388)
- Wei Ren
- Zifan He
- Luhui Mao
Institutions
- Macau University of Science and Technology (MO)
- Hong Kong Polytechnic University (HK)
- Sun Yat-sen University (CN)
- University of Dundee (GB)
- Karolinska Institutet (SE)
- Guangzhou University (CN)
- Sun Yat-sen Memorial Hospital (CN)
- Guangzhou Electronic Technology (China) (CN)
- First Affiliated Hospital of Jinan University (CN)
- China United Network Communications Group (China) (CN)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2025-11-11
- DOI
- https://doi.org/10.1097/js9.0000000000003999
- Citations
- 1
- Primary Topic
- Breast Cancer Treatment Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.94
Funders
- National Natural Science Foundation of China
- Sun Yat-sen University
- Innovation and Technology Fund
- Guangdong Provincial Development and Reform Commission
- Science and Technology Planning Project of Guangdong Province
- Basic and Applied Basic Research Foundation of Guangdong Province