Development and validation of a pathomics-driven machine learning model for individualized prediction of neoadjuvant chemotherapy response and early recurrence in HR-positive, HER2-negative breast cancer
Integrating pathomic features with clinical variables via machine learning enables robust pretreatment prediction of NAC efficacy and short-term recurrence in HR-positive, HER2-negative breast cancer. This approach has the potential to offer a clinically practical tool to optimize individualized therapy and improve patient management, highlighting the translational value of AI-powered digital pathology in breast cancer care.
Authors
- T. Wang
- Jiaxiang Liu (ORCID: https://orcid.org/0000-0003-1764-0322)
- Chao Shang (ORCID: https://orcid.org/0000-0003-3905-4631)
- Qingyao Shang
- Xubin Dong (ORCID: https://orcid.org/0000-0001-7389-9801)
- Chenxuan Yang (ORCID: https://orcid.org/0000-0001-9064-9740)
- Guangyu Li (ORCID: https://orcid.org/0000-0001-5172-9676)
- Zhanyu Wang (ORCID: https://orcid.org/0000-0001-9187-6419)
- Jiaxian Yue
- Xiyu Kang
- D Yang
- P. Yuan
- Jianming Ying
- Wei Wang
- Xin Wang
- Shuhao Wang
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- National Clinical Research (US)
- Thornbury Hospital (GB)
- Beijing Tsinghua Chang Gung Hospital (CN)
- Tsinghua University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2026-02-23
- DOI
- https://doi.org/10.3389/fonc.2026.1770037
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- National Science and Technology Major Project
- Beijing Nova Program