Interpretable Machine Learning for Predicting Neoadjuvant Chemotherapy Response in Breast Cancer Using the Baseline Clinical and Pathological Characteristics
The ML-based pCR prediction model enables more accurate pCR prediction for BC patients at baseline, aiding in optimizing treatment strategies. Additionally, the interpretable SHAP framework enhances model transparency, fostering clinical trust, and understanding among doctors.
Authors
- Wenjie Xia (ORCID: https://orcid.org/0000-0002-2669-1482)
- Wuzhen Chen (ORCID: https://orcid.org/0000-0003-0210-6645)
- Miaochun Zhong
- Chengyan Han (ORCID: https://orcid.org/0009-0005-1785-594X)
- Haibo Zhang (ORCID: https://orcid.org/0000-0002-5134-4167)
- Wei Zhang (ORCID: https://orcid.org/0000-0003-0459-5382)
- Jing Zhang (ORCID: https://orcid.org/0000-0001-8861-7336)
- Hongjun Yuan (ORCID: https://orcid.org/0000-0002-4656-4077)
- S. Fang (ORCID: https://orcid.org/0000-0001-7883-4337)
- Mingxiang Kong
Institutions
- Soochow University (CN)
- Weifang People's Hospital (CN)
- Zhejiang Provincial People's Hospital (CN)
- Second Affiliated Hospital of Zhejiang University (CN)
- Hangzhou Medical College (CN)
Publication Details
- Journal
- Cancer Medicine
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1002/cam4.71221
- Primary Topic
- AI in cancer detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Medical Science and Technology Project of Zhejiang Province
- Zhejiang Province Public Welfare Technology Application Research Project
- Science and Technology Program of Zhejiang Province