Machine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer
This study is the first to introduce ALI into the prediction model for BC completing NACT and develop a large-sample model based on XGBoost. Owing to the particularity of the indicators, training and validation were conducted on real clinical data.
Authors
- Yuefei Zhou (ORCID: https://orcid.org/0000-0002-5371-298X)
- Quan Yuan (ORCID: https://orcid.org/0009-0003-8060-2158)
- Xiaoqiao Cui
- Feng Liu
- Yu Hao
- Yao Qian
- Ming Niu
- Rongjie Ye (ORCID: https://orcid.org/0009-0003-8631-3648)
Institutions
- Fujian Medical University (CN)
- Harbin Medical University (CN)
- First Affiliated Hospital of Xiamen University (CN)
- Third Affiliated Hospital of Harbin Medical University (CN)
Publication Details
- Journal
- Annals of Medicine
- Published
- 2025-08-03
- DOI
- https://doi.org/10.1080/07853890.2025.2541316
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 6.80