Biomarker-based and interpretable machine learning framework for predicting pathological stage in gastric cancer: A retrospective analysis
We developed a robust, interpretable machine learning model for GC staging using routine blood tests and derived ratio features. The model demonstrated excellent discrimination, interpretability, and clinical utility, offering a practical tool for personalized risk stratification and treatment planning.
Authors
- Guanmo Liu
- Yanni He (ORCID: https://orcid.org/0000-0002-8323-2979)
- Weiming Kang (ORCID: https://orcid.org/0000-0002-6736-4728)
- Y Y Wang
- Xin Ye (ORCID: https://orcid.org/0000-0001-8355-4516)
- Chenggang Zhang (ORCID: https://orcid.org/0000-0003-0349-4854)
- Jie Li (ORCID: https://orcid.org/0000-0003-2555-6331)
- Zicheng Zheng
- Sen Yang (ORCID: https://orcid.org/0000-0003-3125-6626)
Institutions
- Chinese Academy of Medical Sciences & Peking Union Medical College (CN)
- Peking Union Medical College Hospital (CN)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251388394
- Citations
- 2
- Primary Topic
- Gastric Cancer Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 3.04