Development and validation of machine-learning model based on dynamic tumor markers in predicting pathological complete response after neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer: a multicenter cohort study
As a novel marker based on dynamic changes in CEA and CA19-9 levels, DTMS effectively predicted pCR within the XGB model, providing clinicians with a practical tool for treatment decision-making regarding LARC.
Authors
- Guoxian Guan (ORCID: https://orcid.org/0000-0001-9883-3139)
- Tao Peng (ORCID: https://orcid.org/0000-0001-5260-7128)
- Shoufeng Li
- Ye Wang (ORCID: https://orcid.org/0000-0002-5423-6196)
- Bin Chen (ORCID: https://orcid.org/0000-0002-2882-9756)
- Changqing Zeng (ORCID: https://orcid.org/0000-0002-6120-1725)
- Xing Liu (ORCID: https://orcid.org/0000-0001-5504-7947)
- Xingrong Lu (ORCID: https://orcid.org/0000-0002-7822-6953)
- Shaoqing Zheng (ORCID: https://orcid.org/0000-0003-3236-8662)
- Jinfu Zhuang (ORCID: https://orcid.org/0000-0001-8347-7776)
- Zhen Pan (ORCID: https://orcid.org/0009-0006-1940-7240)
Institutions
- Fujian Medical University (CN)
- First Affiliated Hospital of Liaoning Medical University (CN)
- First Affiliated Hospital of Fujian Medical University (CN)
- Fujian Provincial Hospital (CN)
- Union Hospital (CN)
Publication Details
- Journal
- International Journal of Colorectal Disease
- Published
- 2025-09-26
- DOI
- https://doi.org/10.1007/s00384-025-04993-9
- Citations
- 1
- Primary Topic
- Colorectal Cancer Surgical Treatments
- Type
- article
- Field-Weighted Citation Impact
- 1.05
Funders
- National Natural Science Foundation of China
- Fujian Medical University