ColoLDB: a machine learning-based predictive model for colorectal cancer using routine laboratory parameters
Our research findings indicate that eight laboratory test indicators may be related the risk of developing CRC. Our RF diagnostic ColoLDB model is an innovative and practical tool that effectively predicts the occurrence of CRC, enhancing the diagnostic efficiency for this disease. This method holds promise as a valuable tool for diagnosing CRC.
Authors
- Kun Deng (ORCID: https://orcid.org/0000-0003-1182-4526)
- Xu Dong Zhang (ORCID: https://orcid.org/0000-0001-9457-8003)
- Chenxi Zhang (ORCID: https://orcid.org/0000-0003-4566-9843)
- Jiayi Liu (ORCID: https://orcid.org/0000-0003-1819-2998)
- Hongyan Han (ORCID: https://orcid.org/0000-0002-2508-9924)
- Jiangtao Mou
- Xuedong Tong
Institutions
- Army Medical University (CN)
- Third People 's Hospital of Chongqing (CN)
- Roche (China) (CN)
- The Affiliated Yongchuan Hospital of Chongqing Medical University (CN)
- Chongqing Medical University (CN)
Publication Details
- Journal
- Journal of Gastrointestinal Oncology
- Published
- 2026-02-01
- DOI
- https://doi.org/10.21037/jgo-2025-611
- Primary Topic
- Colorectal Cancer Screening and Detection
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Centre Scientifique et Technique du Bâtiment