Predictive Performance of Radiomics-Based Machine Learning for Colorectal Cancer Recurrence Risk: Systematic Review and Meta-Analysis
Radiomics-based machine learning models, especially those integrating radiomics and clinical features, showed promising predictive performance for CRC recurrence risk. However, this study has several limitations, such as moderate study quality, limited sample size, and high heterogeneity in modeling approaches. These findings suggest the potential clinical value of integrated models in risk stratification and their potential to enhance personalized treatment, though further high-quality prospective studies are warranted.
Authors
- Zhaolan Liu (ORCID: https://orcid.org/0000-0003-2992-1578)
- Liming Hu (ORCID: https://orcid.org/0009-0005-5389-6060)
- Zhijun Bu (ORCID: https://orcid.org/0009-0006-0051-1662)
- Zhuoyan Shi (ORCID: https://orcid.org/0009-0003-5708-7709)
- Jing An (ORCID: https://orcid.org/0009-0005-8754-8849)
- Jianping Liu (ORCID: https://orcid.org/0000-0002-0320-061X)
- Yuan Sun (ORCID: https://orcid.org/0009-0008-0519-5907)
- Tae-Hun Kim (ORCID: https://orcid.org/0000-0003-3931-7139)
- Bo Li (ORCID: https://orcid.org/0009-0002-4544-9648)
- Chuanlan Ju (ORCID: https://orcid.org/0009-0007-7176-7726)
- Huiyi Sun (ORCID: https://orcid.org/0009-0004-0705-9192)
Institutions
- Shandong University of Traditional Chinese Medicine (CN)
- Beijing University of Chinese Medicine (CN)
- Kyung Hee University (KR)
- Yantai Infectious Diseases Hospital (CN)
- Dongzhimen Hospital Affiliated to Beijing University of Chinese Medicine (CN)
- Yantaishan Hospital (CN)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2025-11-28
- DOI
- https://doi.org/10.2196/78644
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36