Predicting early recurrence of colorectal cancer liver metastases: an integrative approach using radiomics and machine learning
The imaging omics model and the combined model have good predictive efficacy for the recurrence of colorectal cancer liver metastases within one year, and can be used to assist in the clinical stratification of postoperative patients and identify high-risk factors for poor prognosis.
Authors
- Yunxia Huang (ORCID: https://orcid.org/0000-0002-8542-9804)
- Yan‐zong Lin
- Chunkang Yang
- Zhaohui Liu
- Xiaobin Feng (ORCID: https://orcid.org/0000-0002-3580-4696)
Institutions
- Fujian Medical University (CN)
- First Affiliated Hospital of Xiamen University (CN)
- Fujian Provincial Cancer Hospital (CN)
- Beijing Tsinghua Chang Gung Hospital (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2025-11-14
- DOI
- https://doi.org/10.3389/fonc.2025.1613093
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China