Deep learning model for predicting the RAS oncogene status in colorectal cancer liver metastases
The DL model demonstrated satisfactory predictive performance, aiding clinicians in noninvasively predicting the RAS gene status for informed treatment decisions.
Authors
- Yaoxian Xiang
- Chao An (ORCID: https://orcid.org/0000-0003-4029-2709)
- Xuelei He (ORCID: https://orcid.org/0000-0002-8624-0130)
- Baogen Zhang (ORCID: https://orcid.org/0000-0001-6110-7408)
- Ting Xu (ORCID: https://orcid.org/0000-0002-7851-3389)
- Kai You Wang (ORCID: https://orcid.org/0000-0002-6170-4744)
- Siyu Zhu (ORCID: https://orcid.org/0000-0001-5352-2273)
- Jing Wang (ORCID: https://orcid.org/0000-0002-9996-0815)
- Haibin Zhu
- Dong Yan
- Kangjie Wang (ORCID: https://orcid.org/0009-0002-7063-5610)
Institutions
- Peking University (CN)
- Northwest University (CN)
- Peking University Cancer Hospital (CN)
- Beijing Luhe Hospital Affiliated to Capital Medical University (CN)
Publication Details
- Journal
- Journal of Cancer Research and Therapeutics
- Published
- 2025-05-01
- DOI
- https://doi.org/10.4103/jcrt.jcrt_1910_24
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08