Prediction of lymphovascular invasion of gastric cancer based on contrast-enhanced computed tomography radiomics
The combined model had the highest diagnostic effectiveness, and the nomogram established by this model had good performance. It can provide a reliable prediction method for individual treatment of LVI in GC before surgery.
Authors
- Siyu Zhen (ORCID: https://orcid.org/0000-0002-7372-470X)
- Changhua Liang
- Xiaohuan Liu (ORCID: https://orcid.org/0000-0002-3402-8357)
- Ping Li (ORCID: https://orcid.org/0000-0001-8930-3670)
- H. Wei (ORCID: https://orcid.org/0000-0003-1973-4912)
- Ran Song (ORCID: https://orcid.org/0000-0001-6373-7411)
- Xiang-Yan Kong
- Wen-Hua Fan
- Yong Wei (ORCID: https://orcid.org/0000-0003-3019-2173)
Institutions
- Henan Institute of Technology (CN)
- First Affiliated Hospital of Xinxiang Medical University (CN)
- Xinxiang University (CN)
- Henan Medical University (CN)
Publication Details
- Journal
- Frontiers in Oncology
- Published
- 2024-09-05
- DOI
- https://doi.org/10.3389/fonc.2024.1389278
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.40
Funders
- Henan Provincial Science and Technology Research Project