Machine learning assisted radiomics in predicting postoperative occurrence of deep venous thrombosis in patients with gastric cancer
ML algorithms based on imaging information may prove to be novel non-invasive models for predicting postoperative occurrence of DVT in GC patients.
Authors
- Yuan Zeng (ORCID: https://orcid.org/0000-0002-3270-2459)
- Huiya Ying
- Jun Xu (ORCID: https://orcid.org/0000-0002-9541-6388)
- Xiangting Zhang (ORCID: https://orcid.org/0009-0009-6653-2601)
- Xian Song
- Yuhao Chen (ORCID: https://orcid.org/0000-0002-9363-6125)
- Yixiao Wang (ORCID: https://orcid.org/0000-0001-7855-0780)
- Dandan Zhu (ORCID: https://orcid.org/0000-0003-2296-3994)
- Fujun Yu (ORCID: https://orcid.org/0000-0001-5321-7228)
- Ruoru Zhou
Institutions
- Wenzhou Medical University (CN)
- First Affiliated Hospital of Wenzhou Medical University (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-02-07
- DOI
- https://doi.org/10.1186/s12885-025-13630-1
- Citations
- 4
- Primary Topic
- Venous Thromboembolism Diagnosis and Management
- Type
- article
- Field-Weighted Citation Impact
- 7.48
Funders
- National Natural Science Foundation of China
- Wenzhou Municipal Science and Technology Bureau
- Natural Science Foundation of Zhejiang Province