Quantitative vascular feature-based multimodality prediction model for multi-origin malignant cervical lymphadenopathy
The National Natural Science Foundation of China (82171955; 62371476; 82441027); the China Department of Science and Technology (2023YFE0204300); and the R&D project of Pazhou Lab (HuangPu) (2023K0606).
Authors
- Xi Lin (ORCID: https://orcid.org/0000-0003-2984-1703)
- Yao Lu (ORCID: https://orcid.org/0000-0002-9873-8753)
- Ruixia Hong
- Cuiju Yan (ORCID: https://orcid.org/0000-0003-3490-6639)
- Fang Li (ORCID: https://orcid.org/0000-0002-8960-8404)
- Rui Li (ORCID: https://orcid.org/0000-0002-5716-9259)
- Chunyan Li (ORCID: https://orcid.org/0000-0001-7159-3059)
- Tingting Deng (ORCID: https://orcid.org/0000-0002-1003-8710)
- Feng Han (ORCID: https://orcid.org/0000-0002-9337-7936)
- Huiling Xiang (ORCID: https://orcid.org/0000-0001-5734-4080)
- Qingguang Lin (ORCID: https://orcid.org/0000-0001-9798-9238)
- Jinjing Ou
Institutions
- Sun Yat-sen University (CN)
- Sun Yat-sen University Cancer Center (CN)
- Chongqing Cancer Hospital (CN)
Publication Details
- Journal
- EClinicalMedicine
- Published
- 2025-02-12
- DOI
- https://doi.org/10.1016/j.eclinm.2025.103085
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08
Funders
- National Natural Science Foundation of China