Multi-channel deep learning radiomics model based on contrast-enhanced CT for predicting postoperative prognosis in laryngeal carcinoma
The proposed multi-channel deep learning radiomics model showed promising performance for predicting OS in laryngeal cancer patients. This approach may support individualized risk stratification and assist clinical decision-making in patients with laryngeal carcinoma.
Authors
- Qi Zhang (ORCID: https://orcid.org/0000-0002-7918-8952)
- Jun Zhang (ORCID: https://orcid.org/0000-0001-7835-9871)
- Liang Li (ORCID: https://orcid.org/0000-0001-9994-4690)
- Weili Wang (ORCID: https://orcid.org/0000-0002-1627-3025)
- Hua Zhang (ORCID: https://orcid.org/0000-0002-5657-5052)
- Haomiao Ma
- Qian Zhang (ORCID: https://orcid.org/0009-0001-6172-4411)
- Lingjie Wang (ORCID: https://orcid.org/0000-0003-1003-3300)
- Zhilin Li (ORCID: https://orcid.org/0000-0003-4047-940X)
- Wenfeng Wei
- Lingyu Zhang (ORCID: https://orcid.org/0000-0002-3121-2984)
- Yanhong Hao (ORCID: https://orcid.org/0000-0002-8006-5263)
Institutions
- Shanxi Medical University (CN)
- Shanxi Provincial Cancer Hospital (CN)
- First Hospital of Shanxi Medical University (CN)
Publication Details
- Journal
- BMC Cancer
- Published
- 2025-10-16
- DOI
- https://doi.org/10.1186/s12885-025-14912-4
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Shanxi Medical University