Machine learning-based clinical-radiomics model for predicting recurrence risk after radical surgery in sinonasal squamous cell carcinoma: a preliminary 2-year follow-up study
The present study developed an ML-based clinical-radiomics model that exhibited promising performance for predicting recurrence risk after radical surgery in SNSCC. Given its preliminary nature, this model has the potential to serve as an intelligent decision support tool, advancing precision medicine in SNSCC management.
Authors
- Jiawei Kuang
- Yin Wang (ORCID: https://orcid.org/0000-0002-4749-3768)
- Zuohua Tang (ORCID: https://orcid.org/0000-0003-4687-5905)
- Jingfeng Cheng (ORCID: https://orcid.org/0000-0002-1873-4083)
- Yue Geng (ORCID: https://orcid.org/0000-0003-2656-0586)
- D.Z. Shen (ORCID: https://orcid.org/0009-0000-0622-5794)
Institutions
- Shanghai Jiao Tong University (CN)
- Nantong University (CN)
- Affiliated Hospital of Nantong University (CN)
- Shanghai Ninth People's Hospital (CN)
- Eye & ENT Hospital of Fudan University (CN)
Publication Details
- Journal
- BMC Medical Imaging
- Published
- 2025-11-25
- DOI
- https://doi.org/10.1186/s12880-025-02022-3
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Shanghai Municipal Health Commission