Deep learning radiomics and 18F-FDG PET/CT imaging: mediastinal lymph node characteristics as predictors of metastasis in non-small cell lung cancer
The PET/CT-based DLR model accurately differentiates benign from malignant lymph nodes, outperforming conventional methods. Combining DenseNet121-derived features with radiomics improves staging accuracy and aids personalized treatment planning.
Authors
- Dalong Wang (ORCID: https://orcid.org/0000-0002-0869-8701)
- Yu Zhao (ORCID: https://orcid.org/0000-0001-9025-2014)
- Fuqing Duan (ORCID: https://orcid.org/0000-0002-3849-8532)
- Li Yan (ORCID: https://orcid.org/0000-0001-5427-2552)
- Minghui Zhang (ORCID: https://orcid.org/0000-0002-8253-1379)
- Rui Zhang (ORCID: https://orcid.org/0000-0003-1370-8289)
- Ping Li (ORCID: https://orcid.org/0000-0002-4492-4825)
- Xuewei Wang (ORCID: https://orcid.org/0009-0009-0341-2602)
- Haoran Zu (ORCID: https://orcid.org/0000-0002-9721-7593)
Institutions
- Harbin Medical University (CN)
- Third Affiliated Hospital of Harbin Medical University (CN)
Publication Details
- Journal
- Translational Lung Cancer Research
- Published
- 2025-10-01
- DOI
- https://doi.org/10.21037/tlcr-2025-650
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72