Integrating central-slice anatomy and MRF-mapped radiomics dynamics for lung adenocarcinoma subtyping
By fusing static anatomical information with dynamic radiomics evolution maps, DRFN offers both high classification accuracy and transparent interpretability. Our framework thus holds promise as an intelligent diagnostic aid for lung adenocarcinoma subtyping in clinical practice.
Authors
- Yang Liu (ORCID: https://orcid.org/0000-0003-4651-2127)
- He Ren
- Weiwei Shi (ORCID: https://orcid.org/0000-0003-4551-8420)
- Yina Zhang
- Chenxiao Bai
- Chengyu Wu
- Chengcheng Fan
- Jianguo Li
Institutions
- Shanghai University of Medicine and Health Sciences (CN)
- Shanghai Sunshine Rehabilitation Center (CN)
Publication Details
- Journal
- Quantitative Imaging in Medicine and Surgery
- Published
- 2026-01-28
- DOI
- https://doi.org/10.21037/qims-2025-1120
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00