Multi-dimensional interpretable deep learning-radiomics based on intra-tumoral and spatial habitat for preoperative prediction of thymic epithelial tumours risk categorisation
The RDLCSM, which combines whole-tumor radiomics, 2D and 3D deep learning, clinical-visual radiology, and subregional omics, can be used as a non-invasive tool to predict thymoma risk classification.
Authors
- Can Cui (ORCID: https://orcid.org/0000-0003-1139-0182)
- Yuhua Yang (ORCID: https://orcid.org/0000-0002-8880-0525)
- Minjing Zuo (ORCID: https://orcid.org/0000-0003-0479-7080)
- Jiayi Wang (ORCID: https://orcid.org/0000-0002-8314-424X)
- Meiling Cheng (ORCID: https://orcid.org/0000-0001-6405-0697)
- Jia Cheng
- Huijie Huang (ORCID: https://orcid.org/0000-0002-0926-8471)
Institutions
- Nanchang University (CN)
- Jiangxi Provincial Academy of Medical Sciences (CN)
- First Affiliated Hospital of Jiangxi Medical College (CN)
- Second Affiliated Hospital of Nanchang University (CN)
- First Affiliated Hospital of Gannan Medical University (CN)
Publication Details
- Journal
- Acta Oncologica
- Published
- 2025-03-13
- DOI
- https://doi.org/10.2340/1651-226x.2025.42982
- Citations
- 4
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 5.44