A CNN–transformer fusion network for predicting high‐grade patterns in stage IA invasive lung adenocarcinoma
The CNN-transformer fusion network, based on radiomics and clinical information, demonstrates good performance in predicting the presence of HGPs and can be employed to assist in the development of personalized treatment plans for patients with invasive LUAD.
Authors
- Shengdong Nie (ORCID: https://orcid.org/0000-0001-7825-4455)
- Yali Tao
- Wenhui Wu (ORCID: https://orcid.org/0000-0002-0416-7719)
- Xiaodan Ye (ORCID: https://orcid.org/0000-0003-3059-2740)
- Jian Li (ORCID: https://orcid.org/0000-0001-9786-1592)
- Xiujuan Li (ORCID: https://orcid.org/0000-0002-9872-6357)
- Rong Sun (ORCID: https://orcid.org/0009-0008-6345-5228)
- Yuanzhong Xie (ORCID: https://orcid.org/0000-0003-2593-4806)
Institutions
- University of Shanghai for Science and Technology (CN)
- Sun Yat-sen University (CN)
- Fudan University (CN)
- Zhongshan Hospital (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Taian City Central Hospital (CN)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-04-01
- DOI
- https://doi.org/10.1002/mp.17781
- Citations
- 1
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 1.36
Funders
- Natural Science Foundation of Shanghai
- National Natural Science Foundation of China
- Shanghai Key Laboratory of Molecular Imaging