Predicting spread through air space of lung adenocarcinoma based on deep learning and machine learning models
This presented radiomics model can be served as a non-invasive for predicting STAS in Infiltrating lung adenocarcinoma.
Authors
- Lingxin Kong (ORCID: https://orcid.org/0000-0001-5963-6933)
- Wenfei Xue (ORCID: https://orcid.org/0000-0002-2957-2059)
- Zengming Wang (ORCID: https://orcid.org/0000-0001-6695-4403)
- Qingtao Zhao (ORCID: https://orcid.org/0000-0001-6400-2329)
- Wei Shan Li (ORCID: https://orcid.org/0000-0001-7458-2590)
- Xiaopeng Zhang (ORCID: https://orcid.org/0000-0002-0092-6474)
- Shun Xu (ORCID: https://orcid.org/0000-0001-8322-121X)
- Guochen Duan
- Huanfen Zhao (ORCID: https://orcid.org/0009-0007-2657-2487)
- Bin Li (ORCID: https://orcid.org/0000-0001-7838-4399)
Institutions
- Hebei General Hospital (CN)
- First Hospital of China Medical University (CN)
- China Medical University (CN)
Publication Details
- Journal
- Journal of Cardiothoracic Surgery
- Published
- 2025-08-14
- DOI
- https://doi.org/10.1186/s13019-025-03568-7
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Hebei Provincial Key Research Projects