Multimodal deep learning radiomics model for predicting postoperative progression in solid stage I non-small cell lung cancer
MDLR model is effective in predicting the risk of postoperative progression of solid stage I NSCLC, and it is helpful for the treatment and follow-up of solid stage I NSCLC patients.
Authors
- Yehang Chen (ORCID: https://orcid.org/0009-0002-3724-3957)
- Xiaobei Duan
- Kuncai Xu
- Xiaoyan Lei (ORCID: https://orcid.org/0000-0002-7601-9053)
- Wansheng Long (ORCID: https://orcid.org/0000-0002-1331-1858)
- Xiangmeng Chen
- Qionglian Kuang
- Xiaojuan Chen (ORCID: https://orcid.org/0000-0001-7330-0993)
- Bao Feng (ORCID: https://orcid.org/0000-0001-6389-8055)
- Kunwei Li (ORCID: https://orcid.org/0000-0001-5118-8013)
Institutions
- Sun Yat-sen University (CN)
- Guilin University of Aerospace Technology (CN)
- Fifth Affiliated Hospital of Sun Yat-sen University (CN)
- Hainan General Hospital (CN)
- Jiangmen Central Hospital (CN)
Publication Details
- Journal
- Cancer Imaging
- Published
- 2024-10-17
- DOI
- https://doi.org/10.1186/s40644-024-00783-8
- Citations
- 11
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.45
Funders
- Natural Science Foundation of Guangxi Zhuang Autonomous Region
- National Natural Science Foundation of China
- Guangdong Medical Research Foundation
- Natural Science Foundation of Guangxi Province