Multimodal Transformer Integrating CT and Pathology for Postoperative Risk Stratification in Stage I Non–Small Cell Lung Cancer
A biologically interpretable multimodal artificial intelligence framework outperformed guideline-based postoperative risk stratification for stage I non–small cell lung cancer and identified patients who would benefit from adjuvant therapy.
Authors
- Zhiyuan Sun (ORCID: https://orcid.org/0000-0003-1779-9261)
- Long Jiang Zhang (ORCID: https://orcid.org/0000-0002-6664-7224)
- Guangming Lu (ORCID: https://orcid.org/0000-0003-4913-2314)
- Yang Cao (ORCID: https://orcid.org/0000-0003-1534-9152)
- Jingjiang Yao
- 张爱凤
- Z.‐G. Zhou (ORCID: https://orcid.org/0009-0004-0467-6910)
- Yu Zong (ORCID: https://orcid.org/0009-0004-8418-8975)
- Jianrui Li (ORCID: https://orcid.org/0000-0003-4548-8102)
- Liying Wang
- Xinyu Li (ORCID: https://orcid.org/0009-0001-6178-3390)
- Xiaoqing Cheng
- Wenhui Lv (ORCID: https://orcid.org/0000-0002-9409-6167)
- Yifeng Luo
- Hao Tang (ORCID: https://orcid.org/0009-0001-1494-3769)
Institutions
- Jiangsu University (CN)
- Central South University (CN)
- Changsha Medical University (CN)
- Nanjing General Hospital of Nanjing Military Command (CN)
- Zhongda Hospital Southeast University (CN)
- Second Affiliated Hospital of Nanjing Medical University (CN)
- Yixing People's Hospital (CN)
- Chenzhou First People's Hospital (CN)
- Southeast University (CN)
- Nanjing Medical University (CN)
- Nanjing University (CN)
- University of South China (CN)
Publication Details
- Journal
- Radiology Imaging Cancer
- Published
- 2026-10-16
- DOI
- https://doi.org/10.1148/rycan.250617
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00