A foundation model-enhanced CT radiomics signature for the noninvasive assessment of tertiary lymphoid structures and prediction of therapy benefit in gastric cancer
Gastric cancer is a highly heterogeneous tumor type whose treatment responses substantially vary across patients. In recent years, biomarkers have shown promise in guiding individualized treatment regimens for gastric cancer, particularly for targeted therapy and immunotherapy[1][1]. Existing
Authors
- Didi Wen (ORCID: https://orcid.org/0000-0002-5176-0777)
- Kun Feng (ORCID: https://orcid.org/0000-0003-2897-8775)
- Linbin Lu (ORCID: https://orcid.org/0000-0003-3779-3457)
- Yongxi Song (ORCID: https://orcid.org/0000-0002-5736-9453)
- LW Chen
- Jiangpeng Wei (ORCID: https://orcid.org/0000-0001-6619-8695)
- Shibo Wang (ORCID: https://orcid.org/0009-0009-2129-9711)
- Guang-Tao Wu
- Zengshan Li (ORCID: https://orcid.org/0000-0002-3368-1434)
- Zhongyang Zhang (ORCID: https://orcid.org/0000-0002-2521-3884)
- J Yu
- Xianchun Gao
- Qiong Xiao
- Ning Han
- Yongzhan Nie (ORCID: https://orcid.org/0000-0003-1124-4548)
- Zhe Li
- Minwen Zheng
- Yong Xia
- Dai Zhang
Institutions
- Northwestern Polytechnical University (CN)
- National Clinical Research Center for Digestive Diseases (CN)
- First Hospital of China Medical University (CN)
- Xijing Hospital (CN)
- China Medical University (CN)
- Air Force Medical University (CN)
Publication Details
- Journal
- Cancer Biology and Medicine
- Published
- 2026-04-16
- DOI
- https://doi.org/10.20892/j.issn.2095-3941.2026.0076
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00