Deep learning–derived CT body composition enhances survival risk stratification beyond the TNM system in locally advanced gastric cancer: a multi-modality cohort study
Deep learning-derived CTBC metrics, especially VAT/SAT ratio, enhance prognostic stratification beyond TNM staging in locally advanced gastric cancer. This ratio captures a systemic and tumor-level immunometabolic phenotype marked by mitochondrial dysfunction and immune suppression. Our findings highlight VAT/SAT as a noninvasive, clinically actionable biomarker to guide personalized therapy and risk-adapted algorithm in gastric cancer management.
Authors
- Ta-Sen Yeh
- Tzong-Shyuan Tai (ORCID: https://orcid.org/0000-0003-0575-914X)
- Lin Gigin
- Yu-Ching Lin
- Shiu‐Feng Huang
- Tsung-Hsing Chen
- Chun-Yi Tsai
- Ying-Chieh Lai
- Cheng-Yu Ma
- Jun-Te Hsu
Institutions
- Chang Gung University (TW)
- Linkou Chang Gung Memorial Hospital (TW)
Publication Details
- Journal
- International Journal of Surgery
- Published
- 2026-01-22
- DOI
- https://doi.org/10.1097/js9.0000000000004835
- Primary Topic
- Ferroptosis and cancer prognosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00