医学
危险分层
内科学
队列
肿瘤科
生物标志物
队列研究
分层(种子)
癌症
预测模型
免疫系统
总体生存率
成像生物标志物
生存分析
阶段(地层学)
登台系统
回顾性队列研究
比例危险模型
放射科
前瞻性队列研究
风险评估
胃肠病学
作者
Ying-Chieh Lai,Yu-Ching Lin,Tzong-Shyuan Tai,Lin Gigin,Cheng-Yu Ma,Shiu‐Feng Huang,Tsung-Hsing Chen,Chun-Yi Tsai,Jun-Te Hsu,Ta-Sen Yeh
标识
DOI:10.1097/js9.0000000000004835
摘要
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.
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