1268-Deep Learning Biomarkers for Gastric Cancer Prognosis

Researchers have developed a Transformer-based deep learning framework to create an interpretable pathological risk score (TPRS) for gastric cancer patients. This AI model analyzes routine whole-slide images to improve survival predictions beyond the capabilities of the traditional TNM staging system. Validated on both internal and external datasets, the tool acts as an independent prognostic biomarker and effectively identifies stage III patients who would benefit from adjuvant chemotherapy. By integrating transcriptomic data with cellular morphology, the study establishes a biological explanatory chain linking specific gene expressions to physical tumor features. Ultimately, this open-source technology offers a cost-effective, automated solution for precision medicine in clinical oncology settings.References:Ji J, Zhang X, Hua M, et al. An interpretable deep learning biomarker for prognostication and prediction of adjuvant chemotherapy benefit in gastric cancer[J]. npj Precision Oncology, 2026.前往小宇宙评论区与主播互动