A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles

A machine learning approach to risk-stratification of gastric cancer based on tumour-infiltrating immune cell profiles

Background Gastric cancer (GC) is a highly heterogeneous disease, and the response of patients to clinical treatment varies substantially. There is no satisfactory strategy for predicting curative effects to date. We aimed to explore a new method for predicting the clinical efficacy of GC treatment...

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Bibliographic Details
Main Authors: Yanping Hu, Bo Wang, Chao Shi, Pengfei Ren, Chengjuan Zhang, Zhizhong Wang, Jiuzhou Zhao, Jiawen Zheng, Tingjie Wang, Bing Wei, He Zhang, Rentao Yu, Yihang Shen, Jie Ma, Yongjun Guo
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Annals of Medicine
Subjects:
Gastric cancer
tumour-infiltrating immune cells
flow cytometry
risk stratification
disease-free survival
Online Access:https://www.tandfonline.com/doi/10.1080/07853890.2025.2489007
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https://www.tandfonline.com/doi/10.1080/07853890.2025.2489007

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