肿瘤微环境
间质细胞
重编程
肿瘤科
癌症
计算机科学
肿瘤进展
腺癌
免疫系统
内科学
医学
癌症研究
病理
无线电技术
疾病
作者
Sebastian Klein,Dan G. Duda
出处
期刊:Cancers
[MDPI AG]
日期:2021-09-30
卷期号:13 (19): 4919-4919
被引量:3
标识
DOI:10.3390/cancers13194919
摘要
Tumor progression involves an intricate interplay between malignant cells and their surrounding tumor microenvironment (TME) at specific sites. The TME is dynamic and is composed of stromal, parenchymal, and immune cells, which mediate cancer progression and therapy resistance. Evidence from preclinical and clinical studies revealed that TME targeting and reprogramming can be a promising approach to achieve anti-tumor effects in several cancers, including in GEA. Thus, it is of great interest to use modern technology to understand the relevant components of programming the TME. Here, we discuss the approach of machine learning, which recently gained increasing interest recently because of its ability to measure tumor parameters at the cellular level, reveal global features of relevance, and generate prognostic models. In this review, we discuss the relevant stromal composition of the TME in GEAs and discuss how they could be integrated. We also review the current progress in the application of machine learning in different medical disciplines that are relevant for the management and study of GEA.
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