Universität zu Köln

Klein, Sebastian ORCID: 0000-0002-2188-9377 and Duda, Dan G. ORCID: 0000-0001-7065-8797 (2021). Machine Learning for Future Subtyping of the Tumor Microenvironment of Gastro-Esophageal Adenocarcinomas. Cancers, 13 (19). BASEL: MDPI. ISSN 2072-6694

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Abstract

Simple Summary We summarize the main components of the tumor microenvironment in gastro-esophageal adenocarcinomas (GEA). In addition, we highlight past and present applications of machine learning in GEA to propose ways to facilitate its clinical use in the future. 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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Klein, Sebastian UNSPECIFIED orcid.org/0000-0002-2188-9377 UNSPECIFIED Duda, Dan G. UNSPECIFIED orcid.org/0000-0001-7065-8797 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-586251
DOI:	10.3390/cancers13194919
Journal or Publication Title:	Cancers
Volume:	13
Number:	19
Date:	2021
Publisher:	MDPI
Place of Publication:	BASEL
ISSN:	2072-6694
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language GASTRIC-CANCER; DOUBLE-BLIND; ARTIFICIAL-INTELLIGENCE; SEX-DIFFERENCES; NEOADJUVANT CHEMORADIOTHERAPY; PATHOLOGICAL RESPONSE; BARRETTS-ESOPHAGUS; MUTATIONAL BURDEN; GROWTH-FACTOR; EXPRESSION Multiple languages Oncology Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/58625