The seen and the unseen: Molecular classification and image based-analysis of gastrointestinal cancers.
Artificial intelligence
Gastric adenocarcinoma
Image-based classification
Molecular classification
Journal
Computational and structural biotechnology journal
ISSN: 2001-0370
Titre abrégé: Comput Struct Biotechnol J
Pays: Netherlands
ID NLM: 101585369
Informations de publication
Date de publication:
2022
2022
Historique:
received:
05
07
2022
revised:
07
09
2022
accepted:
07
09
2022
entrez:
3
10
2022
pubmed:
4
10
2022
medline:
4
10
2022
Statut:
epublish
Résumé
Gastrointestinal cancers account for 22.5% of cancer related deaths worldwide and represent circa 20% of all cancers. In the last decades, we have witnessed a shift from histology-based to molecular-based classifications using genomic, epigenomic, and transcriptomic data. The molecular based classification revealed new prognostic markers and may aid the therapy selection. Because of the high-costs to perform a molecular classification, in recent years immunohistochemistry-based surrogate classification were developed which permit the stratification of patients, and in parallel multiple groups developed hematoxylin and eosin whole slide image analysis for sub-classifying these entities. Hence, we are witnessing a return to an image-based classification with the purpose to infer hidden information from routine histology images that would permit to detect the patients that respond to specific therapies and would be able to predict their outcome. In this review paper, we will discuss the current histological, molecular, and immunohistochemical classifications of the most common gastrointestinal cancers, gastric adenocarcinoma, and colorectal adenocarcinoma, and will present key aspects for developing a new artificial intelligence aided image-based classification of these malignancies.
Identifiants
pubmed: 36187924
doi: 10.1016/j.csbj.2022.09.010
pii: S2001-0370(22)00415-9
pmc: PMC9489806
doi:
Types de publication
Journal Article
Review
Langues
eng
Pagination
5065-5075Informations de copyright
© 2022 Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology.
Déclaration de conflit d'intérêts
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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