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Official websites use. Share sensitive information only on official, secure websites. Gastrointestinal cancers account for 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.
Keywords: Artificial intelligence, Molecular classification, Image-based classification, Gastric adenocarcinoma. Gastrointestinal cancers GIC account for In order to keep this review concise, we will focus on two main GIC subtypes, gastric adenocarcinoma GAC and colorectal adenocarcinoma CRC , for which the most interesting molecular classification terms outlined in bold are further presented in the Glossary section and the first steps for image based-analysis were recently developed.
Specifically, GAC is the fourth most common cancer type [2] and the number three cause of cancer related death worldwide [3] being an important public health issue. Prior to chemotherapy, the survival was of only 5 months [5] , [6].