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Artif Intell Gastroenterol. Dec 28, 2022; 3(5): 142-162
Published online Dec 28, 2022. doi: 10.35712/aig.v3.i5.142
Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology
Aysen Yavuz, Anil Alpsoy, Elif Ocak Gedik, Mennan Yigitcan Celik, Cumhur Ibrahim Bassorgun, Betul Unal, Gulsum Ozlem Elpek
Aysen Yavuz, Anil Alpsoy, Elif Ocak Gedik, Mennan Yigitcan Celik, Cumhur Ibrahim Bassorgun, Betul Unal, Gulsum Ozlem Elpek, Department of Pathology, Akdeniz University Medical School, Antalya 07070, Turkey
Author contributions: Yavuz A, Alpsoy A, Gedik EO, Celik MY performed the data acquisition; Bassorgun CI, Unal B and Elpek GO designed the outline and coordinated the writing of the paper; all authors equally contributed to the writing of the paper and preparation of the tables.
Conflict-of-interest statement: All the authors declare that they have no conflict of interest.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See:
Corresponding author: Gulsum Ozlem Elpek, MD, Professor, Department of Pathology, Akdeniz University Medical School, Dumlupinar bulvarı, Antalya 07070, Turkey.
Received: October 16, 2022
Peer-review started: October 16, 2022
First decision: November 15, 2022
Revised: November 25, 2022
Accepted: December 13, 2022
Article in press: December 13, 2022
Published online: December 28, 2022

Recent research has provided a wealth of data supporting the application of artificial intelligence (AI)-based applications in routine pathology practice. Indeed, it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors, increasing reliability, and improving workflow. In addition, the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior, course, and treatment in many types of tumors has also been noted. Regarding gastrointestinal system (GIS) cancers, the contribution of AI methods to pathological diagnosis has been investigated in many studies. On the other hand, studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few. For this purpose, the potential of AI models has been studied over a broad spectrum, from tumor subtyping to the identification of new digital biomarkers. The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated. Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers, a wide range of challenges still need to be solved, from laboratory infrastructure to improving the robustness of algorithms, before incorporating AI applications into real-life GIS pathology practice. This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology.

Keywords: Digital pathology, Colorectal cancer, Gastric cancer, Machine learning, Deep learning, Prognosis

Core Tip: This review outlines the potential of artificial intelligence applications for evaluating pathological parameters related to the behavior of gastrointestinal cancers. The role of these methods in determining the behavior of esophageal cancers remains to be investigated. On the other hand, the results are promising, supporting that these models can assist in the determination of conventional pathological parameters and perform molecular subtyping in gastric and colorectal cancers. Furthermore, these applications encourage digital prognostic biomarker discovery by revealing predictions that are impossible when using traditional visual methods. However, further studies are needed to overcome the obstacles to implementing these applications into pathology practice.