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©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
Impact of artificial intelligence in the management of esophageal, gastric and colorectal malignancies
Ayrton Bangolo, Nikita Wadhwani, Vignesh K Nagesh, Shraboni Dey, Hadrian Hoang-Vu Tran, Izage Kianifar Aguilar, Auda Auda, Aman Sidiqui, Aiswarya Menon, Deborah Daoud, James Liu, Sai Priyanka Pulipaka, Blessy George, Flor Furman, Nareeman Khan, Adewale Plumptre, Imranjot Sekhon, Abraham Lo, Simcha Weissman
Ayrton Bangolo, Nikita Wadhwani, Vignesh K Nagesh, Shraboni Dey, Hadrian Hoang-Vu Tran, Izage Kianifar Aguilar, Aman Sidiqui, Aiswarya Menon, James Liu, Blessy George, Flor Furman, Nareeman Khan, Adewale Plumptre, Imranjot Sekhon, Simcha Weissman, Department of Internal Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
Auda Auda, Deborah Daoud, Sai Priyanka Pulipaka, Abraham Lo, Department of Medicine, Palisades Medical Center, North Bergen, NJ 07047, United States
Author contributions: Bangolo A searched the literature, wrote, and revised the manuscript; Wadhwani N, Nagesh VK, Dey S, Tran H, Aguilar IK, Auda A, Sidiqui A, Menon A, Daoud D, Liu J, Pulipaka P, George B, Furman F, Khan N, Plumptre A, and Sekhon I wrote, revised and edited the manuscript; Weissman S and Lo A wrote, revised and approved the final version and are the article’s guarantors; All authors certify that they contributed sufficiently to the intellectual content and data analysis; Each author has reviewed the final version of the manuscript and approves it for publication.
Conflict-of-interest statement: No potential conflict of interest was reported by the authors.
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:
https://creativecommons.org/Licenses/by-nc/4.0/ Corresponding author: Ayrton Bangolo, MBBS, MD, Doctor, Department of Internal Medicine, Palisades Medical Center, 7600 River Road, North Bergen, NJ 07047, United States. ayrtonbangolo0@gmail.com
Received: December 12, 2023
Revised: January 28, 2024
Accepted: March 4, 2024
Published online: June 8, 2024
The incidence of gastrointestinal malignancies has increased over the past decade at an alarming rate. Colorectal and gastric cancers are the third and fifth most commonly diagnosed cancers worldwide but are cited as the second and third leading causes of mortality. Early institution of appropriate therapy from timely diagnosis can optimize patient outcomes. Artificial intelligence (AI)-assisted diagnostic, prognostic, and therapeutic tools can assist in expeditious diagnosis, treatment planning/response prediction, and post-surgical prognostication. AI can intercept neoplastic lesions in their primordial stages, accurately flag suspicious and/or inconspicuous lesions with greater accuracy on radiologic, histopathological, and/or endoscopic analyses, and eliminate over-dependence on clinicians. AI-based models have shown to be on par, and sometimes even outperformed experienced gastroenterologists and radiologists. Convolutional neural networks (state-of-the-art deep learning models) are powerful computational models, invaluable to the field of precision oncology. These models not only reliably classify images, but also accurately predict response to chemotherapy, tumor recurrence, metastasis, and survival rates post-treatment. In this systematic review, we analyze the available evidence about the diagnostic, prognostic, and therapeutic utility of artificial intelligence in gastrointestinal oncology.
Core Tip: Application of artificial intelligence in the realm of gastrointestinal malignancies has burgeoned over the past decade as its incorporation has streamlined the work-up of gastrointestinal malignancies to address the alarming mortality statistics, largely resulting from delayed interception. The latter juxtaposed with the abundant array of contemporary diagnostic, predictive, and prognostic tools, is a testament to their underperforming status and calls for the development of digital tools that can optimize the oncologic work-up and pave the way for personalized therapies.
