| ISSN |
1948-5204 (online) |
| 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/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Gastroenterology & Hepatology |
| Manuscript Type |
Minireviews |
| Article Title |
Deep learning in lower gastrointestinal cancer detection: Advances in endoscopic, radiologic, and histopathologic diagnostics
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Tanisha Sehgal, Tanvi Joshi, Rishi Chowdhary, Omesh Goyal, Shivam Kalra, Rohit Goyal, Varna Taranikanti, Ashita Rukmini Vuthaluru and Manjeet Kumar Goyal |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Manjeet Kumar Goyal, Academic Fellow, Consultant, Department of Internal Medicine, Cleveland Clinic Akron General Hospital, 1, Akron General Avenue, Akron, OH 44308, United States. manjeetgoyal@gmail.com |
| Key Words |
Artificial intelligence; Gastrointestinal cancer; Endoscopy; Radiology; Histopathology; Computer-aided diagnosis |
| Core Tip |
Artificial intelligence (AI), particularly deep learning, is revolutionizing gastrointestinal oncology by enhancing early detection, diagnostic precision, prognostication, and personalized treatment. Deep learning models such as convolutional neural networks improve polyp detection, automate tumor segmentation, and interpret histopathology with high accuracy. Emerging multimodal and explainable AI frameworks integrate imaging, molecular, and clinical data, fostering precision oncology. Despite challenges of data heterogeneity and generalizability, the synergy between AI and clinicians promises earlier diagnosis, individualized therapy, and improved outcomes in lower gastrointestinal cancers. |
| Publish Date |
2026-02-03 06:28 |
| Citation |
Sehgal T, Joshi T, Chowdhary R, Goyal O, Kalra S, Goyal R, Taranikanti V, Vuthaluru AR, Goyal MK. Deep learning in lower gastrointestinal cancer detection: Advances in endoscopic, radiologic, and histopathologic diagnostics. World J Gastrointest Oncol 2026; 18(2): 115974 |
| URL |
https://www.wjgnet.com/1948-5204/full/v18/i2/115974.htm |
| DOI |
https://dx.doi.org/10.4251/wjgo.v18.i2.115974 |
