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©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Feb 15, 2026; 18(2): 115974
Published online Feb 15, 2026. doi: 10.4251/wjgo.v18.i2.115974
Published online Feb 15, 2026. doi: 10.4251/wjgo.v18.i2.115974
Deep learning in lower gastrointestinal cancer detection: Advances in endoscopic, radiologic, and histopathologic diagnostics
Tanisha Sehgal, Department of Internal Medicine, Dayanand Medical College and Hospital, Ludhiana 141001, Punjab, India
Tanvi Joshi, Department of Internal Medicine, Shrimati Kashibai Navale Medical College and General Hospital, Pune 411041, Mahārāshtra, India
Rishi Chowdhary, Department of Medicine, MetroHealth Medical Center, Cleveland, OH 44109, United States
Omesh Goyal, Department of Gastroenterology, Dayanand Medical College and Hospital, Tagore Nagar, Ludhiana 141001, Punjab, India
Shivam Kalra, Department of Internal Medicine, Trident Medical Center, Charleston, SC 29405, United States
Rohit Goyal, Department of Internal Medicine, Louisiana State University Health Shreveport, Shreveport, LA 71103, United States
Varna Taranikanti, Department of Foundational Medical Studies, Oakland University William Beaumont School of Medicine Rochester, Rochester, MI 48309, United States
Ashita Rukmini Vuthaluru, Department of Anesthesiology, All India Institute of Medical Sciences, New Delhi 110029, Delhi, India
Manjeet Kumar Goyal, Department of Internal Medicine, Cleveland Clinic Akron General Hospital, Akron, OH 44308, United States
Co-first authors: Tanisha Sehgal and Tanvi Joshi.
Co-corresponding authors: Omesh Goyal and Manjeet Kumar Goyal.
Author contributions: Chowdhary R, Goyal O, Goyal R and Goyal MK contributed to the conceptualization and design of the study.; Chowdhary R, Goyal MK, and Kalra S developed the methodology; Sehgal T, Joshi T, Taranikanti V, and Vuthaluru AR conducted the investigation, including data curation and acquisition of all relevant study measurements; Chowdhary R, Kalra S, and Goyal MK performed the data visualization and assisted in data interpretation. Chowdhary R, Goyal MK, Joshi T, and Sehgal T prepared the initial draft of the manuscript. All authors contributed to the reviewing and editing of subsequent manuscript versions and approved the final version of the manuscript for submission; Goyal O provided supervision and validation of the study; All authors read and approved the final version of the manuscript. Sehgal T and Joshi T collaborated in equal efforts that were critical to the completion of the study and generation of the manuscript and as such share first authorship.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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: Manjeet Kumar Goyal, DM, DNB, MD, Department of Internal Medicine, Cleveland Clinic Akron General Hospital, 1 Akron General Avenue, Akron, OH 44308, United States. manjeetgoyal@gmail.com
Received: October 30, 2025
Revised: November 15, 2025
Accepted: December 8, 2025
Published online: February 15, 2026
Processing time: 96 Days and 9.9 Hours
Revised: November 15, 2025
Accepted: December 8, 2025
Published online: February 15, 2026
Processing time: 96 Days and 9.9 Hours
Core Tip
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.