Chan SY, Twohig P. Artificial intelligence in liver cancer surgery: Predicting success before the first incision. World J Gastroenterol 2025; 31(16): 107221 [DOI: 10.3748/wjg.v31.i16.107221]
Corresponding Author of This Article
Patrick Twohig, FRCPC, Assistant Professor, Department of Gastroenterology & Hepatology, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14682, United States. patrick_twohig@urmc.rochester.edu
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Editorial
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (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: http://creativecommons.org/licenses/by-nc/4.0/
World J Gastroenterol. Apr 28, 2025; 31(16): 107221 Published online Apr 28, 2025. doi: 10.3748/wjg.v31.i16.107221
Artificial intelligence in liver cancer surgery: Predicting success before the first incision
Shu-Yen Chan, Patrick Twohig
Shu-Yen Chan, Department of Internal Medicine, Weiss Memorial Hospital, Chicago, IL 60640, United States
Patrick Twohig, Department of Gastroenterology & Hepatology, University of Rochester Medical Center, Rochester, NY 14682, United States
Author contributions: Twohig P designed the overall concept and outline of the manuscript; Chan SY contributed to the discussion and design of the manuscript; Chan SY and Twohig P both contributed to the writing, editing of the manuscript, and review of the literature.
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: Patrick Twohig, FRCPC, Assistant Professor, Department of Gastroenterology & Hepatology, University of Rochester Medical Center, 601 Elmwood Avenue, Rochester, NY 14682, United States. patrick_twohig@urmc.rochester.edu
Received: March 18, 2025 Revised: March 30, 2025 Accepted: April 17, 2025 Published online: April 28, 2025 Processing time: 40 Days and 1.9 Hours
Abstract
Advancements in machine learning have revolutionized preoperative risk assessment. In this article, we comment on the article by Huang et al, which presents a recent multicenter cohort study demonstrated that machine learning algorithms effectively stratify recurrence-free survival, providing a robust predictive framework for maximizing surgical outcomes in intrahepatic cholangiocarcinoma. By leveraging interpretable models, the research enhances clinical decision-making, allowing for more precise patient selection and personalized surgical strategies. These findings highlight the growing role of artificial intelligence in optimizing surgical outcomes and improving prognostic accuracy in hepatobiliary oncology.
Core Tip: Machine learning is revolutionizing surgical planning for intrahepatic cholangiocarcinoma by enabling preoperative prediction of “textbook outcomes” through interpretable artificial intelligence models. This approach enhances precision in patient selection, optimizes surgical strategies, and reduces unnecessary procedures, paving the way for more personalized hepatobiliary oncology care.
