Letter to the Editor
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Jun 7, 2025; 31(21): 106808
Published online Jun 7, 2025. doi: 10.3748/wjg.v31.i21.106808
Outcome prediction for cholangiocarcinoma prognosis: Embracing the machine learning era
Arnulfo E Morales-Galicia, Mariana N Rincón-Sánchez, Mariana M Ramírez-Mejía, Nahum Méndez-Sánchez
Arnulfo E Morales-Galicia, Nahum Méndez-Sánchez, Faculty of Medicine, National Autonomous University of Mexico, Mexico City 04360, Mexico
Mariana N Rincón-Sánchez, Faculty of Medicine “Dr. Jose Sierra Flores,” Northeastern University, Tampico 89337, Tamaulipas, Mexico
Mariana M Ramírez-Mejía, Plan of Combined Studies in Medicine, Faculty of Medicine, National Autonomous University of Mexico, Mexico City 04360, Mexico
Mariana M Ramírez-Mejía, Nahum Méndez-Sánchez, Liver Research Unit, Medica Sur Clinic and Foundation, Mexico City 14050, Mexico
Author contributions: Méndez-Sánchez N, Morales-Galicia AE, Rincon-Sanchez MN, and Ramírez-Mejía MM contributed to this paper; Méndez-Sánchez N designed the overall concept and outline of the manuscript; Morales-Galicia AE and Ramírez-Mejía MM contributed to the discussion and design of the manuscript; Méndez-Sánchez N, Morales-Galicia AE, Rincon-Sanchez MN, and Ramírez-Mejía MM contributed to the writing and editing of the manuscript, illustrations, and literature review.
Conflict-of-interest statement: All authors declare that they have no competing interests.
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: Nahum Méndez-Sánchez, Liver Research Unit, Medica Sur Clinic and Foundation, Puente de Piedra 150, Col. Toriello Guerra, Mexico City 14050, Mexico and Faculty of Medicine, National Autonomous University of Mexico, Mexico City 04360, Mexico. nmendez@medicasur.org.mx
Received: March 9, 2025
Revised: April 15, 2025
Accepted: May 12, 2025
Published online: June 7, 2025
Processing time: 91 Days and 2.1 Hours
Core Tip

Core Tip: Machine learning-driven preoperative risk stratification enhances surgical planning in intrahepatic cholangiocarcinoma. Huang et al demonstrated that the concept of the textbook outcome can be predicted preoperatively using artificial intelligence models, which outperform traditional prognostic methods. Their study underscored the importance of dynamic, data-driven approaches for improving disease-free survival and optimizing patient selection for curative resection.