Morales-Galicia AE, Rincón-Sánchez MN, Ramírez-Mejía MM, Méndez-Sánchez N. Outcome prediction for cholangiocarcinoma prognosis: Embracing the machine learning era. World J Gastroenterol 2025; 31(21): 106808 [DOI: 10.3748/wjg.v31.i21.106808]
Corresponding Author of This Article
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
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
Letter to the Editor
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. 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
Abstract
We read with great interest the study by Huang et al. Cholangiocarcinoma (CC) is the second most common type of primary liver tumor worldwide. Although surgical resection remains the primary treatment for this disease, almost 50% of patients experience relapse within 2 years after surgery, which negatively affects their prognosis. Key predictors can be used to identify several factors (e.g., tumor size, tumor location, tumor stage, nerve invasion, the presence of intravascular emboli) and their correlations with long-term survival and the risk of postoperative morbidity. In recent years, artificial intelligence (AI) has become a new tool for prognostic assessment through the integration of multiple clinical, surgical, and imaging parameters. However, a crucial question has arisen: Are we ready to trust AI with respect to clinical decisions? The study by Huang et al demonstrated that AI can predict preoperative textbook outcomes in patients with CC and highlighted the precision of machine learning algorithms using useful prognostic factors. This letter to the editor aimed to explore the challenges and potential impact of AI and machine learning in the prognostic assessment of patients with CC.
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.
