Letter to the Editor
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. May 7, 2025; 31(17): 106592
Published online May 7, 2025. doi: 10.3748/wjg.v31.i17.106592
Illuminating the black box: Machine learning enhances preoperative prediction in intrahepatic cholangiocarcinoma
Eyad Gadour, Mohammed S AlQahtani
Eyad Gadour, Mohammed S AlQahtani, Multiorgan Transplant Centre of Excellence, Liver Transplantation Unit, King Fahad Specialist Hospital, Dammam 32253, Saudi Arabia
Eyad Gadour, Internal Medicine, Faculty of Medicine, Zamzam University College, Khartoum North 11113, Khartoum, Sudan
Mohammed S AlQahtani, Department of Surgery, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
Author contributions: Gadour E and AlQahtani MS contributed equally. Both authors have read and approved the final manuscript.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
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: Eyad Gadour, MD, Associate Professor, Multiorgan Transplant Centre of Excellence, Liver Transplantation Unit, King Fahad Specialist Hospital, Ammar Bin Thabit Street, Dammam 32253, Saudi Arabia. eyadgadour@doctors.org.uk
Received: March 3, 2025
Revised: March 13, 2025
Accepted: March 19, 2025
Published online: May 7, 2025
Processing time: 58 Days and 21.6 Hours
Abstract

The study by Huang et al, published in the World Journal of Gastroenterology, advances intrahepatic cholangiocarcinoma (ICC) management by developing a machine-learning model to predict textbook outcomes (TO) based on preoperative factors. By analyzing data from 376 patients across four Chinese medical centers, the researchers identified key variables influencing TO, including Child-Pugh classification, Eastern Cooperative Oncology Group score, hepatitis B status, and tumor size. The model, created using logistic regression and the extreme gradient boosting algorithm, demonstrated high predictive accuracy, with area under the curve values of 0.8825 for internal validation and 0.8346 for external validation. The integration of the Shapley additive explanation technique enhances the interpretability of the model, which is crucial for clinical decision-making. This research highlights the potential of machine learning to improve surgical planning and patient outcomes in ICC, opening possibilities for personalized treatment approaches based on individual patient characteristics and risk factors.

Keywords: Intrahepatic cholangiocarcinoma; Textbook outcome; Machine learning; Predictive model; Shapley additive explanations; Preoperative assessment; Surgical outcomes; Disease-free survival; Extreme gradient boosting; Clinical decision-making

Core Tip: The extreme gradient boosting model, used in conjunction with the Shapley additive explanation algorithm, as described by Huang et al, offers a revolutionary outlook into the future of surgical oncology for patients with intrahepatic cholangiocarcinoma. This model identifies crucial preoperative factors that influence patient outcomes, enhances understanding of disease progression and treatment efficacy, and underscores its utility in clinical decision-making for patient care and surgical interventions. Moreover, its accurate predictive prognostic potential offers insights into successful treatment mechanisms and personalized care strategies.