Illuminating the black box: Machine learning enhances preoperative prediction in intrahepatic cholangiocarcinoma

doi:10.3748/wjg.v31.i17.106592

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May 7, 2025 (publication date) through Mar 2, 2026

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World Journal of Gastroenterology

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1007-9327

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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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.

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 21.7 Hours

Core Tip

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.