Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.
World J Gastroenterol. Jun 14, 2026; 32(22): 116533
Published online Jun 14, 2026. doi: 10.3748/wjg.v32.i22.116533
Published online Jun 14, 2026. doi: 10.3748/wjg.v32.i22.116533
Letter to the Editor: Splitting the risk: Interpretable machine learning enters the era of split liver transplantation
Salvatore S Sciarrone, Milena Di Leo, Lucia Fini, Alessandra Losco, Luca De Luca, Department of Surgery, Gastroenterology and Digestive Endoscopy Unit, ASST Santi Paolo e Carlo, University of Milan, Milan 20142, Lombardy, Italy
Author contributions: Sciarrone SS, Di Leo M, Fini L, Losco A, De Luca L contributed to writing the manuscript.
AI contribution statement: AI with chat GPT was only used for a more accurate bibliography and to check the DOI and rights comments for some corrections for some sentences. None AI tools were used in the article writing, making completely letter to editor and making corrections of English.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Corresponding author: Luca De Luca, MD, Director, Department of Surgery, Gastroenterology and Digestive Endoscopy Unit, ASST Santi Paolo e Carlo, University of Milan, Via Antonio Rudini’ 2, Milan 20142, Lombardy, Italy. luca.deluca@asst-santipaolocarlo.it
Received: November 17, 2025
Revised: January 4, 2026
Accepted: February 3, 2026
Published online: June 14, 2026
Processing time: 196 Days and 20.7 Hours
Revised: January 4, 2026
Accepted: February 3, 2026
Published online: June 14, 2026
Processing time: 196 Days and 20.7 Hours
Core Tip
Core Tip: In a single-center retrospective cohort of 109 adult recipients of right tri-segment split liver transplantation, Wang et al report an early postoperative complications (EPC) rate of 33.9%, including hemorrhage, vascular complications, biliary events, ascites and infections. They evaluated four modeling strategies, random forest, support vector machine, extreme gradient boosting and logistic regression, identifying random forest as the best-performing algorithm. Critically, they did not stop at prediction performance. Using SHapley Additive exPlanations, they quantified the contribution of each variable to the model and highlighted six major drivers of EPC risk.