Publication Name	World Journal of Gastroenterology
Manuscript ID	116533
Country	Italy

Category	Gastroenterology & Hepatology
Manuscript Type	Letter to the Editor
Article Title	Splitting the risk: Interpretable machine learning enters the era of split liver transplantation
Manuscript Source	Invited Manuscript
All Author List	Salvatore S Sciarrone, Milena Di Leo, Lucia Fini, Alessandra Losco and Luca De Luca
Funding Agency and Grant Number
Corresponding Author	Luca De Luca, Director, MD, Departement 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
Key Words	Artificial intelligence; Cirrhosis; Surgery; Split liver; Transplantation
Core Tip	In a single-centre 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 haemorrhage, vascular complications, biliary events, ascites and infections. They evaluated four modelling 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.
Citation	Sciarrone SS, Di Leo M, Fini L, Losco A, De Luca L. Splitting the risk: Interpretable machine learning enters the era of split liver transplantation. World J Gastroenterol 2026; In press

Received	2025-11-17 03:51
Peer-Review Started	2025-11-17 03:52
First Decision by Editorial Office Director	2025-12-30 07:36
Return for Revision	2025-12-30 07:36
Revised	2026-01-04 12:12
Publication Fee Transferred
Second Decision by Editor	2026-02-03 02:43
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-02-03 05:47
Articles in Press	2026-02-03 05:47
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1007-9327 (print) and 2219-2840 (online)
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