Agrawal H, Gupta N, Tanwar H. Artificial intelligence in expanding hepatic resection boundaries: Integrating portal flow modulation and regenerative strategies. Artif Intell Gastroenterol 2026; 7(2): 116057 [DOI: 10.35712/aig.v7.i2.116057]
Corresponding Author of This Article
Nikhil Gupta, Department of Surgery, Atal Bihari Vajpayee Institute of Medical Sciences and Dr. Ram Manohar Lohia Hospital, BKS Marg, Delhi 110001, India. nikhil_ms26@yahoo.co.in
Research Domain of This Article
Gastroenterology & Hepatology
Article-Type of This Article
review-article
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/
Artif Intell Gastroenterol. Aug 8, 2026; 7(2): 116057 Published online Aug 8, 2026. doi: 10.35712/aig.v7.i2.116057
Artificial intelligence in expanding hepatic resection boundaries: Integrating portal flow modulation and regenerative strategies
Himanshu Agrawal, Nikhil Gupta, Himanshu Tanwar
Himanshu Agrawal, Himanshu Tanwar, Department of Surgery, University College of Medical Sciences (University of Delhi), GTB Hospital, Delhi 110095, India
Nikhil Gupta, Department of Surgery, Atal Bihari Vajpayee Institute of Medical Sciences and Dr. Ram Manohar Lohia Hospital, Delhi 110001, India
Co-first authors: Himanshu Agrawal and Nikhil Gupta.
Author contributions: Agrawal H contributed to the conceptualization of the study, data collection, and analysis, assisted in writing the initial draft and provided critical revisions to improve the manuscript; Gupta N led the study design and methodology. Oversaw data analysis and interpretation. Coordinated the manuscript preparation and finalized revisions for submission. Corresponded with the journal and handled all communications; Tanwar H participated in data collection and analysis, contributed to literature review, and assisted in drafting and revising sections of the manuscript. Agrawal H and Gupta N contributed equally to this work as co-first authors.
AI contribution statement: AI tools (ChatGPT) were used solely for linguistic refinement and formatting assistance. No AI tool was involved in the generation of research data, interpretation of results, or formulation of conclusions. All AI-generated outputs were critically reviewed and revised by the authors.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Corresponding author: Nikhil Gupta, Department of Surgery, Atal Bihari Vajpayee Institute of Medical Sciences and Dr. Ram Manohar Lohia Hospital, BKS Marg, Delhi 110001, India. nikhil_ms26@yahoo.co.in
Received: November 2, 2025 Revised: November 24, 2025 Accepted: January 13, 2026 Published online: August 8, 2026 Processing time: 278 Days and 15.3 Hours
Abstract
Post-hepatectomy liver failure (PHLF) remains the principal barrier to major hepatectomy despite its curative potential for hepatocellular carcinoma and colorectal liver metastases; accordingly, this narrative review (aligned with PRISMA guidance) synthesizes evidence from PubMed, Scopus, Web of Science, and Google Scholar (January 2010 to October 2025) on how artificial intelligence (AI)-spanning automated liver and vascular segmentation, volumetry, radiomics, risk prediction, and virtual planning-can be integrated with portal flow modulation and regenerative strategies [portal vein embolization (PVE), liver venous deprivation (LVD), and associating liver partition and portal vein ligation for staged hepatectomy (ALPPS)] to safely expand resection boundaries. Across English-language studies meeting predefined inclusion criteria, AI demonstrated high segmentation accuracy (dice > 0.95) and robust PHLF prediction (area under the curve ≈ 0.82-0.94), while patient-specific 3D/VR models altered operative plans in approximately 44% of major hepatectomies to improve future liver remnant (FLR) preservation. Stepwise hypertrophy outcomes favored PVE (approximately 37%-40% in 3-6 weeks) and LVD (approximately 50%-70% in 2-4 weeks), with ALPPS achieving rapid hypertrophy (approximately 60%-80% in 7-10 days) at the cost of higher morbidity and mortality, supporting selective use. Importantly, radiomics and magnetic resonance imaging-derived features refined functional FLR assessment beyond volume alone and enhanced individualized risk stratification. Overall, pairing AI-enabled planning and outcome prediction with targeted hypertrophy strategies can broaden indications for curative liver resection while mitigating PHLF risk, though prospective validation, bias mitigation, workflow integration, and cost-effectiveness analyses are required before routine adoption.
Core Tip: Advanced artificial intelligence tools-automated segmentation, radiomics, and explainable risk models-optimize future liver remnant assessment and surgical planning. When combined with portal flow modulation (portal vein embolization/liver venous deprivation) and judicious associating liver partition and portal vein ligation for staged hepatectomy use, they offer a pragmatic pathway to expand resection candidacy without compromising safety, provided implementation is guided by rigorous validation and ethical safeguards.
