Machry M, Ferreira LF, Lucchese AM, Kalil AN, Feier FH. Liver volumetric and anatomic assessment in living donor liver transplantation: The role of modern imaging and artificial intelligence. World J Transplant 2023; 13(6): 290-298 [PMID: 38174151 DOI: 10.5500/wjt.v13.i6.290]
Corresponding Author of This Article
Flavia Heinz Feier, PhD, Professor, Department of Hepato-Biliary-Pancreatic Surgery and Liver Transplantation, Irmandade Santa Casa de Misericórdia de Porto Alegre, Rua Prof Annes Dias, Porto Alegre 90020-090, Brazil. flavia.feier@gmail.com
Research Domain of This Article
Radiology, Nuclear Medicine & Medical Imaging
Article-Type of This Article
Minireviews
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/
World J Transplant. Dec 18, 2023; 13(6): 290-298 Published online Dec 18, 2023. doi: 10.5500/wjt.v13.i6.290
Liver volumetric and anatomic assessment in living donor liver transplantation: The role of modern imaging and artificial intelligence
Mayara Machry, Luis Fernando Ferreira, Angelica Maria Lucchese, Antonio Nocchi Kalil, Flavia Heinz Feier
Mayara Machry, Angelica Maria Lucchese, Antonio Nocchi Kalil, Flavia Heinz Feier, Department of Hepato-Biliary-Pancreatic Surgery and Liver Transplantation, Irmandade Santa Casa de Misericórdia de Porto Alegre, Porto Alegre 90020-090, Brazil
Luis Fernando Ferreira, Antonio Nocchi Kalil, Flavia Heinz Feier, Postgraduation Program in Medicine: Hepatology, Federal University of Health Sciences of Porto Alegre, Porto Alegre 90050-170, Brazil
Author contributions: Machry M and Feier FH designed the research study; Ferreira LF and Machry M wrote the manuscript; Kalil AN, Feier FH and Lucchese AM wrote the manuscript and critically evaluated the final version; All authors have read and approve the final manuscript.
Supported byPart by The Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil (CAPES).
Conflict-of-interest statement: Authors declare no conflict of interests for this article.
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: Flavia Heinz Feier, PhD, Professor, Department of Hepato-Biliary-Pancreatic Surgery and Liver Transplantation, Irmandade Santa Casa de Misericórdia de Porto Alegre, Rua Prof Annes Dias, Porto Alegre 90020-090, Brazil. flavia.feier@gmail.com
Received: June 27, 2023 Peer-review started: June 27, 2023 First decision: July 28, 2023 Revised: August 17, 2023 Accepted: October 17, 2023 Article in press: October 17, 2023 Published online: December 18, 2023 Processing time: 173 Days and 14.9 Hours
Core Tip
Core Tip: Accurate liver’s volumetry (LV) is imperative for successful living-donor liver transplantation to ensure adequate future liver remnant and graft volumes. Manual computed tomography scan delineation conventionally serves as the standard approach; however, it is constrained by factors such as cost, subjectivity, and variability. In contrast, automated LV techniques utilizing advanced segmentation algorithms present superior reproducibility, reduced variability, and enhanced efficiency compared with manual measurements. However, the accuracy of automated LV requires further investigation. The study comprehensively reviewed both traditional and emerging LV methods, including semi-automated image processing, automated LV techniques, and machine learning-based approaches, while analyzing their respective strengths and weaknesses.
