Bredt LC, Peres LAB. Artificial neural network for prediction of acute kidney injury after liver transplantation for cirrhosis and hepatocellular carcinoma. Artif Intell Cancer 2021; 2(5): 51-59 [DOI: 10.35713/aic.v2.i5.51]
Corresponding Author of This Article
Luis Cesar Bredt, FRCS (Gen Surg), MD, PhD, Full Professor, Surgeon, Department of Surgical Oncology and General Surgery, University Hospital of Western Paraná, State University of Western Paraná, Tancredo Neves Avenue, Cascavel 85819-110, Paraná, Brazil. lcbredt@gmail.com
Research Domain of This Article
Transplantation
Article-Type of This Article
Opinion Review
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 Cancer. Oct 28, 2021; 2(5): 51-59 Published online Oct 28, 2021. doi: 10.35713/aic.v2.i5.51
Artificial neural network for prediction of acute kidney injury after liver transplantation for cirrhosis and hepatocellular carcinoma
Luis Cesar Bredt, Luis Alberto Batista Peres
Luis Cesar Bredt, Department of Surgical Oncology and General Surgery, University Hospital of Western Paraná, State University of Western Paraná, Cascavel 85819-110, Paraná, Brazil
Luis Alberto Batista Peres, Department of Nephrology, University Hospital of Western Paraná, State University of Western Paraná, Cascavel 85819-110, Paraná, Brazil
Author contributions: Bredt LC and Peres LAB contributed equally to this review article; all authors equally contributed to this paper with conception and design of the study, literature review and analysis, drafting and critical revision and editing, and final approval of the final version.
Conflict-of-interest statement: No potential conflicts of interest. No financial support.
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: Luis Cesar Bredt, FRCS (Gen Surg), MD, PhD, Full Professor, Surgeon, Department of Surgical Oncology and General Surgery, University Hospital of Western Paraná, State University of Western Paraná, Tancredo Neves Avenue, Cascavel 85819-110, Paraná, Brazil. lcbredt@gmail.com
Received: October 12, 2021 Peer-review started: October 12, 2021 First decision: October 20, 2021 Revised: October 22, 2021 Accepted: October 27, 2021 Article in press: October 27, 2021 Published online: October 28, 2021 Processing time: 15 Days and 10.2 Hours
Abstract
Acute kidney injury (AKI) has serious consequences on the prognosis of patients undergoing liver transplantation (LT) for liver cancer and cirrhosis. Artificial neural network (ANN) has recently been proposed as a useful tool in many fields in the setting of solid organ transplantation and surgical oncology, where patient prognosis depends on a multidimensional and nonlinear relationship between variables pertaining to the surgical procedure, the donor (graft characteristics), and the recipient comorbidities. In the specific case of LT, ANN models have been developed mainly to predict survival in patients with cirrhosis, to assess the best donor-to-recipient match during allocation processes, and to foresee postoperative complications and outcomes. This is a specific opinion review on the role of ANN in the prediction of AKI after LT for liver cancer and cirrhosis, highlighting potential strengths of the method to forecast this serious postoperative complication.
Core Tip: This opinion review aims to explore the potential benefits of artificial neural network models in predicting the occurrence of acute kidney injury in the postoperative period of liver transplantation for cirrhosis and hepatocellular carcinoma.
