Systematic Reviews
Copyright ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Nov 14, 2020; 26(42): 6679-6688
Published online Nov 14, 2020. doi: 10.3748/wjg.v26.i42.6679
Prognostic role of artificial intelligence among patients with hepatocellular cancer: A systematic review
Quirino Lai, Gabriele Spoletini, Gianluca Mennini, Zoe Larghi Laureiro, Diamantis I Tsilimigras, Timothy Michael Pawlik, Massimo Rossi
Quirino Lai, Gianluca Mennini, Zoe Larghi Laureiro, Massimo Rossi, Hepato-biliary and Organ Transplant Unit, Department of Surgery, Sapienza University of Rome, Rome 00161, Italy
Gabriele Spoletini, General Surgery and Liver Transplantation Unit, Fondazione Policlinico Universitario A Gemelli IRCCS, Rome 00100, Italy
Diamantis I Tsilimigras, Timothy Michael Pawlik, Wexner Medical Center, The Ohio State University, Columbus, OH 43210, United States
Author contributions: Lai Q designed the study; Rossi M gave administrative support; Lai Q and Larghi Laureiro Z collected the data; Lai Q, Spoletini G, Mennini G, Larghi Laureiro Z and Tsilimigras DI analyzed and interpreted the data; Lai Q, Spoletini G, and Pawlik TM wrote the manuscript; all the authors approved the final version of the manuscript.
Conflict-of-interest statement: The authors have no conflict of interest to declare.
PRISMA 2009 Checklist statement: The authors have read the PRISMA 2009 Checklist, and the manuscript was prepared and revised in accordance with this checklist.
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: http://creativecommons.org/licenses/by-nc/4.0/
Corresponding author: Quirino Lai, MD, PhD, Academic Fellow, Academic Research, Assistant Lecturer, Doctor, Senior Lecturer, Hepato-biliary and Organ Transplant Unit, Department of Surgery, Sapienza University of Rome, Roma, Rome 00161, Italy. quirino.lai@uniroma1.it
Received: August 29, 2020
Peer-review started: August 29, 2020
First decision: September 12, 2020
Revised: September 14, 2020
Accepted: October 1, 2020
Article in press: October 1, 2020
Published online: November 14, 2020
ARTICLE HIGHLIGHTS
Research background

Prediction of survival after the treatment of hepatocellular carcinoma (HCC) has been widely investigated, yet remains inadequate. The application of artificial intelligence (AI) is emerging as a valid adjunct to traditional statistics due to the ability to process vast amounts of data and find hidden interconnections between variables. AI and deep learning are increasingly employed in several topics of liver cancer research, including diagnosis, pathology, and prognosis.

Research motivation

AI applied to survival prediction after HCC treatment should provide enhanced accuracy compared with conventional linear systems of analysis.

Research objectives

Improved transferability and reproducibility will facilitate the widespread use of AI methodologies.

Research methods

A web-based literature search was performed according to the Preferred Reporting Items for Systemic Reviews and Meta-Analysis guidelines using the keywords “artificial intelligence”, “deep learning” and “hepatocellular carcinoma” (and synonyms).

Research results

Among the 598 articles screened, nine papers met the inclusion criteria, six of which had low-risk rates of bias. Eight articles were published in the last decade; all came from eastern countries. Patient sample size was extremely heterogenous (n = 11-22926). AI methodologies employed included artificial neural networks (ANN) in six studies, as well as support vector machine, artificial plant optimization, and peritumoral radiomics in the remaining three studies. All the studies testing the role of ANN compared the performance of ANN with traditional statistics. Training cohorts were used to train the neural networks that were then applied to validation cohorts. In all cases, the AI models demonstrated superior predictive performance compared with traditional statistics with significantly improved areas under the curve.

Research conclusions

AI applied to survival prediction after HCC treatment provided enhanced accuracy compared with conventional linear systems of analysis.

Research perspectives

Improved transferability and reproducibility will facilitate the widespread use of AI methodologies.