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World J Gastrointest Oncol. Apr 15, 2022; 14(4): 765-793
Published online Apr 15, 2022. doi: 10.4251/wjgo.v14.i4.765
Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities
Chrysanthos D Christou, Georgios Tsoulfas
Chrysanthos D Christou, Georgios Tsoulfas, Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, Thessaloniki 54622, Greece
Author contributions: Christou CD performed the screening of articles for eligibility and drafted the manuscript; Tsoulfas G performed the screening of articles for eligibility and edited the manuscript.
Conflict-of-interest statement: The authors declare no conflict of interest for this article. The authors received no specific funding for this work.
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: Georgios Tsoulfas, MD, PhD, Associate Professor, Surgeon, Department of Transplantation Surgery, Hippokration General Hospital, School of Medicine, Aristotle University of Thessaloniki, 49 Konstantinoupoleos Street, Thessaloniki 54622, Greece. tsoulfasg@gmail.com
Received: April 15, 2021
Peer-review started: April 15, 2021
First decision: June 4, 2021
Revised: August 24, 2021
Accepted: March 25, 2022
Article in press: March 25, 2022
Published online: April 15, 2022
Abstract

Hepatocellular carcinoma (HCC) constitutes the fifth most frequent malignancy worldwide and the third most frequent cause of cancer-related deaths. Currently, treatment selection is based on the stage of the disease. Emerging fields such as three-dimensional (3D) printing, 3D bioprinting, artificial intelligence (AI), and machine learning (ML) could lead to evidence-based, individualized management of HCC. In this review, we comprehensively report the current applications of 3D printing, 3D bioprinting, and AI/ML-based models in HCC management; we outline the significant challenges to the broad use of these novel technologies in the clinical setting with the goal of identifying means to overcome them, and finally, we discuss the opportunities that arise from these applications. Notably, regarding 3D printing and bioprinting-related challenges, we elaborate on cost and cost-effectiveness, cell sourcing, cell viability, safety, accessibility, regulation, and legal and ethical concerns. Similarly, regarding AI/ML-related challenges, we elaborate on intellectual property, liability, intrinsic biases, data protection, cybersecurity, ethical challenges, and transparency. Our findings show that AI and 3D printing applications in HCC management and healthcare, in general, are steadily expanding; thus, these technologies will be integrated into the clinical setting sooner or later. Therefore, we believe that physicians need to become familiar with these technologies and prepare to engage with them constructively.

Keywords: Artificial intelligence, Machine learning, Three-dimensional printing, Bioprinting, Hepatocellular carcinoma, Liver cancer

Core Tip: The opportunities that arise from the application of three-dimensional (3D) printing and 3D bioprinting in the management of hepatocellular carcinoma (HCC) include resident education, patient education, preoperative planning, fabrication of custom-made medical tools, liver models for antitumor drug development, and patient-derived HCC models for targeted treatment selection. Similarly, the opportunities that arise from the application of artificial intelligence/machine learning in the management of HCC include targeted screening for patients with chronic hepatitis B and C infections, non-invasive early detection of HCC, increased diagnostic accuracy, frameworks for evidence-based, individualized treatment allocation, and prognostic models for the prediction of patient outcomes including overall survival, disease-free survival, and recurrence that could be used for patient and family counseling.