Copyright
©The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Feb 15, 2026; 18(2): 113870
Published online Feb 15, 2026. doi: 10.4251/wjgo.v18.i2.113870
Published online Feb 15, 2026. doi: 10.4251/wjgo.v18.i2.113870
Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation
Sami Akbulut, Surgery and Liver Transplantation, Inonu University Faculty of Medicine, Malatya 44280, Türkiye
Sami Akbulut, Cemil Colak, Biostatistics and Medical Informatics, Inonu University Faculty of Medicine, Malatya 44280, Türkiye
Author contributions: Akbulut S and Colak C conceived the project and designed the research; Colak C performed the literature analysis and prepared the tables. Both authors contributed to writing the manuscript and approved the final version.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
PRISMA 2009 Checklist statement: This is a narrative review and does not require the full PRISMA protocol of a systematic review. A reproducible search string and a summary SANRA table was added.
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: Sami Akbulut, MD, FACS, Professor, Surgery and Liver Trans
Received: September 5, 2025
Revised: September 11, 2025
Accepted: November 26, 2025
Published online: February 15, 2026
Processing time: 151 Days and 8.7 Hours
Revised: September 11, 2025
Accepted: November 26, 2025
Published online: February 15, 2026
Processing time: 151 Days and 8.7 Hours
Core Tip
Core Tip: Hepatocellular carcinoma remains a major cause of cancer-related mortality worldwide, with limited sensitivity of current screening tools for early detection. Deep learning (DL) offers great promise, but computational demands often hinder its clinical use. This review highlights advances in efficiency-oriented strategies, including lightweight architectures, pruning, quantization, and multimodal integration, which enable smaller and faster models without major loss of accuracy. By emphasizing validation, fairness audits, regulatory alignment, and workflow integration, we provide guidance for developing explainable and efficient DL solutions that are clinically deployable and impactful.