Publication Name	World Journal of Gastrointestinal Oncology
Manuscript ID	113870
Country	Türkiye

Category	Gastroenterology & Hepatology
Manuscript Type	Systematic Reviews
Article Title	Pioneering Efficient Deep Learning Architectures for Enhanced Hepatocellular Carcinoma Prediction and Clinical Translation
Manuscript Source	Invited Manuscript
All Author List	Sami Akbulut and Cemil Colak
Funding Agency and Grant Number
Corresponding Author	Sami Akbulut, FACS, MD, Professor, Surgery and Liver Transplantation, Inonu University Faculty of Medicine, Elazig Yolu 10 Km, Malatya 44280, Türkiye. akbulutsami@gmail.com
Key Words	Hepatocellular carcinoma; Deep learning; Convolutional Neural Networks; Recurrent Neural Networks; Transformers; Medical imaging; Artificial intelligence efficiency
Core Tip	Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality worldwide, with limited sensitivity of current screening tools for early detection. Deep learning 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 deep learning solutions that are clinically deployable and impactful.
Citation	Akbulut S, Colak C. Pioneering efficient deep learning architectures for enhanced hepatocellular carcinoma prediction and clinical translation. World J Gastrointest Oncol 2025; In press

Received	2025-09-05 03:13
Peer-Review Started	2025-09-05 03:13
To Make the First Decision
Return for Revision	2025-09-09 09:27
Revised	2025-09-11 22:03
Second Decision	2025-11-26 02:34
Accepted by Journal Editor-in-Chief
Accepted by Executive Editor-in-Chief	2025-11-26 07:02
Articles in Press	2025-11-26 07:02
Publication Fee Transferred
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1948-5204 (online)
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/
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