Akbulut S, Colak C. Edge learning applications in the prediction and classification of combined hepatocellular-cholangiocarcinoma: A comprehensive narrative review. World J Clin Oncol 2025; 16(7): 107246 [DOI: 10.5306/wjco.v16.i7.107246]
Corresponding Author of This Article
Sami Akbulut, MD, PhD, FACS, Department of Surgery and Liver Transplantation, Inonu University Faculty of Medicine, Elazig Yolu 10. Km, Malatya 44280, Türkiye. akbulutsami@gmail.com
Research Domain of This Article
Surgery
Article-Type of This Article
Minireviews
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/
World J Clin Oncol. Jul 24, 2025; 16(7): 107246 Published online Jul 24, 2025. doi: 10.5306/wjco.v16.i7.107246
Edge learning applications in the prediction and classification of combined hepatocellular-cholangiocarcinoma: A comprehensive narrative review
Sami Akbulut, Cemil Colak
Sami Akbulut, Department of 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
Co-corresponding authors: Sami Akbulut and Cemil Colak.
Author contributions: Akbulut S and Colak C conceived the project and designed research, wrote the manuscript and reviewed final version.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
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, PhD, FACS, Department of Surgery and Liver Transplantation, Inonu University Faculty of Medicine, Elazig Yolu 10. Km, Malatya 44280, Türkiye. akbulutsami@gmail.com
Received: March 19, 2025 Revised: April 26, 2025 Accepted: June 18, 2025 Published online: July 24, 2025 Processing time: 125 Days and 20 Hours
Core Tip
Core Tip: Edge learning presents a novel approach in combined hepatocellular-cholangiocarcinoma diagnosis and classification, leveraging decentralized artificial intelligence for real-time processing and enhanced data privacy. Unlike traditional cloud-based artificial intelligence, edge learning enables on-site analysis of histopathological features and medical imaging (computed tomography, magnetic resonance imaging) while reducing latency and bandwidth usage. This review explores its technical integration, including federated learning, deep learning optimizations (convolutional neural networks, pruning, quantization), and privacy-preserving artificial intelligence frameworks. By overcoming challenges like diagnostic complexity and data security, edge learning enhances clinical decision-making, treatment planning, and diagnostic accuracy, offering a transformative potential in precision oncology and liver cancer management.
