Akbulut S, Colak C. Multitask learning in hepatocellular carcinoma: Integrating diagnosis, prognosis, and clinical decision support. World J Gastrointest Oncol 2026; 18(9): 121975 [DOI: 10.4251/wjgo.121975]
Corresponding Author of This Article
Sami Akbulut, FACS, MD, PhD, Professor, Surgery and Liver Transplantation, Inonu University Faculty of Medicine, Elazig Yolu 10 Kilometers, Malatya 44280, Türkiye. akbulutsami@gmail.com
Research Domain of This Article
Surgery
Article-Type of This Article
review-article
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 Gastrointest Oncol. Sep 15, 2026; 18(9): 121975 Published online Sep 15, 2026. doi: 10.4251/wjgo.121975
Multitask learning in hepatocellular carcinoma: Integrating diagnosis, prognosis, and clinical decision support
Sami Akbulut, Cemil Colak
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 and designed the review, performed the literature synthesis, wrote the manuscript, critically revised the content, and approved the final version.
AI contribution statement: A large language model, ChatGPT (OpenAI, San Francisco, CA, United States), was used solely for English-language polishing, including grammar correction, word choice, sentence structure, and readability improvement, as the authors are not native English speakers. No part of the scientific content was generated by AI. The large language model was not used for literature selection, scientific interpretation, study design, data analysis, or formulation of the conclusions. No figures or images were generated by AI. All scientific content was written, carefully reviewed, and approved by the authors, who accept full responsibility for its accuracy and integrity.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Sami Akbulut, FACS, MD, PhD, Professor, Surgery and Liver Transplantation, Inonu University Faculty of Medicine, Elazig Yolu 10 Kilometers, Malatya 44280, Türkiye. akbulutsami@gmail.com
Received: April 7, 2026 Revised: May 20, 2026 Accepted: June 17, 2026 Published online: September 15, 2026 Processing time: 156 Days and 8.9 Hours
Abstract
Hepatocellular carcinoma (HCC) is a clinically heterogeneous malignancy in which diagnosis, structural characterization, biologic aggressiveness assessment, prognostication, and treatment planning are closely interconnected, yet these objectives are often modeled in isolation. Multitask learning (MTL) jointly optimizes related tasks within a shared representational framework and may improve data efficiency, reduce overfitting, and better reflect the multidimensional nature of clinical decision-making in HCC. This narrative review provides a structured overview of MTL in HCC and current applications. Using a Scale for the Assessment of Narrative Review Articles-informed targeted literature search, 35 studies were examined and 16 were retained for final synthesis, organized into diagnostic, structural, prognostic, and treatment-related tasks. Reviewed evidence indicates that MTL has been applied to tumor segmentation with histological grading, joint prediction of microvascular invasion and vessels encapsulating tumor clusters, recurrence and survival modeling, subtype-specific prognostic stratification, treatment response prediction, and future macrovascular invasion risk assessment. Where directly compared, MTL often outperformed corresponding single-task models and enabled coherent risk stratification. Architecturally, designs have expanded from hard parameter-sharing convolutional neural networks to transformer-based, uncertainty-aware, adversarial, and multimodal models. Although promising for integrated clinical decision support, clinical translation still requires biologically justified task pairing, improved interpretability, robust external validation, prospective multicenter studies, and larger high-quality multitask annotated datasets.
Core Tip: Hepatocellular carcinoma (HCC) management involves interrelated tasks, including diagnosis, tumor characterization, microvascular invasion prediction, recurrence-risk estimation, survival modeling, and treatment-response assessment. Conventional single-task artificial intelligence models address these endpoints separately, limiting their ability to capture shared disease biology. This review highlights multitask learning (MTL) as an emerging integrated framework for HCC analysis. Current evidence suggests that MTL may improve performance across structurally and clinically linked tasks while providing more coherent decision support. We also summarize key architectural trends, current limitations, and future directions required for broader clinical translation of MTL in HCC through prospective validation and multicenter methodological refinement.
