Copyright
©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Jul 15, 2025; 17(7): 106610
Published online Jul 15, 2025. doi: 10.4251/wjgo.v17.i7.106610
Published online Jul 15, 2025. doi: 10.4251/wjgo.v17.i7.106610
Integrating radiomics and machine learning for the diagnosis and prognosis of hepatocellular carcinoma
Na Feng, Kun Wang, Department of Sports Medicine, Orthopedics' Clinic, The First Hospital of Jilin University, Changchun 130021, Jilin Province, China
Yan Jiao, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun 130021, Jilin Province, China
Co-corresponding authors: Kun Wang and Yan Jiao.
Author contributions: Feng N, Wang K, and Jiao Y collectively conceptualized and designed the research; Feng N contributed extensively to the manuscript writing, editing, and preparation of tables, as well as conducting the literature search and compiling relevant data; Wang K played a pivotal role in designing the overall conceptual framework, outlining the manuscript, critically interpreting data, and ensuring methodological rigor throughout the research process; Jiao Y contributed significantly to the intellectual content, participating actively in discussions regarding the manuscript structure, clinical relevance, and implications of the findings. Both Wang K and Jiao Y have served as co-corresponding authors, each providing indispensable contributions to the project. Wang K was instrumental in the foundational concept development, overall research design, and strategic oversight of manuscript preparation, while also supervising critical methodological aspects. Jiao Y led the integration of clinical perspectives, particularly focusing on the clinical implications, interpretability, and translational aspects of the manuscript. Furthermore, Jiao Y was responsible for coordinating the submission process and managing correspondence with the journal. The collaboration between Wang K and Jiao Y was essential for the successful completion of this manuscript, demonstrating complementary roles that significantly enhanced the scientific rigor, clarity, and practical relevance of the research. All authors have reviewed, approved the final manuscript, and endorse its publication.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
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: Yan Jiao, Department of Hepatobiliary and Pancreatic Surgery, General Surgery Center, The First Hospital of Jilin University, No. 1 Xinmin Street, Changchun 130021, Jilin Province, China. lagelangri1@126.com
Received: March 3, 2025
Revised: April 18, 2025
Accepted: May 23, 2025
Published online: July 15, 2025
Processing time: 133 Days and 17.3 Hours
Revised: April 18, 2025
Accepted: May 23, 2025
Published online: July 15, 2025
Processing time: 133 Days and 17.3 Hours
Core Tip
Core Tip: The integration of radiomics with machine learning (ML) algorithms holds significant promise in improving the diagnosis and prognosis of hepatocellular carcinoma. Key radiomic features, such as texture, shape, and intensity, when combined with advanced ML techniques, can enhance tumor characterization, predict treatment responses, and provide better prognostic insights. However, challenges related to data heterogeneity, model interpretability, and multi-modal data integration must be addressed for these technologies to be widely adopted in clinical practice.