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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Sep 14, 2025; 31(34): 111541
Published online Sep 14, 2025. doi: 10.3748/wjg.v31.i34.111541
Published online Sep 14, 2025. doi: 10.3748/wjg.v31.i34.111541
Multiparametric magnetic resonance imaging of deep learning-based super-resolution reconstruction for predicting histopathologic grade in hepatocellular carcinoma
Zi-Zheng Wang, Department of Hepatobiliary Surgery, Senior Department of Hepatology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing 100039, China
Shao-Ming Song, Rui-Qiu Chen, Rong Liu, The First School of Clinical Medicine, Lanzhou University, Lanzhou 730000, Gansu Province, China
Gong Zhang, Zhuo-Chao Zhang, Rong Liu, Faculty of Hepatopancreatobiliary Surgery, The First Medical Center of Chinese PLA General Hospital, Beijing 100853, China
Co-first authors: Zi-Zheng Wang and Shao-Ming Song.
Author contributions: Wang ZZ and Liu R were involved in the study conception and design; Wang ZZ, Song SM, Zhang G, Chen RQ and Zhang ZC were involved in acquisition of data; Song SM, Zhang G, Chen RQ and Zhang ZC participated in the development and testing of models, analysis and interpretation of the data; Wang ZZ and Song SM were involved in drafting of the manuscript; Liu R is responsible for review, editing and supervision; all authors read and approved the final manuscript. Wang ZZ and Song SM contributed equally to this work and should be considered as the co-first authors.
Supported by AI+ Health Collaborative Innovation Cultivation Project of Beijing City, No. Z221100003522005.
Institutional review board statement: The study was approved by the Institutional Review Board of the Chinese PLA General Hospital (S2016-098-02).
Informed consent statement: Patients were not required to give informed consent to the study because the analysis used anonymous clinical data.
Conflict-of-interest statement: All authors declare no conflicts-of-interest related to this article.
Data sharing statement: The data that support the findings of this study are available from the corresponding author upon reasonable request.
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: Rong Liu, Faculty of Hepatopancreatobiliary Surgery, The First Medical Center of Chinese PLA General Hospital, No. 28 Fuxing Road, Haidian District, Beijing 100853, China. liurong301@126.com
Received: July 4, 2025
Revised: August 1, 2025
Accepted: August 14, 2025
Published online: September 14, 2025
Processing time: 63 Days and 18.8 Hours
Revised: August 1, 2025
Accepted: August 14, 2025
Published online: September 14, 2025
Processing time: 63 Days and 18.8 Hours
Core Tip
Core Tip: In this study, multiparametric magnetic resonance imaging radiomics could non-invasively classify histopathologic grade in hepatocellular carcinoma. The image quality is crucial for radiomics feature extraction and model development. Deep learning-based super-resolution (SR) reconstruction further improved the prediction by optimizing radiomics features. Deep learning-based SR reconstruction may provide deeper insights for precision medicine and disease management.