Wang ZZ, Song SM, Zhang G, Chen RQ, Zhang ZC, Liu R. Multiparametric magnetic resonance imaging of deep learning-based super-resolution reconstruction for predicting histopathologic grade in hepatocellular carcinoma. World J Gastroenterol 2025; 31(34): 111541 [DOI: 10.3748/wjg.v31.i34.111541]
Corresponding Author of This Article
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
Research Domain of This Article
Oncology
Article-Type of This Article
Retrospective Study
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 Gastroenterol. Sep 14, 2025; 31(34): 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, Shao-Ming Song, Gong Zhang, Rui-Qiu Chen, Zhuo-Chao Zhang, Rong Liu
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
Abstract
BACKGROUND
Deep learning-based super-resolution (SR) reconstruction can obtain high-quality images with more detailed information.
AIM
To compare multiparametric normal-resolution (NR) and SR magnetic resonance imaging (MRI) in predicting the histopathologic grade in hepatocellular carcinoma.
METHODS
We retrospectively analyzed a total of 826 patients from two medical centers (training 459; validation 196; test 171). T2-weighted imaging, diffusion-weighted imaging, and portal venous phases were collected. Tumor segmentations were conducted automatically by 3D U-Net. Based on generative adversarial network, we utilized 3D SR reconstruction to produce SR MRI. Radiomics models were developed and validated by XGBoost and Catboost. The predictive efficiency was demonstrated by calibration curves, decision curve analysis, area under the curve (AUC) and net reclassification index (NRI).
RESULTS
We extracted 3045 radiomic features from both NR and SR MRI, retaining 29 and 28 features, respectively. For XGBoost models, SR MRI yielded higher AUC value than NR MRI in the validation and test cohorts (0.83 vs 0.79; 0.80 vs 0.78), respectively. Consistent trends were seen in CatBoost models: SR MRI achieved AUCs of 0.89 and 0.80 compared to NR MRI’s 0.81 and 0.76. NRI indicated that the SR MRI models could improve the prediction accuracy by -1.6% to 20.9% compared to the NR MRI models.
CONCLUSION
Deep learning-based SR MRI could improve the predictive performance of histopathologic grade in HCC. It may be a powerful tool for better stratification management for patients with operable HCC.
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.
