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©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Radiol. Dec 28, 2025; 17(12): 112911
Published online Dec 28, 2025. doi: 10.4329/wjr.v17.i12.112911
Published online Dec 28, 2025. doi: 10.4329/wjr.v17.i12.112911
Interpretable model based on multisequence magnetic resonance imaging radiomics for predicting the pathological grades of hepatocellular carcinomas
Yue Shi, Peng Zhang, Jing Zheng, Lin Yang, Department of Radiology, Interventional Medical Center, Science and Technology Innovation Center, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
Li Li, Hui-Min Yang, Zu-Mao Li, Institute of Basic Medicine, Department of Pathology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China
Co-corresponding authors: Jing Zheng and Lin Yang.
Author contributions: Shi Y and Zhang P performed the data curation work and prepared the original draft of the manuscript; Zheng J and Yang L conceptualized the study; they contributed equally to this article and are the co-corresponding authors of this manuscript; Li L, Yang HM, and Li ZM performed the pathological grading analysis; and all authors contributed to manuscript revision and provided approval for publishing the final version of the manuscript.
Institutional review board statement: This study was approved by the Medical Ethics Committee of Affiliated Hospital of North Sichuan Medical College, No. 2025ER8-1.
Informed consent statement: Owing to the retrospective data analysis, informed consent from the patients was waived.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.
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: Lin Yang, MD, Department of Radiology, Interventional Medical Center, Science and Technology Innovation Center, Affiliated Hospital of North Sichuan Medical College, No. 63 Wenhua Road, Nanchong 637000, Sichuan Province, China.
Received: August 11, 2025
Revised: September 15, 2025
Accepted: December 3, 2025
Published online: December 28, 2025
Processing time: 138 Days and 22.4 Hours
Revised: September 15, 2025
Accepted: December 3, 2025
Published online: December 28, 2025
Processing time: 138 Days and 22.4 Hours
Core Tip
Core Tip: Despite the promising prospects of using artificial intelligence and machine learning for disease classification and prediction purposes, the complexity and lack of explainability of these methods make it difficult to apply the constructed models in clinical practice. This study aimed to develop and validate an interpretable machine learning model for conducting preoperative pathological grade prediction in hepatocellular carcinoma patients via a combination of multisequence magnetic resonance imaging radiomics and clinical features, which will help clinicians better understand the situation and develop personalized treatment plans.