Retrospective Study
Copyright ©The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Hepatol. Aug 27, 2025; 17(8): 109530
Published online Aug 27, 2025. doi: 10.4254/wjh.v17.i8.109530
Biparametric magnetic resonance imaging-based radiomic and deep learning models for predicting Ki-67 risk stratification in hepatocellular carcinoma
Xue-Yong Zuo, Hai-Feng Liu
Xue-Yong Zuo, Department of Gastroenterology, Third Affiliated Hospital of Soochow University, Changzhou 213003, Jiangsu Province, China
Hai-Feng Liu, Department of Radiology, Third Affiliated Hospital of Soochow University: Changzhou First People’s Hospital, Changzhou 213003, Jiangsu Province, China
Author contributions: Zuo XY, Liu HF conceived and designed research, performed experiments and analyzed data, prepared figures, edited and revised manuscript; Zuo XY interpreted results of experiments, drafted manuscript; all authors have read and approve the final manuscript.
Supported by Clinical Trials from the Third Affiliated Hospital of Soochow University, No. 2024-156; and Changzhou Science and Technology Program, No. CJ20244017.
Institutional review board statement: The study was approved by Ethics Committee of Third Affiliated Hospital of Soochow University (2022-CL027-01).
Informed consent statement: As no direct participant contact or additional data collection occurred, no statement of informed consent is required.
Conflict-of-interest statement: The authors declare that they have no conflict of interest.
Data sharing statement: All data used or analyzed during this study are included in this article and its supplementary information files.
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: Xue-Yong Zuo, MM, Department of Gastroenterology, Third Affiliated Hospital of Soochow University, No. 185 Juqian Street, Tianning District, Changzhou 213003, Jiangsu Province, China. zuoxueyong@outlook.com
Received: May 15, 2025
Revised: June 16, 2025
Accepted: July 10, 2025
Published online: August 27, 2025
Processing time: 105 Days and 17.5 Hours
Abstract
BACKGROUND

Hepatocellular carcinoma (HCC) is a prevalent and life-threatening cancer with increasing incidence worldwide. High Ki-67 risk stratification is closely associated with higher recurrence rates and worse outcomes following curative therapies in patients with HCC. However, the performance of radiomic and deep transfer learning (DTL) models derived from biparametric magnetic resonance imaging (bpMRI) in predicting Ki-67 risk stratification and recurrence-free survival (RFS) in patients with HCC remains limited.

AIM

To develop a nomogram model integrating bpMRI-based radiomic and DTL signatures for predicting Ki-67 risk stratification and RFS in patients with HCC.

METHODS

This study included 198 patients with histopathologically confirmed HCC who underwent preoperative bpMRI. Ki-67 risk stratification was categorized as high (> 20%) or low (≤ 20%) according to immunohistochemical staining. Radiomic and DTL signatures were extracted from the T2-weighted and arterial-phase images and combined through a random forest algorithm to establish radiomic and DTL models, respectively. Multivariate regression analysis identified clinical risk factors for high Ki-67 risk stratification, and a predictive nomogram model was developed.

RESULTS

A nonsmooth margin and the absence of an enhanced capsule were independent factors for high Ki-67 risk stratification. The area under the curve (AUC) of the clinical model was 0.77, while those of the radiomic and DTL models were 0.81 and 0.87, respectively, for the prediction of high Ki-67 risk stratification, and the nomogram model achieved a better AUC of 0.92. The median RFS times for patients with high and low Ki-67 risk stratification were 33.00 months and 66.73 months, respectively (P < 0.001). Additionally, patients who were predicted to have high Ki-67 risk stratification by the nomogram model had a lower median RFS than those who were predicted to have low Ki-67 risk stratification (33.53 vs 66.74 months, P = 0.007).

CONCLUSION

Our developed nomogram model demonstrated good performance in predicting Ki-67 risk stratification and predicting survival outcomes in patients with HCC.

Keywords: Hepatocellular carcinoma; Ki-67; Radiomics; Deep transfer learning; Recurrence-free survival

Core Tip: In this study, our developed nomogram model can serve as an effective imaging biomarker for predicting Ki-67 risk stratification and predicting survival outcomes in hepatocellular carcinoma patients, with better reliability and clinical utility.