Zhu ZW, Wu J, Guo Y, Ren QY, Li DN, Li ZY, Han L. Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features. World J Gastrointest Oncol 2025; 17(5): 104172 [DOI: 10.4251/wjgo.v17.i5.104172]
Corresponding Author of This Article
Lei Han, MD, Associate Chief Physician, Department of Hepatobiliary Surgery, The General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang 110016, Liaoning Province, China. hanlei1974@sina.com
Research Domain of This Article
Gastroenterology & Hepatology
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 Gastrointest Oncol. May 15, 2025; 17(5): 104172 Published online May 15, 2025. doi: 10.4251/wjgo.v17.i5.104172
Prediction of Ki-67 expression in hepatocellular carcinoma with machine learning models based on intratumoral and peritumoral radiomic features
Zi-Wei Zhu, Jun Wu, Yang Guo, Qiong-Yuan Ren, Dong-Ning Li, Ze-Yu Li, Lei Han
Zi-Wei Zhu, Ze-Yu Li, China Medical University, The General Hospital of Northern Theater Command Training Base for Graduate, Shenyang 110000, Liaoning Province, China
Jun Wu, Yang Guo, Lei Han, Department of Hepatobiliary Surgery, The General Hospital of Northern Theater Command, Shenyang 110016, Liaoning Province, China
Qiong-Yuan Ren, Dong-Ning Li, Dalian Medical University, The General Hospital of Northern Theater Command Training Base for Graduate, Shenyang 110000, Liaoning Province, China
Co-first authors: Zi-Wei Zhu and Jun Wu.
Author contributions: Zhu ZW and Wu J contribute equally to this study as co-first authors; Zhu ZW and Wu J conducted the final model construction, performed comprehensive validation and evaluation of all models, and critically revised and approved the final manuscript; Han L designed the entire study; Zhu ZW conducted the radiomics modeling process and completed the manuscript; Wu J and Guo Y gathered imaging data and completed the delineation of the region of interest; Ren QY, Li DN, and Li ZY collected clinical data. All authors have read and agreed to the published version of the manuscript.
Supported by the Joint Science and Technology Plan Project of Liaoning Province, China, No. 2024JH2/102600291.
Institutional review board statement: The study was reviewed and approved by the Ethics Committee of The General Hospital of Northern Theater Command, approval No. Y(2024)168.
Informed consent statement: This study was conducted in compliance with the Declaration of Helsinki. Owing to the retrospective nature of the study and the use of anonymized data, there was no potential risk to the patients involved. Thus, an exemption from informed consent was obtained.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The datasets generated and analysed during the current study are not publicly available due to the hospital policy regarding the use of datasets but are available from the corresponding author on 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: Lei Han, MD, Associate Chief Physician, Department of Hepatobiliary Surgery, The General Hospital of Northern Theater Command, No. 83 Wenhua Road, Shenyang 110016, Liaoning Province, China. hanlei1974@sina.com
Received: December 27, 2024 Revised: January 20, 2025 Accepted: February 26, 2025 Published online: May 15, 2025 Processing time: 138 Days and 18.6 Hours
Abstract
BACKGROUND
Hepatocellular carcinoma (HCC) is one of the most common malignant tumours of the digestive system worldwide. The expression of Ki-67 is crucial for the diagnosis, treatment, and prognostic evaluation of HCC.
AIM
To construct a machine learning model for the preoperative evaluation of Ki-67 expression in HCC and to assist in clinical decision-making.
METHODS
This study included 164 pathologically confirmed HCC patients. Radiomic features were extracted from the computed tomography images reconstructed by superresolution of the intratumoral and peritumoral regions. Features were selected via the intraclass correlation coefficient, t tests, Pearson correlation coefficients and least absolute shrinkage and selection operator regression methods, and models were constructed via various machine learning methods. The best model was selected, and the radiomics score (Radscore) was calculated. A nomogram incorporating the Radscore and clinical risk factors was constructed. The predictive performance of each model was evaluated via receiver operating characteristic (ROC) curves and calibration curves, and decision curve analysis was used to assess the clinical benefits.
RESULTS
In total, 164 HCC patients, namely, 104 patients with high Ki-67 expression and 60 with low Ki-67 expression, were included. Compared with the models in which only intratumoral or peritumoral features were used, the fusion model in which intratumoral and peritumoral features were combined demonstrated stronger predictive ability. Moreover, the clinical-radiomics model including the Radscore and clinical features had higher predictive performance than did the fusion model (area under the ROC curve = 0.848 vs 0.780 in the training group, area under the ROC curve = 0.830 vs 0.760 in the validation group). The calibration curve showed good consistency between the predicted probability and the actual probability, and the decision curve further confirmed its clinical benefit.
CONCLUSION
A machine learning model based on the radiomic features of the intratumoral and peritumoral regions on superresolution computed tomography in conjunction with clinical factors can accurately evaluate Ki-67 expression. The model provides valuable assistance in selecting treatment strategies for HCC patients and contributes to research on neoadjuvant therapy for liver cancer.
Core Tip: Ki-67 expression is significantly correlated with hepatocellular carcinoma prognosis, and preoperative prediction of Ki-67 expression is crucial. To date, scholars have used radiomic features of tumour regions to predict their expression but have overlooked the important role of the peritumoral region. The findings in this study indicate that machine learning models that fully utilize the features of radiomics, tumour surrounding areas, and clinical factors can more accurately predict Ki-67 expression in hepatocellular carcinoma, thereby helping to improve personalized treatment strategies for liver cancer patients.
