Retrospective Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastroenterol. Apr 7, 2023; 29(13): 2001-2014
Published online Apr 7, 2023. doi: 10.3748/wjg.v29.i13.2001
Preoperative prediction of macrotrabecular-massive hepatocellular carcinoma through dynamic contrast-enhanced magnetic resonance imaging-based radiomics
Yang Zhang, Dong He, Jing Liu, Yu-Guo Wei, Lin-Lin Shi
Yang Zhang, Dong He, Jing Liu, Cancer Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou 310014, Zhejiang Province, China
Yu-Guo Wei, Precision Health Institution, General Electric Healthcare, Hangzhou 310014, Zhejiang Province, China
Lin-Lin Shi, Department of Gastroenterology, Zhejiang Hospital of Integrated Traditional Chinese and Western Medicine, Hangzhou 310005, Zhejiang Province, China
Author contributions: Shi LL was the guarantor and designed the study; Zhang Y, He D, Liu J, and Shi LL participated in the acquisition, analysis, and interpretation of the data; Wei YG reviewed statistical methods; Zhang Y drafted the initial manuscript; Shi LL revised the article critically for important intellectual content; all authors have read and approve the final manuscript.
Supported by Zhejiang Provincial Natural Science Foundation of China, No. LTGY23H180017; and Medical Science and Technology Project of Zhejiang Province, No. 2023KY503.
Institutional review board statement: The study was reviewed and approved by the ethics committee at Zhejiang Provincial People’s Hospital (Approval No. QT2022339).
Informed consent statement: Written informed consent was not required for this study because of retrospective study.
Conflict-of-interest statement: There are no conflicts of interest to report.
Data sharing statement: No additional data are available.
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-Lin Shi, MD, Doctor, Department of Gastroenterology, Zhejiang Hospital of Integrated Traditional Chinese and Western Medicine, No. 208 East Ring Road, Hangzhou 310005, Zhejiang Province, China. linlinshi2022@163.com
Received: December 12, 2022
Peer-review started: December 12, 2022
First decision: January 22, 2023
Revised: February 1, 2023
Accepted: March 20, 2023
Article in press: March 20, 2023
Published online: April 7, 2023
Processing time: 116 Days and 2.9 Hours
Abstract
BACKGROUND

Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) is closely related to aggressive phenotype, gene mutation, carcinogenic pathway, and immunohistochemical markers and is a strong independent predictor of early recurrence and poor prognosis. With the development of imaging technology, successful applications of contrast-enhanced magnetic resonance imaging (MRI) have been reported in identifying the MTM-HCC subtype. Radiomics, as an objective and beneficial method for tumour evaluation, is used to convert medical images into high-throughput quantification features that greatly push the development of precision medicine.

AIM

To establish and verify a nomogram for preoperatively identifying MTM-HCC by comparing different machine learning algorithms.

METHODS

This retrospective study enrolled 232 (training set, 162; test set, 70) hepatocellular carcinoma patients from April 2018 to September 2021. A total of 3111 radiomics features were extracted from dynamic contrast-enhanced MRI, followed by dimension reduction of these features. Logistic regression (LR), K-nearest neighbour (KNN), Bayes, Tree, and support vector machine (SVM) algorithms were used to select the best radiomics signature. We used the relative standard deviation (RSD) and bootstrap methods to quantify the stability of these five algorithms. The algorithm with the lowest RSD represented the best stability, and it was used to construct the best radiomics model. Multivariable logistic analysis was used to select the useful clinical and radiological features, and different predictive models were established. Finally, the predictive performances of the different models were assessed by evaluating the area under the curve (AUC).

RESULTS

The RSD values based on LR, KNN, Bayes, Tree, and SVM were 3.8%, 8.6%, 4.3%, 17.7%, and 17.4%, respectively. Therefore, the LR machine learning algorithm was selected to construct the best radiomics signature, which performed well with AUCs of 0.766 and 0.739 in the training and test sets, respectively. In the multivariable analysis, age [odds ratio (OR) = 0.956, P = 0.034], alpha-fetoprotein (OR = 10.066, P < 0.001), tumour size (OR = 3.316, P = 0.002), tumour-to-liver apparent diffusion coefficient (ADC) ratio (OR = 0.156, P = 0.037), and radiomics score (OR = 2.923, P < 0.001) were independent predictors of MTM-HCC. Among the different models, the predictive performances of the clinical-radiomics model and radiological-radiomics model were significantly improved compared to those of the clinical model (AUCs: 0.888 vs 0.836, P = 0.046) and radiological model (AUCs: 0.796 vs 0.688, P = 0.012), respectively, in the training set, highlighting the improved predictive performance of radiomics. The nomogram performed best, with AUCs of 0.896 and 0.805 in the training and test sets, respectively.

CONCLUSION

The nomogram containing radiomics, age, alpha-fetoprotein, tumour size, and tumour-to-liver ADC ratio revealed excellent predictive ability in preoperatively identifying the MTM-HCC subtype.

Keywords: Hepatocellular carcinoma; Macrotrabecular-massive subtype; Algorithms; Radiomics; Models; Nomogram

Core Tip: Radiomics features can be used to predict the macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) subtype. The logistic regression algorithm can improve the accuracy and stability of predicting MTM-HCC. Age, alpha-fetoprotein, tumour size, tumour-to-liver apparent diffusion coefficient ratio, and radiomics score were significant independent predictors of MTM-HCC. The nomogram based on radiomics, clinical and radiological features can serve as a noninvasive biomarker to preoperatively identify MTM-HCC.