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
ARTICLE HIGHLIGHTS
Research background

Macrotrabecular-massive hepatocellular carcinoma (MTM-HCC) shows an aggressive phenotype. Early diagnosis of MTM-HCC is beneficial to prevent early recurrence and improve prognosis. Radiomics can convert medical images into high-throughput quantification features, which greatly push the development of precision medicine.

Research motivation

Currently, magnetic resonance imaging (MRI) features have been successfully applied to identify MTM-HCC but have mainly focused on the qualitative analysis of imaging features. In this study, we systematically analysed radiomics, clinical and radiological features to build a more comprehensive prediction model. We aimed to develop a noninvasive model for the preoperative prediction of MTM-HCC.

Research objectives

In this study, we aimed to establish and verify a nomogram based on contrast-enhanced MRI for preoperatively identifying MTM-HCC by comparing different machine learning algorithms.

Research methods

A total of 232 (training set, 162; test set, 70) hepatocellular carcinoma patients were enrolled. Radiomics features were extracted from contrast-enhanced MRI, followed by dimension reduction. Logistic regression (LR), K-nearest neighbour, Bayes, Tree, and support vector machine algorithms were used to construct radiomics signatures. The relative standard deviation (RSD) was used to quantify the stability of these five algorithms. Multivariable logistic analysis was used to select the useful clinical and radiological features, and different predictive models were established. The performances of the different models were assessed using the area under the curve (AUC).

Research results

The LR algorithm with the smaller RSD (3.8%) was used to construct the best radiomics signature, which performed well with AUCs of 0.766 and 0.739 in the training and test sets, respectively. Age, alpha-fetoprotein, tumour size, tumour-to-liver apparent diffusion coefficient ratio, and radiomics score were identified as independent predictors of MTM-HCC to build the nomogram, which performed best with AUCs of 0.896 and 0.805 in the training and test sets, respectively.

Research conclusions

The nomogram is a reliable tool for preoperatively identifying the MTM-HCC subtype.

Research perspectives

More precise and reliable tools are urgently needed to predict the MTM-HCC subtype. Radiomics is a new method to convert medical images into high-throughput quantification features. In this study, we aimed to develop a dynamic contrast-enhanced MRI-based nomogram for preoperatively identifying the MTM-HCC subtype.