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©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.
Application of texture signatures based on multiparameter-magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma: Retrospective study
Hai-Yang Nong, Yong-Yi Cen, Mi Qin, Wen-Qi Qin, You-Xiang Xie, Lin Li, Man-Rong Liu, Ke Ding
Hai-Yang Nong, Mi Qin, Wen-Qi Qin, You-Xiang Xie, Ke Ding, Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
Hai-Yang Nong, Department of Radiology, The Affiliated Hospital of Youjiang Medical University for Nationalities, Baise 533000, Guangxi Zhuang Autonomous Region, China
Yong-Yi Cen, Department of Radiology, The First Affiliated Hospital of Guangxi University of Traditional Chinese Medicine, Nanning 530031, Guangxi Zhuang Autonomous Region, China
Lin Li, Department of Hepatobiliary Surgery, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
Man-Rong Liu, Department of Ultrasound, The Third Affiliated Hospital of Guangxi Medical University, Nanning 530031, Guangxi Zhuang Autonomous Region, China
Co-corresponding authors: Man-Rong Liu and Ke Ding.
Author contributions: Nong HY, Ding K, Cen YY, and Qin M carried out the studies, participated in collecting data, and drafted the manuscript; Ding K, Liu MR, Nong HY, and Li L performed the statistical analysis and participated in its design; Qin WQ and Xie YX helped to draft the manuscript; all authors read and approved the final manuscript. Liu MR and Ding K contributed equally to this study and serve as co-corresponding authors of this study.
Supported by National Natural Science Foundation of China, No. 81560278; and the Health Commission of Guangxi Zhuang Autonomous Region, No. Z-A20221157, No. Z20200953, and No. G201903023.
Institutional review board statement: The study protocol was approved by the Ethics Committees of The Third Affiliated Hospital of Guangxi Medical University.
Informed consent statement: All the patients provided written informed consent for participation.
Conflict-of-interest statement: There are no conflicts of interest to report.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
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: Ke Ding, MD, Doctor, Department of Radiology, The Third Affiliated Hospital of Guangxi Medical University, No. 13 Dancun Road, Nanning 530031, Guangxi Zhuang Autonomous Region, China. 272480365@qq.com
Received: October 30, 2023
Peer-review started: October 30, 2023
First decision: December 5, 2023
Revised: December 18, 2023
Accepted: February 5, 2024
Article in press: February 5, 2024
Published online: April 15, 2024
ARTICLE HIGHLIGHTS
Research background
The study aims to provide an objective, non-invasive method for the hepatocellular carcinoma (HCC) prediction, addressing the limitations of current assessment methods that are invasive and carry risks. It seeks to validate the use of texture analysis in magnetic resonance (MR) imaging (MRI) as a reliable predictive tool for microvascular invasion (MVI), potentially guiding clinical decisions and improving poor patient outcomes in HCC.
Research motivation
The primary motivation is to enhance preoperative MVI prediction in HCC, which is crucial for selecting appropriate treatment plans.
Research objectives
The main objective of the study was to assess the effectiveness of texture analysis based on multi-parametric MR images in predicting MVI of HCC. The goal was to provide a non-invasive, objective method to aid in the preoperative prediction of MVI, thereby valuable information for treatment planning and prognosis evaluation in HCC patients.
Research methods
The study employed a retrospective analysis approach, including 105 patients with pathologically confirmed HCC. It used texture analysis methods such as original data analysis, principal component analysis, linear discriminant analysis (LDA), and non-LDA on multi-parametric MR images. The effectiveness of these methods was evaluated using the misjudgment rate derived from the MaZda4.6 software. This approach allowes for a detailed quantitative analysis of the MR images, offering novel insights into the potential of texture analysis in medical imaging.
Research results
The study found that texture analysis of arterial phase images from multi-parametric MRI was highly effective in predicting MVI in HCC. The combination of MI + PA + F dimensionality reduction method and nonlinear discriminant analysis showed the highest prediction accuracy. These results contribute significantly to the field by offering a non-invasive, objective predictive tool for MVI in HCC, potentially improving treatment decisions. However, the need for larger, prospective studies to validate these findings, highlighting a key area for future research in this domain.
Research conclusions
This study introduced new methods for predicting MVI in HCC using texture analysis of multi-parametric MR images. It proposed the combination of MI + PA + F dimensionality reduction and nonlinear discriminant analysis as a novel and effective approach. This methodology represents a significant advancement in non-invasive, objective diagnostic tool in the field of HCC management.
Research perspectives
Future research should focus on validating the efficacy of texture analysis in larger, multicenter studies, exploring its integration with other diagnostic modalities to enhance MVI prediction accuracy in HCC. Additionally, investigating the applicability of this method in other types of cancer could broaden its clinical significance and utility.
