Published online Apr 15, 2024. doi: 10.4251/wjgo.v16.i4.1309
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
Processing time: 163 Days and 18.3 Hours
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.
The primary motivation is to enhance preoperative MVI prediction in HCC, which is crucial for selecting appropriate treatment plans.
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.
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.
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.
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.
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.