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The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis

OBJECTIVE: The aim of this study was to assess the diagnostic performance of radiomics-based MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHOD: The databases of PubMed, Cochrane library, Embase, Web of Science, Ovid MEDLINE, Springer, and Science Direct were se...

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Autores principales: Liang, Gao, Yu, Wei, Liu, Shuqin, Zhang, Mingxing, Xie, Mingguo, Liu, Min, Liu, Wenbin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9928182/
https://www.ncbi.nlm.nih.gov/pubmed/36798691
http://dx.doi.org/10.3389/fonc.2022.960944
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author Liang, Gao
Yu, Wei
Liu, Shuqin
Zhang, Mingxing
Xie, Mingguo
Liu, Min
Liu, Wenbin
author_facet Liang, Gao
Yu, Wei
Liu, Shuqin
Zhang, Mingxing
Xie, Mingguo
Liu, Min
Liu, Wenbin
author_sort Liang, Gao
collection PubMed
description OBJECTIVE: The aim of this study was to assess the diagnostic performance of radiomics-based MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHOD: The databases of PubMed, Cochrane library, Embase, Web of Science, Ovid MEDLINE, Springer, and Science Direct were searched for original studies from their inception to 20 August 2022. The quality of each study included was assessed according to the Quality Assessment of Diagnostic Accuracy Studies 2 and the radiomics quality score. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated. The summary receiver operating characteristic (SROC) curve was plotted and the area under the curve (AUC) was calculated to evaluate the diagnostic accuracy. Sensitivity analysis and subgroup analysis were performed to explore the source of the heterogeneity. Deeks’ test was used to assess publication bias. RESULTS: A total of 15 studies involving 981 patients were included. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.79 (95%CI: 0.72–0.85), 0.81 (95%CI: 0.73–0.87), 4.1 (95%CI:2.9–5.9), 0.26 (95%CI: 0.19–0.35), 16 (95%CI: 9–28), and 0.87 (95%CI: 0.84–0.89), respectively. The results showed great heterogeneity among the included studies. Sensitivity analysis indicated that the results of this study were statistically reliable. The results of subgroup analysis showed that hepatocyte-specific contrast media (HSCM) had equivalent sensitivity and equivalent specificity compared to the other set. The least absolute shrinkage and selection operator method had high sensitivity and specificity than other methods, respectively. The investigated area of the region of interest had high specificity compared to the volume of interest. The imaging-to-surgery interval of 15 days had higher sensitivity and slightly low specificity than the others. Deeks’ test indicates that there was no publication bias (P=0.71). CONCLUSION: Radiomics-based MRI has high accuracy in predicting MVI in HCC, and it can be considered as a non-invasive method for assessing MVI in HCC.
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spelling pubmed-99281822023-02-15 The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis Liang, Gao Yu, Wei Liu, Shuqin Zhang, Mingxing Xie, Mingguo Liu, Min Liu, Wenbin Front Oncol Oncology OBJECTIVE: The aim of this study was to assess the diagnostic performance of radiomics-based MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC). METHOD: The databases of PubMed, Cochrane library, Embase, Web of Science, Ovid MEDLINE, Springer, and Science Direct were searched for original studies from their inception to 20 August 2022. The quality of each study included was assessed according to the Quality Assessment of Diagnostic Accuracy Studies 2 and the radiomics quality score. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and diagnostic odds ratio (DOR) were calculated. The summary receiver operating characteristic (SROC) curve was plotted and the area under the curve (AUC) was calculated to evaluate the diagnostic accuracy. Sensitivity analysis and subgroup analysis were performed to explore the source of the heterogeneity. Deeks’ test was used to assess publication bias. RESULTS: A total of 15 studies involving 981 patients were included. The pooled sensitivity, specificity, PLR, NLR, DOR, and AUC were 0.79 (95%CI: 0.72–0.85), 0.81 (95%CI: 0.73–0.87), 4.1 (95%CI:2.9–5.9), 0.26 (95%CI: 0.19–0.35), 16 (95%CI: 9–28), and 0.87 (95%CI: 0.84–0.89), respectively. The results showed great heterogeneity among the included studies. Sensitivity analysis indicated that the results of this study were statistically reliable. The results of subgroup analysis showed that hepatocyte-specific contrast media (HSCM) had equivalent sensitivity and equivalent specificity compared to the other set. The least absolute shrinkage and selection operator method had high sensitivity and specificity than other methods, respectively. The investigated area of the region of interest had high specificity compared to the volume of interest. The imaging-to-surgery interval of 15 days had higher sensitivity and slightly low specificity than the others. Deeks’ test indicates that there was no publication bias (P=0.71). CONCLUSION: Radiomics-based MRI has high accuracy in predicting MVI in HCC, and it can be considered as a non-invasive method for assessing MVI in HCC. Frontiers Media S.A. 2023-01-31 /pmc/articles/PMC9928182/ /pubmed/36798691 http://dx.doi.org/10.3389/fonc.2022.960944 Text en Copyright © 2023 Liang, Yu, Liu, Zhang, Xie, Liu and Liu https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Liang, Gao
Yu, Wei
Liu, Shuqin
Zhang, Mingxing
Xie, Mingguo
Liu, Min
Liu, Wenbin
The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis
title The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis
title_full The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis
title_fullStr The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis
title_full_unstemmed The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis
title_short The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis
title_sort diagnostic performance of radiomics-based mri in predicting microvascular invasion in hepatocellular carcinoma: a meta-analysis
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9928182/
https://www.ncbi.nlm.nih.gov/pubmed/36798691
http://dx.doi.org/10.3389/fonc.2022.960944
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