The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis
ObjectiveThe aim of this study was to assess the diagnostic performance of radiomics-based MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodThe databases of PubMed, Cochrane library, Embase, Web of Science, Ovid MEDLINE, Springer, and Science Direct were searche...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.960944/full |
| _version_ | 1797938327898292224 |
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| author | Gao Liang Wei Yu Shuqin Liu Mingxing Zhang Mingguo Xie Min Liu Wenbin Liu |
| author_facet | Gao Liang Wei Yu Shuqin Liu Mingxing Zhang Mingguo Xie Min Liu Wenbin Liu |
| author_sort | Gao Liang |
| collection | DOAJ |
| description | ObjectiveThe aim of this study was to assess the diagnostic performance of radiomics-based MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodThe 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. ResultsA 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).ConclusionRadiomics-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. |
| first_indexed | 2024-04-10T18:58:43Z |
| format | Article |
| id | doaj.art-7ae359c13c084f59abecca8d6b7f6027 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-10T18:58:43Z |
| publishDate | 2023-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-7ae359c13c084f59abecca8d6b7f60272023-01-31T13:32:39ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2023-01-011210.3389/fonc.2022.960944960944The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysisGao Liang0Wei Yu1Shuqin Liu2Mingxing Zhang3Mingguo Xie4Min Liu5Wenbin Liu6Department of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaDepartment of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaDepartment of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaDepartment of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaDepartment of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaToxicology Department, West China-Frontier PharmaTech Co., Ltd. (WCFP), Chengdu, Sichuan, ChinaDepartment of Radiology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, Sichuan, ChinaObjectiveThe aim of this study was to assess the diagnostic performance of radiomics-based MRI in predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC).MethodThe 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. ResultsA 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).ConclusionRadiomics-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.https://www.frontiersin.org/articles/10.3389/fonc.2022.960944/fullhepatocellular carcinomamicrovascular invasionMRIradiomicsmeta-analysis |
| spellingShingle | Gao Liang Wei Yu Shuqin Liu Mingxing Zhang Mingguo Xie Min Liu Wenbin Liu The diagnostic performance of radiomics-based MRI in predicting microvascular invasion in hepatocellular carcinoma: A meta-analysis Frontiers in Oncology hepatocellular carcinoma microvascular invasion MRI radiomics 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 | hepatocellular carcinoma microvascular invasion MRI radiomics meta-analysis |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.960944/full |
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