Accuracy of magnetic resonance imaging texture analysis in differentiating low-grade from high-grade gliomas: systematic review and meta-analysis
| Autor: | Ye Yuan, Hongyang Zhao, De-Qiang Lei, Qiangping Wang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Cochrane Library
Likelihood ratios in diagnostic testing 030218 nuclear medicine & medical imaging Diagnosis Differential 03 medical and health sciences 0302 clinical medicine Medicine Humans texture analysis Original Research Receiver operating characteristic medicine.diagnostic_test business.industry Brain Neoplasms Area under the curve Reproducibility of Results Magnetic resonance imaging General Medicine Publication bias Glioma Nomogram meta-analysis Diffusion Magnetic Resonance Imaging Evidence Based Practice Meta-analysis Neoplasm Grading Nuclear medicine business 030217 neurology & neurosurgery MRI |
| Zdroj: | BMJ Open |
| ISSN: | 2044-6055 |
| Popis: | ObjectivesTexture analysis (TA) is a method used for quantifying the spatial distributions of intensities in images using scanning software. MRI TA could be applied to grade gliomas. This meta-analysis was performed for assessing the accuracy of MRI TA in differentiating low-grade gliomas from high-grade ones.MethodsPubMed, Cochrane Library, Science Direct and Embase were searched for identifying suitable studies from their inception to 1 September 2018. The quality of the studies was evaluated on the basis of the Quality Assessment of Diagnostic Accuracy Studies guidelines. We estimated the pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR) and diagnostic OR (DOR) using the summary receiver operating characteristic (SROC) for identifying the accuracy of MRI TA in grading gliomas. Fagan nomogram was applied for assessing the clinical utility of TA.ResultsSix studies including 440 patients were included and analysed. The pooled sensitivity, specificity, PLR, NLR and DOR with 95% CIs were 0.93 (95% CI 0.88 to 0.96), 0.86 (95% CI 0.81 to 0.89), 6.4 (95% CI 4.8 to 8.6), 0.08 (95% CI 0.05 to 0.15) and 78 (95% CI 39 to 156), respectively. The SROC curve showed an area under the curve of 0.96 (95% CI 0.93 to 0.97). Deeks test confirmed no significant publication bias in all studies. Fagan nomogram revealed that the post-test probability increased by 43% in patients with positive pre-test.ConclusionsThe findings of this meta-analysis suggested that MRI TA has high accuracy in differentiating low-grade gliomas from high-grade ones. A standardised methodology is warranted to guide the use of this technique for clinical decision-making. |
| Databáze: | OpenAIRE |
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