Related Experiment Video
Updated: Feb 17, 2026

PET and MRI Guided Irradiation of a Glioblastoma Rat Model Using a Micro-irradiator
Published on: December 28, 2017
Discrimination between primary low-grade and high-grade glioma with 11C-methionine PET: a bivariate diagnostic test
Anna Falk Delgado1,2, Alberto Falk Delgado3
11 Department of Clinical Neuroscience, Karolinska Institute , Stockholm , Sweden.
Objective:
To perform a meta-analysis evaluating the diagnostic accuracy of 11C-methionine (MET) positron emission tomography (PET) to discriminate between primary low-grade glioma (LGG) and high-grade glioma (HGG).
Methods:
A systematic database search was performed by a librarian in relevant databases with the latest search on 07 November 2016. Hits were assessed for inclusion independently by two authors. Individual patient data on relative MET uptake was extracted on patients examined pre-operatively with MET PET and subsequent neuropathological diagnosis of astrocytoma or oligodendroglioma. Individual patient data were analysed for diagnostic accuracy using a bivariate diagnostic random-effects meta-analysis model with restricted maximum likelihood estimation method. Bivariate meta-regression and subgroup analyses assessed study heterogeneity and validity. This study is registered with PROSPERO, number CRD42016050747.
Results:
Out of 1828 hits, 13 studies comprising of 241 individuals were included in the quantitative and qualitative analysis. MET PET had an area under the bivariate summary receiver operating characteristics curve of 0.78 to discriminate between LGG and HGG and a summary sensitivity of 0.80 with 95% confidence interval (CI) (0.66-0.88) and a summary false positive rate of 0.28, 95% CI (0.19-0.38). Heterogeneity was described by; bias in patient inclusion, study quality, and ratio method. Optimal cutoff for relative MET uptake was 2.21.
Conclusion:
MET PET had a moderately high diagnostic accuracy for the discrimination between primary LGG and HGG. Advances in knowledge: MET PET can be used as a clinical tool for the non-invasive discrimination between LGG and HGG with a moderately high accuracy at cut-off 2.21.
More Related Videos
06:51Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer
Published on: July 21, 2018
06:48On-Site Sampling and Extraction of Brain Tumors for Metabolomics and Lipidomics Analysis
Published on: May 31, 2020