High-grade glioma can be differentiated from low-grade glioma with chemical shift gradient echo MRI with high degrees of accuracy.
Signal loss ratio>9‰ allows discriminating between grade III–IV glioma versus grade II glioma with 100% specificity and 100% sensitivity.
Signal loss ratio>20‰ predicts a grade IV glioma versus grade III glioma with 75% specificity and 73% sensitivity.
The purpose of this prospective study was to determine whether chemical shift gradient-echo magnetic resonance imaging (MRI) could predict glioma grade.
Materials and methods
A total of 69 patients with 69 gliomas were prospectively included. There were 41 men and 28 women with a mean age of 50±(SD) years (range: 16–82years). All patients had MRI of the brain including chemical shift gradient-echo sequence, further referred to as in- and out-of phase sequence (IP/OP). Intravoxel fat content was estimated by signal loss ratio (SLR=[IP-OP]/2IP), between in- and out-of-phase images, using a region of interest placed on the viable portion of the gliomas. Association between SLR and glioma grade was searched for using Wilcoxon and Mann–Whitney U tests and diagnostic capabilities using area under the receiver operating characteristic (AUROC) curves.
A significant association was found between SLR value and glioma grade (P<0.0001). SLR>9‰ allowed complete discrimination between grade III and grade II glioma with 100% specificity (95% CI: 85–100%), 100% sensitivity (95% CI: 78–100%) and 100% accuracy (95% CI: 90–100%) (AUROC=1). A SLR>20‰ allowed discriminating between grade IV and grade III glioma with 75% specificity (95% CI: 57–89%), 73% sensitivity (95% CI: 45–92%) and 72% accuracy (95% CI: 57–84%) (AUC=0.825, 95% CI: 0.702–0.948). The AUROC for the diagnosis of high-grade glioma (grade III and IV vs. grade II) was 1.
Chemical shift gradient echo MRI provides accurate grading of gliomas. This simple method should be used as a biomarker to predict glioma grade.Le texte complet de cet article est disponible en PDF.
Keywords : Glioma, Chemical shift imaging, Diagnosis, Prospective studies, Biomarkers