title: Lipid and macromolecules quantitation in differentiating glioblastoma from solitary metastasis: a short-echo time single-voxel magnetic resonance spectroscopy study at 3 T.
abstract: OBJECTIVE
  The differentiation between solitary metastasis (MET) and glioblastoma (GBM) is difficult using only magnetic resonance imaging techniques. Magnetic resonance spectroscopy (MRS) lipid signal indicates cellular necrosis both in GBMs and METs. The purpose of this prospective study was to determine whether a class of lipids and/or macromolecules (MMs), able to efficiently discriminate between these two types of lesions, exists.
  
  
  METHODS
  Forty-one patients with solitary brain tumor (23 GBMs and 18 METs) underwent magnetic resonance imaging and single-voxel MRS. Short-echo time point resolved spectroscopy sequence acquisition with water suppression technique was used. Spectra were analyzed using LCModel. Absolute quantification was performed with "water-scaling" procedure. The analysis was focused on sums of lipid and macromolecular (LM) components at 0.9 and 1.3 ppm.
  
  
  RESULTS
  The LM13 absolute concentration was statistically different (P < 0.0001) between GBMs and METs. With a cutoff of 81 mM in LM13 absolute concentration, METs and GBMs can be distinguished with a 78% of specificity and an 81% of sensitivity. The presence of the MM12 peak, related to the fucose II complex, in tumors harboring a K-ras gene mutation has been investigated.
  
  
  CONCLUSIONS
  We exploited the performance of a clinically easily implementable method, such as short-echo time single-voxel MRS, for the differentiation between brain metastasis and primary brain tumors. The study showed that MRS absolute lipid and macromolecular signals could be helpful in differentiating GBM from metastasis. LM13 class was found to be a discriminant parameter with an accuracy of 85%. Detection of the MM12-fucose peak may also have a role in understanding molecular biology of brain metastasis and should be further investigated to address specific metabolic phenotypes.
authors.affiliation: Department of Neuroradiology, Parma University Hospital Trust, Parma, Italy. gcrisi@ao.pr.it
authors.forename: Girolamo
authors.initials: G
authors.lastname: Crisi
authors.forename: Laura
authors.initials: L
authors.lastname: Orsingher
authors.forename: Silvano
authors.initials: S
authors.lastname: Filice
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001932:Brain Neoplasms / Q000175:diagnosis / Q000378:metabolism* / Q000556:secondary; D003287:Contrast Media; D003937:Diagnosis, Differential; D005260:Female; D005909:Glioblastoma / Q000175:diagnosis / Q000378:metabolism* / Q000556:secondary; D006801:Humans; D050356:Lipid Metabolism; D046911:Macromolecular Substances / Q000378:metabolism*; D008279:Magnetic Resonance Imaging; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D012372:ROC Curve; D012680:Sensitivity and Specificity; D018709:Statistics, Nonparametric
chemical_list: D003287:Contrast Media; D046911:Macromolecular Substances