PET applications in neurooncology have received new effectiveness by the advent of positron-emission labelled amino acids, so that it has been coined the term “Amino acid PET” to differentiate this imaging tool from FDG-PET. Radiolabeled amino acids are a very interesting class of PET tracers with great diagnostic potential in neuro-oncology because of their low uptake in normal brain and, conversely, high uptake in most brain tumors including low-grade gliomas.

The use of L-[methyl-11C]Methionine (MET) is restricted to PET centers with an in-house cyclotron and radiochemistry facility, because of the short half-life (20 min) of 11C. The promising results of MET have stimulated the development of 18F-labelled aminoacid tracers, particularly O-(2-18F-fluoeoethyl1)-L-tyrosine (FET), that has the same properties of MET and, thanks to the longer half-life of 18F (about 110 min), allows a distribution strategy from a production tracer site to user satellite PET centers. Considering a more widespread use of Amino acid PET, together with the recent development of integrated PET-MRI imaging systems, and the oncoming clinical validation of other interesting PET tracers, i.e. FMISO or 18F-FAZA for hypoxia imaging and FLT for tumor proliferation imaging, it can be reasonably expected that metabolic imaging with PET is close to becoming a key diagnostic modality in the management of brain tumors, as has already been for Total Body FDG-PET/CT in extra-brain oncology 1).

Modern multiparametric MRI techniques such as diffusion weighted imaging (DWI) with apparent diffusion coefficient (ADC) mapping, dynamic susceptibility weighted contrast enhanced perfusion imaging, and MR spectroscopy (MRS) allow a much deeper and still noninvasive insight into interpretation of brain lesions, resulting in greater specificity of diagnostic imaging, especially in combination with PET with radiolabeled aminoacid 2) 3) 4). 5). 6).

Crippa F, Alessi A, Serafini GL. PET with radiolabeled aminoacid. Q J Nucl Med Mol Imaging. 2012 Apr;56(2):151-62. Review. PubMed PMID: 22617237.
Kao H.-W., Chiang S.-W., Chung H.-W., Tsai F. Y., Chen C.-Y. Advanced MR imaging of gliomas: an update. BioMed Research International. 2013;2013:14. doi: 10.1155/2013/970586.970586
Bulik M., Jancalek R., Vanicek J., Skoch A., Mechl M. Potential of MR spectroscopy for assessment of glioma grading. Clinical Neurology and Neurosurgery. 2013;115(2):146–153. doi: 10.1016/j.clineuro.2012.11.002
Roy B., Gupta R. K., Maudsley A. A., et al. Utility of multiparametric 3-T MRI for glioma characterization. Neuroradiology. 2013;55(5):603–613. doi: 10.1007/s00234-013-1145-x
Ahmed R., Oborski M. J., Hwang M., Lieberman F. S., Mountz J. M. Malignant gliomas: current perspectives in diagnosis, treatment, and early response assessment using advanced quantitative imaging methods. Cancer Management and Research. 2014;6(1):149–170. doi: 10.2147/cmar.s54726
Ion-Margineanu A., van Cauter S., Sima D. M., et al. Tumour relapse prediction using multiparametric MR data recorded during follow-up of GBM patients. BioMed Research International. In press
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