RT Journal Article
SR Electronic
T1 Methodological differences in O-(2-[F-18]Fluoroethyl)-L-tyrosine PET of gliomas
JF Journal of Nuclear Medicine
JO J Nucl Med
FD Society of Nuclear Medicine
SP 1553
OP 1553
VO 56
IS supplement 3
A1 Filss, Christian
A1 Jansen, Nathalie
A1 Böning, Guido
A1 Rota Kops, Elena
A1 Suchorska, Bogdana
A1 Galldiks, Norbert
A1 Mottaghy, Felix
A1 Bartenstein, Peter
A1 Tonn, Joerg-Christian
A1 Langen, Karl-Josef
YR 2015
UL http://jnm.snmjournals.org/content/56/supplement_3/1553.abstract
AB 1553 Objectives PET using O-(2-[F-18]Fluoroethyl)-L-tyrosine (FET) is a well-established method for diagnosis of gliomas but there are controversies about different results and threshold values for tumor evaluation. We analyzed methodological differences between two large centers in Germany.Methods Both centers (A/B) used an ECAT - HR+ PET Scanner (Siemens) for FET PET. Methodological differences between A and B were registered concerning framing of PET dynamic data, data reconstruction [A: filtered back projection (FBP) plus 5 mm 3D Gaussian filter; B: iterative reconstruction (ITR) without filtering], tumor to brain rations (TBR) and tumor volume (Tvol) based on different cut-off for tumor delineation [A: TBR > 1.8; B: TBR > 1.6], different ROI definition to determine time activity curves (TAC) in the tumor [A: 90% isocontour of tumor maximum; B: TBR>1.6]. The effect of the different methodologies on TBRmean, TBRmax, Tvol, time-to-peak (TTP) and slope of the TAC (10-40 min p.i.) were analyzed in 20 patients with cerebral gliomas.Results Significant differences between centers A and B in tumor characterization were found for TBRmax (2.51± 0.86 vs 3.31 ± 1.04; p< 0.001), TBRmean (2.07± 0.43 vs 2.14 ± 0.41; p=0.04), and for Tvol (1.22± 1.58 vs 1.65 ± 1.75; p< 0.001) (paired t-test). The differences in TTP and slope of the TAC were not significant.Conclusions Differences in data processing between the two centers lead to considerable differences especially for TBR and Tvol which could lead to important differences in clinical decision making. A standardization of data processing is needed to make clinical results comparable.