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@ARTICLE{Rapp:133044,
author = {Rapp, M. and Heinzel, Alexander and Galldiks, Norbert and
Stoffels, Gabriele and Felsberg, Jörg and Ewelt, Christian
and Sabel, Michael and Steiger, Hans J and Reifenberger,
Guido and Beez, Thomas and Coenen, Heinrich Hubert and
Floeth, Frank W and Langen, Karl-Josef},
title = {{D}iagnostic {P}erformance of 18{F}-{FET} {PET} in {N}ewly
{D}iagnosed {C}erebral {L}esions {S}uggestive of {G}lioma},
journal = {Journal of nuclear medicine},
volume = {54},
number = {2},
issn = {0161-5505},
address = {Reston, Va.},
publisher = {SNM84042},
reportid = {FZJ-2013-01608},
pages = {229-235},
year = {2013},
abstract = {The aim of this study was to assess the clinical value of
O-(2-(18)F-fluoroethyl)-l-tyrosine ((18)F-FET) PET in the
initial diagnosis of cerebral lesions suggestive of
glioma.In a retrospective study, we analyzed the clinical,
radiologic, and neuropathologic data of 174 patients (77
women and 97 men; mean age, 45 ± 15 y) who had been
referred for neurosurgical assessment of unclear brain
lesions and had undergone (18)F-FET PET. Initial histology
(n = 168, confirmed after surgery or biopsy) and the
clinical course and follow-up MR imaging in 2 patients
revealed 66 high-grade gliomas (HGG), 77 low-grade gliomas
(LGG), 2 lymphomas, and 25 nonneoplastic lesions (NNL). In a
further 4 patients, initial histology was unspecific, but
during the course of the disease all patients developed an
HGG. The diagnostic value of maximum and mean tumor-to-brain
ratios (TBR(max/)TBR(mean)) of (18)F-FET uptake was assessed
using receiver-operating-characteristic (ROC) curve analyses
to differentiate between neoplastic lesions and NNL, between
HGG and LGG, and between high-grade tumor (HGG or lymphoma)
and LGG or NNL.Neoplastic lesions showed significantly
higher (18)F-FET uptake than NNL (TBR(max), 3.0 ± 1.3 vs.
1.8 ± 0.5; P < 0.001). ROC analysis yielded an optimal
cutoff of 2.5 for TBR(max) to differentiate between
neoplastic lesions and NNLs (sensitivity, $57\%;$
specificity, $92\%;$ accuracy, $62\%;$ area under the curve
[AUC], 0.76; $95\%$ confidence interval [CI], 0.68-0.84).
The positive predictive value (PPV) was $98\%,$ and the
negative predictive value (NPV) was $27\%.$ ROC analysis for
differentiation between HGG and LGG (TBR(max), 3.6 ± 1.4
vs. 2.4 ± 1.0; P < 0.001) yielded an optimal cutoff of 2.5
for TBR(max) (sensitivity, $80\%;$ specificity, $65\%;$
accuracy, $72\%;$ AUC, 0.77; PPV, $66\%;$ NPV, $79\%;$
$95\%$ CI, 0.68-0.84). Best differentiation between
high-grade tumors (HGG or lymphoma) and both NNL and LGG was
achieved with a TBR(max) cutoff of 2.5 (sensitivity, $79\%;$
specificity, $72\%;$ accuracy, $75\%;$ AUC, 0.79; PPV,
$65\%;$ NPV, $84\%;$ $95\%$ CI, 0.71-0.86). The results for
TBR(mean) were similar with a cutoff of 1.9.(18)F-FET uptake
ratios provide valuable additional information for the
differentiation of cerebral lesions and the grading of
gliomas. TBR(max) of (18)F-FET uptake beyond the threshold
of 2.5 has a high PPV for detection of a neoplastic lesion
and supports the necessity of an invasive procedure, for
example, biopsy or surgical resection. Low (18)F-FET uptake
(TBR(max) < 2.5) excludes a high-grade tumor with high
probability.},
cin = {INM-3 / INM-4 / INM-5},
ddc = {610},
cid = {I:(DE-Juel1)INM-3-20090406 / I:(DE-Juel1)INM-4-20090406 /
I:(DE-Juel1)INM-5-20090406},
pnm = {333 - Pathophysiological Mechanisms of Neurological and
Psychiatric Diseases (POF2-333)},
pid = {G:(DE-HGF)POF2-333},
typ = {PUB:(DE-HGF)16},
pubmed = {pmid:23232275},
UT = {WOS:000314691200023},
doi = {10.2967/jnumed.112.109603},
url = {https://juser.fz-juelich.de/record/133044},
}