000873811 001__ 873811
000873811 005__ 20210130004517.0
000873811 0247_ $$2doi$$a10.1007/s00259-020-04695-0
000873811 0247_ $$2ISSN$$a0340-6997
000873811 0247_ $$2ISSN$$a1432-105X
000873811 0247_ $$2ISSN$$a1619-7070
000873811 0247_ $$2ISSN$$a1619-7089
000873811 0247_ $$2Handle$$a2128/24861
000873811 0247_ $$2altmetric$$aaltmetric:76016200
000873811 0247_ $$2pmid$$apmid:32034446
000873811 0247_ $$2WOS$$aWOS:000515952800001
000873811 037__ $$aFZJ-2020-01016
000873811 082__ $$a610
000873811 1001_ $$0P:(DE-HGF)0$$aBauer, Elena K.$$b0
000873811 245__ $$aPrediction of survival in patients with IDH-wildtype astrocytic gliomas using dynamic O-(2-[18F]-fluoroethyl)-l-tyrosine PET
000873811 260__ $$aHeidelberg [u.a.]$$bSpringer-Verl.$$c2020
000873811 3367_ $$2DRIVER$$aarticle
000873811 3367_ $$2DataCite$$aOutput Types/Journal article
000873811 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1589290792_24819
000873811 3367_ $$2BibTeX$$aARTICLE
000873811 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000873811 3367_ $$00$$2EndNote$$aJournal Article
000873811 520__ $$aPurposeIntegrated histomolecular diagnostics of gliomas according to the World Health Organization (WHO) classification of 2016 has refined diagnostic accuracy and prediction of prognosis. This study aimed at exploring the prognostic value of dynamic O-(2-[18F]-fluoroethyl)-l-tyrosine (FET) PET in newly diagnosed, histomolecularly classified astrocytic gliomas of WHO grades III or IV.MethodsBefore initiation of treatment, dynamic FET PET imaging was performed in patients with newly diagnosed glioblastoma (GBM) and anaplastic astrocytoma (AA). Static FET PET parameters such as maximum and mean tumour/brain ratios (TBRmax/mean), the metabolic tumour volume (MTV) as well as the dynamic FET PET parameters time-to-peak (TTP) and slope, were obtained. The predictive ability of FET PET parameters was evaluated concerning the progression-free and overall survival (PFS, OS). Using ROC analyses, threshold values for FET PET parameters were obtained. Subsequently, univariate Kaplan-Meier and multivariate Cox regression survival analyses were performed to assess the predictive power of these parameters for survival.ResultsSixty patients (45 GBM and 15 AA patients) of two university centres were retrospectively identified. Patients with isocitrate dehydrogenase (IDH)-mutant or O6-methylguanine-DNA-methyltransferase (MGMT) promoter-methylated tumours had a significantly longer PFS and OS (both P < 0.001). Furthermore, ROC analysis of IDH-wildtype glioma patients (n = 45) revealed that a TTP > 25 min (AUC, 0.90; sensitivity, 90%; specificity, 87%; P < 0.001) was highly prognostic for longer PFS (13 vs. 7 months; P = 0.005) and OS (29 vs. 12 months; P < 0.001). In contrast, at a lower level of significance, TBRmax, TBRmean, and MTV were only prognostic for longer OS (P = 0.004, P = 0.038, and P = 0.048, respectively). Besides complete resection and a methylated MGMT promoter, TTP remained significant in multivariate survival analysis (all P ≤ 0.02), indicating an independent predictor for OS.ConclusionsOur data suggest that dynamic FET PET allows the identification of patients with longer OS among patients with newly diagnosed IDH-wildtype GBM and AA.
000873811 536__ $$0G:(DE-HGF)POF3-572$$a572 - (Dys-)function and Plasticity (POF3-572)$$cPOF3-572$$fPOF III$$x0
000873811 588__ $$aDataset connected to CrossRef
000873811 7001_ $$0P:(DE-Juel1)131627$$aStoffels, Gabriele$$b1
000873811 7001_ $$0P:(DE-HGF)0$$aBlau, Tobias$$b2
000873811 7001_ $$0P:(DE-HGF)0$$aReifenberger, Guido$$b3
000873811 7001_ $$0P:(DE-HGF)0$$aFelsberg, Jörg$$b4
000873811 7001_ $$0P:(DE-HGF)0$$aWerner, Jan M.$$b5
000873811 7001_ $$0P:(DE-Juel1)145110$$aLohmann, Philipp$$b6
000873811 7001_ $$0P:(DE-HGF)0$$aRosen, Jurij$$b7
000873811 7001_ $$0P:(DE-HGF)0$$aCeccon, Garry$$b8
000873811 7001_ $$0P:(DE-Juel1)168559$$aTscherpel, Caroline$$b9
000873811 7001_ $$0P:(DE-HGF)0$$aRapp, Marion$$b10
000873811 7001_ $$0P:(DE-Juel1)165921$$aSabel, Michael$$b11
000873811 7001_ $$0P:(DE-Juel1)141877$$aFilss, Christian P.$$b12
000873811 7001_ $$0P:(DE-Juel1)131794$$aShah, Nadim J.$$b13
000873811 7001_ $$0P:(DE-Juel1)166419$$aNeumaier, Bernd$$b14
000873811 7001_ $$0P:(DE-Juel1)131720$$aFink, Gereon R.$$b15
000873811 7001_ $$0P:(DE-Juel1)131777$$aLangen, Karl-Josef$$b16
000873811 7001_ $$0P:(DE-Juel1)143792$$aGalldiks, Norbert$$b17$$eCorresponding author
000873811 773__ $$0PERI:(DE-600)2098375-X$$a10.1007/s00259-020-04695-0$$p1486–1495$$tEuropean journal of nuclear medicine and molecular imaging$$v47$$x1619-7089$$y2020
000873811 8564_ $$uhttps://juser.fz-juelich.de/record/873811/files/Bauer2020_Article_PredictionOfSurvivaLInPatients.pdf$$yOpenAccess
000873811 8564_ $$uhttps://juser.fz-juelich.de/record/873811/files/Bauer2020_Article_PredictionOfSurvivaLInPatients.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000873811 909CO $$ooai:juser.fz-juelich.de:873811$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000873811 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131627$$aForschungszentrum Jülich$$b1$$kFZJ
000873811 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145110$$aForschungszentrum Jülich$$b6$$kFZJ
000873811 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)141877$$aForschungszentrum Jülich$$b12$$kFZJ
000873811 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131794$$aForschungszentrum Jülich$$b13$$kFZJ
000873811 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)166419$$aForschungszentrum Jülich$$b14$$kFZJ
000873811 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131720$$aForschungszentrum Jülich$$b15$$kFZJ
000873811 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131777$$aForschungszentrum Jülich$$b16$$kFZJ
000873811 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)143792$$aForschungszentrum Jülich$$b17$$kFZJ
000873811 9131_ $$0G:(DE-HGF)POF3-572$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$v(Dys-)function and Plasticity$$x0
000873811 9141_ $$y2020
000873811 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000873811 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences
000873811 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000873811 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000873811 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bEUR J NUCL MED MOL I : 2017
000873811 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000873811 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000873811 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000873811 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000873811 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000873811 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bEUR J NUCL MED MOL I : 2017
000873811 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database
000873811 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000873811 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine
000873811 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List
000873811 920__ $$lyes
000873811 9201_ $$0I:(DE-Juel1)INM-3-20090406$$kINM-3$$lKognitive Neurowissenschaften$$x0
000873811 9201_ $$0I:(DE-Juel1)INM-4-20090406$$kINM-4$$lPhysik der Medizinischen Bildgebung$$x1
000873811 9201_ $$0I:(DE-Juel1)INM-5-20090406$$kINM-5$$lNuklearchemie$$x2
000873811 980__ $$ajournal
000873811 980__ $$aVDB
000873811 980__ $$aUNRESTRICTED
000873811 980__ $$aI:(DE-Juel1)INM-3-20090406
000873811 980__ $$aI:(DE-Juel1)INM-4-20090406
000873811 980__ $$aI:(DE-Juel1)INM-5-20090406
000873811 9801_ $$aFullTexts