000908194 001__ 908194
000908194 005__ 20220621190116.0
000908194 0247_ $$2doi$$a10.1093/neuonc/nox168.652
000908194 0247_ $$2ISSN$$a1522-8517
000908194 0247_ $$2ISSN$$a1523-5866
000908194 037__ $$aFZJ-2022-02447
000908194 082__ $$a610
000908194 1001_ $$0P:(DE-Juel1)145110$$aLohmann, Philipp$$b0$$ufzj
000908194 245__ $$aNIMG-82. PREDICTING ISOCITRATE DEHYDROGENASE GENOTYPE IN GLIOMAS USING FET PET RADIOMICS
000908194 260__ $$c2017
000908194 3367_ $$2DataCite$$aText
000908194 3367_ $$0PUB:(DE-HGF)4$$2PUB:(DE-HGF)$$aCommunication$$bcomm$$mcomm$$s1655807717_15481
000908194 3367_ $$2BibTeX$$aMISC
000908194 3367_ $$2ORCID$$aOTHER
000908194 3367_ $$2DINI$$aOther
000908194 3367_ $$04$$2EndNote$$aPersonal Communication
000908194 520__ $$aAbstractBACKGROUNDWe investigated the potential of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) PET textural features compared with static and dynamic FET PET parameters for preoperative differentiation of IDH-mutated (mut) from IDH-wild type (wt) gliomas.METHODSEighty-four glioma patients underwent dynamic FET PET imaging prior to histological confirmation on a stand-alone PET scanner (56 patients; 31 GBM-wt, 3 GBM-mut, 10 AA-wt, 7 AA-mut, 2 AII-mut, 3 ODGII-mut) or a high-resolution hybrid PET/MR scanner (28 patients; 15 GBM-wt, 2 GBM-mut, 1 AA-wt, 7 AA-mut, 1 ODGIII-mut, 1 AII-wt, 1 AII-mut). The IDH genotype was assessed by immunohistochemistry or direct sequencing (if immunohistochemistry was negative). Maximum and mean tumor-to-brain ratios (TBRmax/mean) of FET uptake were determined and time-activity curves of FET uptake were used to evaluate the dynamic PET parameters time-to-peak (TTP) and slope (slope of linear regression line evaluated 20-50 min post-injection). Additionally, 39 textural parameters were calculated using the software LifeX. The diagnostic accuracy for IDH genotype prediction by FET PET was evaluated using ROC analyses using neuropathological results of IDH analysis as reference. In order to further increase the diagnostic accuracy, parameters were combined using linear logistic regression. Data of each scanner type were analyzed separately.RESULTSIndependent of scanner type, diagnostic accuracies of slope, TBRmean and TBRmax were similar (range, 75-80%). Ten textural features showed an accuracy ranging from 71-79% independent of scanner type. For both PET scanners the combined analysis increased the diagnostic accuracy (84% and 93%, respectively).CONCLUSIONSThe combination of static and dynamic FET PET parameters with radiomics derived from textural feature analysis leads to a high diagnostic accuracy to predict IDH genotype of cerebral gliomas.
000908194 536__ $$0G:(DE-HGF)POF4-5253$$a5253 - Neuroimaging (POF4-525)$$cPOF4-525$$fPOF IV$$x0
000908194 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000908194 7001_ $$0P:(DE-Juel1)164254$$aLerche, Christoph$$b1$$ufzj
000908194 7001_ $$0P:(DE-HGF)0$$aBauer, Elena$$b2
000908194 7001_ $$0P:(DE-HGF)0$$aSteger, Jan$$b3
000908194 7001_ $$0P:(DE-Juel1)131627$$aStoffels, Gabriele$$b4$$ufzj
000908194 7001_ $$0P:(DE-HGF)0$$aBlau, Tobias$$b5
000908194 7001_ $$0P:(DE-Juel1)156211$$aDunkl, Veronika$$b6
000908194 7001_ $$0P:(DE-Juel1)141877$$aFilss, Christian P$$b7$$ufzj
000908194 7001_ $$0P:(DE-Juel1)156479$$aStegmayr, Carina$$b8$$ufzj
000908194 7001_ $$0P:(DE-Juel1)166419$$aNeumaier, Bernd$$b9$$ufzj
000908194 7001_ $$0P:(DE-Juel1)131794$$aShah, Nadim J$$b10$$ufzj
000908194 7001_ $$0P:(DE-Juel1)131720$$aFink, Gereon$$b11$$ufzj
000908194 7001_ $$0P:(DE-Juel1)131777$$aLangen, Karl-Josef$$b12$$ufzj
000908194 7001_ $$0P:(DE-Juel1)143792$$aGalldiks, Norbert$$b13$$ufzj
000908194 773__ $$0PERI:(DE-600)2094060-9$$a10.1093/neuonc/nox168.652$$gVol. 19, no. suppl_6, p. vi160 - vi160$$x1523-5866$$y2017
000908194 909CO $$ooai:juser.fz-juelich.de:908194$$pVDB
000908194 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145110$$aForschungszentrum Jülich$$b0$$kFZJ
000908194 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)164254$$aForschungszentrum Jülich$$b1$$kFZJ
000908194 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131627$$aForschungszentrum Jülich$$b4$$kFZJ
000908194 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)141877$$aForschungszentrum Jülich$$b7$$kFZJ
000908194 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)156479$$aForschungszentrum Jülich$$b8$$kFZJ
000908194 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)166419$$aForschungszentrum Jülich$$b9$$kFZJ
000908194 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131794$$aForschungszentrum Jülich$$b10$$kFZJ
000908194 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131720$$aForschungszentrum Jülich$$b11$$kFZJ
000908194 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131777$$aForschungszentrum Jülich$$b12$$kFZJ
000908194 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)143792$$aForschungszentrum Jülich$$b13$$kFZJ
000908194 9131_ $$0G:(DE-HGF)POF4-525$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5253$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vDecoding Brain Organization and Dysfunction$$x0
000908194 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz$$d2021-02-03$$wger
000908194 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2021-02-03
000908194 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2021-02-03
000908194 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2021-02-03
000908194 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2021-02-03
000908194 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-02-03
000908194 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2021-02-03
000908194 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-02-03
000908194 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2021-02-03
000908194 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bNEURO-ONCOLOGY : 2019$$d2021-02-03
000908194 915__ $$0StatID:(DE-HGF)9910$$2StatID$$aIF >= 10$$bNEURO-ONCOLOGY : 2019$$d2021-02-03
000908194 9201_ $$0I:(DE-Juel1)INM-4-20090406$$kINM-4$$lPhysik der Medizinischen Bildgebung$$x0
000908194 9201_ $$0I:(DE-Juel1)INM-11-20170113$$kINM-11$$lJara-Institut Quantum Information$$x1
000908194 9201_ $$0I:(DE-Juel1)VDB1046$$kJARA-BRAIN$$lJülich-Aachen Research Alliance - Translational Brain Medicine$$x2
000908194 9201_ $$0I:(DE-Juel1)INM-3-20090406$$kINM-3$$lKognitive Neurowissenschaften$$x3
000908194 980__ $$acomm
000908194 980__ $$aVDB
000908194 980__ $$aI:(DE-Juel1)INM-4-20090406
000908194 980__ $$aI:(DE-Juel1)INM-11-20170113
000908194 980__ $$aI:(DE-Juel1)VDB1046
000908194 980__ $$aI:(DE-Juel1)INM-3-20090406
000908194 980__ $$aUNRESTRICTED