000270296 001__ 270296
000270296 005__ 20240707004303.0
000270296 0247_ $$2doi$$a10.1007/s00259-024-06654-5
000270296 0247_ $$2pmid$$apmid:38396261
000270296 0247_ $$2pmc$$apmc:PMC11178656
000270296 0247_ $$2ISSN$$a1619-7070
000270296 0247_ $$2ISSN$$a1619-7089
000270296 0247_ $$2altmetric$$aaltmetric:160041495
000270296 037__ $$aDZNE-2024-00768
000270296 041__ $$aEnglish
000270296 082__ $$a610
000270296 1001_ $$00000-0002-2084-5858$$aKaiser, Lena$$b0
000270296 245__ $$aEnhancing predictability of IDH mutation status in glioma patients at initial diagnosis: a comparative analysis of radiomics from MRI, [18F]FET PET, and TSPO PET.
000270296 260__ $$aHeidelberg [u.a.]$$bSpringer-Verl.$$c2024
000270296 3367_ $$2DRIVER$$aarticle
000270296 3367_ $$2DataCite$$aOutput Types/Journal article
000270296 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1719822674_19444
000270296 3367_ $$2BibTeX$$aARTICLE
000270296 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000270296 3367_ $$00$$2EndNote$$aJournal Article
000270296 520__ $$aAccording to the World Health Organization classification for tumors of the central nervous system, mutation status of the isocitrate dehydrogenase (IDH) genes has become a major diagnostic discriminator for gliomas. Therefore, imaging-based prediction of IDH mutation status is of high interest for individual patient management. We compared and evaluated the diagnostic value of radiomics derived from dual positron emission tomography (PET) and magnetic resonance imaging (MRI) data to predict the IDH mutation status non-invasively.Eighty-seven glioma patients at initial diagnosis who underwent PET targeting the translocator protein (TSPO) using [18F]GE-180, dynamic amino acid PET using [18F]FET, and T1-/T2-weighted MRI scans were examined. In addition to calculating tumor-to-background ratio (TBR) images for all modalities, parametric images quantifying dynamic [18F]FET PET information were generated. Radiomic features were extracted from TBR and parametric images. The area under the receiver operating characteristic curve (AUC) was employed to assess the performance of logistic regression (LR) classifiers. To report robust estimates, nested cross-validation with five folds and 50 repeats was applied.TBRGE-180 features extracted from TSPO-positive volumes had the highest predictive power among TBR images (AUC 0.88, with age as co-factor 0.94). Dynamic [18F]FET PET reached a similarly high performance (0.94, with age 0.96). The highest LR coefficients in multimodal analyses included TBRGE-180 features, parameters from kinetic and early static [18F]FET PET images, age, and the features from TBRT2 images such as the kurtosis (0.97).The findings suggest that incorporating TBRGE-180 features along with kinetic information from dynamic [18F]FET PET, kurtosis from TBRT2, and age can yield very high predictability of IDH mutation status, thus potentially improving early patient management.
000270296 536__ $$0G:(DE-HGF)POF4-352$$a352 - Disease Mechanisms (POF4-352)$$cPOF4-352$$fPOF IV$$x0
000270296 588__ $$aDataset connected to CrossRef, PubMed, , Journals: pub.dzne.de
000270296 650_7 $$2Other$$aIDH mutation status
000270296 650_7 $$2Other$$aBraTS
000270296 650_7 $$2Other$$aFET PET
000270296 650_7 $$2Other$$aGlioma
000270296 650_7 $$2Other$$aRadiomics
000270296 650_7 $$2Other$$aTSPO PET
000270296 650_7 $$2NLM Chemicals$$aReceptors, GABA
000270296 650_7 $$0EC 1.1.1.41$$2NLM Chemicals$$aIsocitrate Dehydrogenase
000270296 650_7 $$2NLM Chemicals$$aTSPO protein, human
000270296 650_7 $$01326R5J1IA$$2NLM Chemicals$$a(18F)fluoroethyltyrosine
000270296 650_7 $$042HK56048U$$2NLM Chemicals$$aTyrosine
000270296 650_2 $$2MeSH$$aHumans
000270296 650_2 $$2MeSH$$aFemale
000270296 650_2 $$2MeSH$$aReceptors, GABA: genetics
000270296 650_2 $$2MeSH$$aReceptors, GABA: metabolism
000270296 650_2 $$2MeSH$$aMale
000270296 650_2 $$2MeSH$$aMiddle Aged
000270296 650_2 $$2MeSH$$aIsocitrate Dehydrogenase: genetics
000270296 650_2 $$2MeSH$$aMutation
000270296 650_2 $$2MeSH$$aPositron-Emission Tomography: methods
000270296 650_2 $$2MeSH$$aGlioma: diagnostic imaging
000270296 650_2 $$2MeSH$$aGlioma: genetics
000270296 650_2 $$2MeSH$$aMagnetic Resonance Imaging
000270296 650_2 $$2MeSH$$aAdult
000270296 650_2 $$2MeSH$$aBrain Neoplasms: diagnostic imaging
000270296 650_2 $$2MeSH$$aBrain Neoplasms: genetics
000270296 650_2 $$2MeSH$$aAged
000270296 650_2 $$2MeSH$$aTyrosine: analogs & derivatives
000270296 650_2 $$2MeSH$$aImage Processing, Computer-Assisted
000270296 650_2 $$2MeSH$$aRadiomics
000270296 7001_ $$aQuach, S.$$b1
000270296 7001_ $$aZounek, A. J.$$b2
000270296 7001_ $$aWiestler, B.$$b3
000270296 7001_ $$0P:(DE-2719)9001654$$aZatcepin, A.$$b4$$udzne
000270296 7001_ $$aHolzgreve, A.$$b5
000270296 7001_ $$aBollenbacher, A.$$b6
000270296 7001_ $$aBartos, L. M.$$b7
000270296 7001_ $$aRuf, V. C.$$b8
000270296 7001_ $$aBöning, G.$$b9
000270296 7001_ $$aThon, N.$$b10
000270296 7001_ $$0P:(DE-HGF)0$$aHerms, J.$$b11
000270296 7001_ $$aRiemenschneider, M. J.$$b12
000270296 7001_ $$aStöcklein, S.$$b13
000270296 7001_ $$0P:(DE-2719)9001539$$aBrendel, M.$$b14$$udzne
000270296 7001_ $$aRupprecht, R.$$b15
000270296 7001_ $$aTonn, J. C.$$b16
000270296 7001_ $$aBartenstein, P.$$b17
000270296 7001_ $$avon Baumgarten, L.$$b18
000270296 7001_ $$aZiegler, S.$$b19
000270296 7001_ $$aAlbert, N. L.$$b20
000270296 773__ $$0PERI:(DE-600)2098375-X$$a10.1007/s00259-024-06654-5$$gVol. 51, no. 8, p. 2371 - 2381$$n8$$p2371 - 2381$$tEuropean journal of nuclear medicine and molecular imaging$$v51$$x1619-7070$$y2024
000270296 8564_ $$uhttps://pub.dzne.de/record/270296/files/DZNE-2024-00768%20SUP.xlsx
000270296 8564_ $$uhttps://pub.dzne.de/record/270296/files/DZNE-2024-00768.pdf$$yOpenAccess
000270296 8564_ $$uhttps://pub.dzne.de/record/270296/files/DZNE-2024-00768%20SUP.csv
000270296 8564_ $$uhttps://pub.dzne.de/record/270296/files/DZNE-2024-00768%20SUP.ods
000270296 8564_ $$uhttps://pub.dzne.de/record/270296/files/DZNE-2024-00768%20SUP.xls
000270296 8564_ $$uhttps://pub.dzne.de/record/270296/files/DZNE-2024-00768.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000270296 909CO $$ooai:pub.dzne.de:270296$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000270296 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)9001654$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b4$$kDZNE
000270296 9101_ $$0I:(DE-588)1065079516$$6P:(DE-2719)9001539$$aDeutsches Zentrum für Neurodegenerative Erkrankungen$$b14$$kDZNE
000270296 9131_ $$0G:(DE-HGF)POF4-352$$1G:(DE-HGF)POF4-350$$2G:(DE-HGF)POF4-300$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bGesundheit$$lNeurodegenerative Diseases$$vDisease Mechanisms$$x0
000270296 9141_ $$y2024
000270296 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-08-23
000270296 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-08-23
000270296 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2023-08-23
000270296 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000270296 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2023-08-23
000270296 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bEUR J NUCL MED MOL I : 2022$$d2023-08-23
000270296 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-08-23
000270296 915__ $$0StatID:(DE-HGF)3002$$2StatID$$aDEAL Springer$$d2023-08-23$$wger
000270296 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-08-23
000270296 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000270296 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2023-08-23
000270296 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bEUR J NUCL MED MOL I : 2022$$d2023-08-23
000270296 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-08-23
000270296 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2023-08-23
000270296 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-08-23
000270296 9201_ $$0I:(DE-2719)1110007$$kAG Haass$$lMolecular Neurodegeneration$$x0
000270296 980__ $$ajournal
000270296 980__ $$aVDB
000270296 980__ $$aUNRESTRICTED
000270296 980__ $$aI:(DE-2719)1110007
000270296 9801_ $$aFullTexts