001     910206
005     20231027114346.0
024 7 _ |a 10.1093/neuonc/noac229
|2 doi
024 7 _ |a 1522-8517
|2 ISSN
024 7 _ |a 1523-5866
|2 ISSN
024 7 _ |a 2128/34417
|2 Handle
024 7 _ |a 36215231
|2 pmid
024 7 _ |a WOS:000881636600001
|2 WOS
037 _ _ |a FZJ-2022-03683
082 _ _ |a 610
100 1 _ |a Wollring, Michael M
|0 P:(DE-Juel1)190394
|b 0
245 _ _ |a Prediction of response to lomustine-based chemotherapy in glioma patients at recurrence using MRI and FET PET
260 _ _ |a Oxford
|c 2023
|b Oxford Univ. Press
336 7 _ |a article
|2 DRIVER
336 7 _ |a Output Types/Journal article
|2 DataCite
336 7 _ |a Journal Article
|b journal
|m journal
|0 PUB:(DE-HGF)16
|s 1684222545_30060
|2 PUB:(DE-HGF)
336 7 _ |a ARTICLE
|2 BibTeX
336 7 _ |a JOURNAL_ARTICLE
|2 ORCID
336 7 _ |a Journal Article
|0 0
|2 EndNote
520 _ _ |a BackgroundWe evaluated O-(2-[18F]fluoroethyl)-l-tyrosine (FET) PET and MRI for early response assessment in recurrent glioma patients treated with lomustine-based chemotherapy.MethodsThirty-six adult patients with WHO CNS grade 3 or 4 gliomas (glioblastoma, 69%) at recurrence (median number of recurrences, 1; range, 1–3) were retrospectively identified. Besides MRI, serial FET PET scans were performed at baseline and early after chemotherapy initiation (not later than two cycles). Tumor-to-brain ratios (TBR), metabolic tumor volumes (MTV), the occurrence of new distant hotspots with a mean TBR >1.6 at follow-up, and the dynamic parameter time-to-peak were derived from all FET PET scans. PET parameter thresholds were defined using ROC analyses to predict PFS of ≥6 months and OS of ≥12 months. MRI response assessment was based on RANO criteria. The predictive values of FET PET parameters and RANO criteria were subsequently evaluated using univariate and multivariate survival estimates.ResultsAfter treatment initiation, the median follow-up time was 11 months (range, 3–71 months). Relative changes of TBR, MTV, and RANO criteria predicted a significantly longer PFS (all P ≤ .002) and OS (all P ≤ .045). At follow-up, the occurrence of new distant hotspots (n ≥ 1) predicted a worse outcome, with significantly shorter PFS (P = .005) and OS (P < .001). Time-to-peak changes did not predict a significantly longer survival. Multivariate survival analyses revealed that new distant hotspots at follow-up FET PET were most potent in predicting non-response (P < .001; HR, 8.578).ConclusionsData suggest that FET PET provides complementary information to RANO criteria for response evaluation of lomustine-based chemotherapy early after treatment initiation.
536 _ _ |a 5253 - Neuroimaging (POF4-525)
|0 G:(DE-HGF)POF4-5253
|c POF4-525
|f POF IV
|x 0
536 _ _ |a 5251 - Multilevel Brain Organization and Variability (POF4-525)
|0 G:(DE-HGF)POF4-5251
|c POF4-525
|f POF IV
|x 1
536 _ _ |a 5252 - Brain Dysfunction and Plasticity (POF4-525)
|0 G:(DE-HGF)POF4-5252
|c POF4-525
|f POF IV
|x 2
588 _ _ |a Dataset connected to CrossRef, Journals: juser.fz-juelich.de
700 1 _ |a Werner, Jan-Michael
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Bauer, Elena K
|0 P:(DE-HGF)0
|b 2
700 1 _ |a Tscherpel, Caroline
|0 P:(DE-Juel1)171739
|b 3
700 1 _ |a Ceccon, Garry S
|0 P:(DE-HGF)0
|b 4
700 1 _ |a Lohmann, Philipp
|0 P:(DE-Juel1)145110
|b 5
700 1 _ |a Stoffels, Gabriele
|0 P:(DE-Juel1)131627
|b 6
700 1 _ |a Langen, Karl-Josef
|0 P:(DE-Juel1)131777
|b 7
700 1 _ |a Kabbasch, Christoph
|0 P:(DE-HGF)0
|b 8
700 1 _ |a Goldbrunner, Roland
|0 P:(DE-HGF)0
|b 9
700 1 _ |a Fink, Gereon R
|0 P:(DE-Juel1)131720
|b 10
700 1 _ |a Galldiks, Norbert
|0 P:(DE-Juel1)143792
|b 11
|e Corresponding author
773 _ _ |a 10.1093/neuonc/noac229
|g p. noac229
|0 PERI:(DE-600)2094060-9
|n 5
|p 984–994
|t Neuro-Oncology
|v 25
|y 2023
|x 1522-8517
856 4 _ |u https://juser.fz-juelich.de/record/910206/files/Invoice_E15736808.pdf
856 4 _ |y Published on 2022-10-10. Available in OpenAccess from 2023-10-10.
|u https://juser.fz-juelich.de/record/910206/files/noac229_postprint.pdf
909 C O |o oai:juser.fz-juelich.de:910206
|p openaire
|p open_access
|p OpenAPC
|p driver
|p VDB
|p openCost
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)190394
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)145110
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 6
|6 P:(DE-Juel1)131627
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 7
|6 P:(DE-Juel1)131777
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 10
|6 P:(DE-Juel1)131720
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 11
|6 P:(DE-Juel1)143792
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5253
|x 0
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5251
|x 1
913 1 _ |a DE-HGF
|b Key Technologies
|l Natural, Artificial and Cognitive Information Processing
|1 G:(DE-HGF)POF4-520
|0 G:(DE-HGF)POF4-525
|3 G:(DE-HGF)POF4
|2 G:(DE-HGF)POF4-500
|4 G:(DE-HGF)POF
|v Decoding Brain Organization and Dysfunction
|9 G:(DE-HGF)POF4-5252
|x 2
914 1 _ |y 2023
915 p c |a Local Funding
|0 PC:(DE-HGF)0001
|2 APC
915 _ _ |a Embargoed OpenAccess
|0 StatID:(DE-HGF)0530
|2 StatID
915 _ _ |a WoS
|0 StatID:(DE-HGF)0113
|2 StatID
|b Science Citation Index Expanded
|d 2022-11-08
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2022-11-08
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2023-10-24
|w ger
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b NEURO-ONCOLOGY : 2022
|d 2023-10-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-10-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-10-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2023-10-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-10-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-10-24
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1110
|2 StatID
|b Current Contents - Clinical Medicine
|d 2023-10-24
915 _ _ |a IF >= 15
|0 StatID:(DE-HGF)9915
|2 StatID
|b NEURO-ONCOLOGY : 2022
|d 2023-10-24
920 _ _ |l yes
920 1 _ |0 I:(DE-Juel1)INM-4-20090406
|k INM-4
|l Physik der Medizinischen Bildgebung
|x 0
920 1 _ |0 I:(DE-Juel1)INM-3-20090406
|k INM-3
|l Kognitive Neurowissenschaften
|x 1
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a UNRESTRICTED
980 _ _ |a I:(DE-Juel1)INM-4-20090406
980 _ _ |a I:(DE-Juel1)INM-3-20090406
980 _ _ |a APC
980 1 _ |a APC
980 1 _ |a FullTexts


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21