guest ::
| Home > Publications database > FET PET Radiomics for Differentiating Pseudoprogression from Early Tumor Progression in Glioma Patients Post-Chemoradiation > print |
| Home > Publications database > FET PET Radiomics for Differentiating Pseudoprogression from Early Tumor Progression in Glioma Patients Post-Chemoradiation > print |
| 001 | 890229 | ||
| 005 | 20230522110533.0 | ||
| 024 | 7 | _ | |a 10.3390/cancers12123835 |2 doi |
| 024 | 7 | _ | |a 2128/27048 |2 Handle |
| 024 | 7 | _ | |a altmetric:96544102 |2 altmetric |
| 024 | 7 | _ | |a 33353180 |2 pmid |
| 024 | 7 | _ | |a WOS:000601842800001 |2 WOS |
| 037 | _ | _ | |a FZJ-2021-00816 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Lohmann, Philipp |0 P:(DE-Juel1)145110 |b 0 |e Corresponding author |
| 245 | _ | _ | |a FET PET Radiomics for Differentiating Pseudoprogression from Early Tumor Progression in Glioma Patients Post-Chemoradiation |
| 260 | _ | _ | |a Basel |c 2020 |b MDPI |
| 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 1611738394_16236 |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 Currently, a reliable diagnostic test for differentiating pseudoprogression from early tumor progression is lacking. We explored the potential of O-(2-[18F]fluoroethyl)-L-tyrosine (FET) positron emission tomography (PET) radiomics for this clinically important task. Thirty-four patients (isocitrate dehydrogenase (IDH)-wildtype glioblastoma, 94%) with progressive magnetic resonance imaging (MRI) changes according to the Response Assessment in Neuro-Oncology (RANO) criteria within the first 12 weeks after completing temozolomide chemoradiation underwent a dynamic FET PET scan. Static and dynamic FET PET parameters were calculated. For radiomics analysis, the number of datasets was increased to 102 using data augmentation. After randomly assigning patients to a training and test dataset, 944 features were calculated on unfiltered and filtered images. The number of features for model generation was limited to four to avoid data overfitting. Eighteen patients were diagnosed with early tumor progression, and 16 patients had pseudoprogression. The FET PET radiomics model correctly diagnosed pseudoprogression in all test cohort patients (sensitivity, 100%; negative predictive value, 100%). In contrast, the diagnostic performance of the best FET PET parameter (TBRmax) was lower (sensitivity, 81%; negative predictive value, 80%). The results suggest that FET PET radiomics helps diagnose patients with pseudoprogression with a high diagnostic performance. Given the clinical significance, further studies are warranted |
| 536 | _ | _ | |a 572 - (Dys-)function and Plasticity (POF3-572) |0 G:(DE-HGF)POF3-572 |c POF3-572 |x 0 |f POF III |
| 536 | _ | _ | |a DFG project 428090865 - Radiomics basierend auf MRT und Aminosäure PET in der Neuroonkologie |0 G:(GEPRIS)428090865 |c 428090865 |x 1 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 700 | 1 | _ | |a Elahmadawy, Mai A. |0 0000-0002-3536-5945 |b 1 |
| 700 | 1 | _ | |a Gutsche, Robin |0 P:(DE-Juel1)181076 |b 2 |
| 700 | 1 | _ | |a Werner, Jan-Michael |0 0000-0001-7147-4594 |b 3 |
| 700 | 1 | _ | |a Bauer, Elena K. |0 P:(DE-HGF)0 |b 4 |
| 700 | 1 | _ | |a Ceccon, Garry |0 P:(DE-HGF)0 |b 5 |
| 700 | 1 | _ | |a Kocher, Martin |0 P:(DE-Juel1)173675 |b 6 |
| 700 | 1 | _ | |a Lerche, Christoph W. |0 P:(DE-Juel1)164254 |b 7 |
| 700 | 1 | _ | |a Rapp, Marion |0 P:(DE-HGF)0 |b 8 |
| 700 | 1 | _ | |a Fink, Gereon R. |0 P:(DE-Juel1)131720 |b 9 |
| 700 | 1 | _ | |a Shah, Nadim J. |0 P:(DE-Juel1)131794 |b 10 |u fzj |
| 700 | 1 | _ | |a Langen, Karl-Josef |0 P:(DE-Juel1)131777 |b 11 |
| 700 | 1 | _ | |a Galldiks, Norbert |0 P:(DE-Juel1)143792 |b 12 |
| 773 | _ | _ | |a 10.3390/cancers12123835 |g Vol. 12, no. 12, p. 3835 - |0 PERI:(DE-600)2527080-1 |n 12 |p 3835 - |t Cancers |v 12 |y 2020 |x 2072-6694 |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/890229/files/Invoice_MDPI_cancers-1041982_1738.52EUR.pdf |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/890229/files/Lohmann_2020_Cancers_FET%20PET%20radiomics%20for%20differentiating%20....pdf |y OpenAccess |
| 909 | C | O | |o oai:juser.fz-juelich.de:890229 |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)145110 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)181076 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)173675 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 7 |6 P:(DE-Juel1)164254 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 9 |6 P:(DE-Juel1)131720 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 10 |6 P:(DE-Juel1)131794 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 11 |6 P:(DE-Juel1)131777 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 12 |6 P:(DE-Juel1)143792 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Decoding the Human Brain |1 G:(DE-HGF)POF3-570 |0 G:(DE-HGF)POF3-572 |3 G:(DE-HGF)POF3 |2 G:(DE-HGF)POF3-500 |4 G:(DE-HGF)POF |v (Dys-)function and Plasticity |x 0 |
| 914 | 1 | _ | |y 2020 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2020-09-11 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0160 |2 StatID |b Essential Science Indicators |d 2020-09-11 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |d 2020-09-11 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1190 |2 StatID |b Biological Abstracts |d 2020-09-11 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |d 2020-09-11 |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b CANCERS : 2018 |d 2020-09-11 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2020-09-11 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2020-09-11 |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0113 |2 StatID |b Science Citation Index Expanded |d 2020-09-11 |
| 915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2020-09-11 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2020-09-11 |
| 915 | _ | _ | |a Creative Commons Attribution CC BY 4.0 |0 LIC:(DE-HGF)CCBY4 |2 HGFVOC |
| 915 | _ | _ | |a OpenAccess |0 StatID:(DE-HGF)0510 |2 StatID |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |d 2020-09-11 |
| 915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2020-09-11 |
| 915 | _ | _ | |a IF >= 5 |0 StatID:(DE-HGF)9905 |2 StatID |b CANCERS : 2018 |d 2020-09-11 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2020-09-11 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0320 |2 StatID |b PubMed Central |d 2020-09-11 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2020-09-11 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-3-20090406 |k INM-3 |l Kognitive Neurowissenschaften |x 0 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-4-20090406 |k INM-4 |l Physik der Medizinischen Bildgebung |x 1 |
| 920 | 1 | _ | |0 I:(DE-Juel1)INM-11-20170113 |k INM-11 |l Jara-Institut Quantum Information |x 2 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)INM-3-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-4-20090406 |
| 980 | _ | _ | |a I:(DE-Juel1)INM-11-20170113 |
| 980 | _ | _ | |a APC |
| 980 | _ | _ | |a UNRESTRICTED |
| 980 | 1 | _ | |a APC |
| 980 | 1 | _ | |a FullTexts |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|