001010425 001__ 1010425
001010425 005__ 20240408211952.0
001010425 0247_ $$2pmid$$apmid:37562802
001010425 0247_ $$2ISSN$$a0097-9058
001010425 0247_ $$2ISSN$$a0022-3123
001010425 0247_ $$2ISSN$$a0161-5505
001010425 0247_ $$2ISSN$$a1535-5667
001010425 0247_ $$2ISSN$$a2159-662X
001010425 0247_ $$2doi$$a10.2967/jnumed.123.265725
001010425 037__ $$aFZJ-2023-03049
001010425 041__ $$aEnglish
001010425 082__ $$a610
001010425 1001_ $$0P:(DE-Juel1)181076$$aGutsche, Robin$$b0
001010425 245__ $$aAutomated Brain Tumor Detection and Segmentation for Treatment Response Assessment Using Amino Acid PET.
001010425 260__ $$aNew York, NY$$bSoc.$$c2023
001010425 3367_ $$2DRIVER$$aarticle
001010425 3367_ $$2DataCite$$aOutput Types/Journal article
001010425 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1712566408_21290
001010425 3367_ $$2BibTeX$$aARTICLE
001010425 3367_ $$2ORCID$$aJOURNAL_ARTICLE
001010425 3367_ $$00$$2EndNote$$aJournal Article
001010425 520__ $$aEvaluation of metabolic tumor volume (MTV) changes using amino acid PET has become an important tool for response assessment in brain tumor patients. MTV is usually determined by manual or semiautomatic delineation, which is laborious and may be prone to intra- and interobserver variability. The goal of our study was to develop a method for automated MTV segmentation and to evaluate its performance for response assessment in patients with gliomas. Methods: In total, 699 amino acid PET scans using the tracer O-(2-[18F]fluoroethyl)-l-tyrosine (18F-FET) from 555 brain tumor patients at initial diagnosis or during follow-up were retrospectively evaluated (mainly glioma patients, 76%). 18F-FET PET MTVs were segmented semiautomatically by experienced readers. An artificial neural network (no new U-Net) was configured on 476 scans from 399 patients, and the network performance was evaluated on a test dataset including 223 scans from 156 patients. Surface and volumetric Dice similarity coefficients (DSCs) were used to evaluate segmentation quality. Finally, the network was applied to a recently published 18F-FET PET study on response assessment in glioblastoma patients treated with adjuvant temozolomide chemotherapy for a fully automated response assessment in comparison to an experienced physician. Results: In the test dataset, 92% of lesions with increased uptake (n = 189) and 85% of lesions with iso- or hypometabolic uptake (n = 33) were correctly identified (F1 score, 92%). Single lesions with a contiguous uptake had the highest DSC, followed by lesions with heterogeneous, noncontiguous uptake and multifocal lesions (surface DSC: 0.96, 0.93, and 0.81 respectively; volume DSC: 0.83, 0.77, and 0.67, respectively). Change in MTV, as detected by the automated segmentation, was a significant determinant of disease-free and overall survival, in agreement with the physician's assessment. Conclusion: Our deep learning-based 18F-FET PET segmentation allows reliable, robust, and fully automated evaluation of MTV in brain tumor patients and demonstrates clinical value for automated response assessment.
001010425 536__ $$0G:(DE-HGF)POF4-5253$$a5253 - Neuroimaging (POF4-525)$$cPOF4-525$$fPOF IV$$x0
001010425 536__ $$0G:(GEPRIS)428090865$$aDFG project 428090865 - Radiomics basierend auf MRT und Aminosäure PET in der Neuroonkologie (428090865)$$c428090865$$x1
001010425 536__ $$0G:(GEPRIS)491111487$$aDFG project 491111487 - Open-Access-Publikationskosten / 2022 - 2024 / Forschungszentrum Jülich (OAPKFZJ) (491111487)$$c491111487$$x2
001010425 588__ $$aDataset connected to DataCite, PubMed, , Journals: juser.fz-juelich.de
001010425 650_7 $$2Other$$aAI
001010425 650_7 $$2Other$$aFET PET
001010425 650_7 $$2Other$$aartificial intelligence
001010425 650_7 $$2Other$$amachine learning
001010425 650_7 $$2Other$$aneurooncology
001010425 650_7 $$2Other$$avolumetry
001010425 7001_ $$0P:(DE-HGF)0$$aLowis, Carsten$$b1
001010425 7001_ $$0P:(DE-HGF)0$$aZiemons, Karl$$b2
001010425 7001_ $$0P:(DE-Juel1)173675$$aKocher, Martin$$b3
001010425 7001_ $$0P:(DE-HGF)0$$aCeccon, Garry$$b4
001010425 7001_ $$0P:(DE-HGF)0$$aBrambilla, Cláudia Régio$$b5
001010425 7001_ $$0P:(DE-Juel1)131794$$aShah, Nadim J$$b6
001010425 7001_ $$0P:(DE-Juel1)131777$$aLangen, Karl-Josef$$b7
001010425 7001_ $$0P:(DE-Juel1)143792$$aGalldiks, Norbert$$b8
001010425 7001_ $$0P:(DE-HGF)0$$aIsensee, Fabian$$b9
001010425 7001_ $$0P:(DE-Juel1)145110$$aLohmann, Philipp$$b10$$eCorresponding author
001010425 773__ $$0PERI:(DE-600)2040222-3$$a10.2967/jnumed.123.265725$$gVol. 64, no. 10, p. 1594 - 1602$$n10$$p-$$tJournal of nuclear medicine$$v64$$x0097-9058$$y2023
001010425 8564_ $$uhttps://juser.fz-juelich.de/record/1010425/files/Invoice_JNUMED_2023_265725.pdf
001010425 8564_ $$uhttps://juser.fz-juelich.de/record/1010425/files/06_2023_Gutsche_FET_PET_Segmentation.pdf$$yRestricted
001010425 8564_ $$uhttps://juser.fz-juelich.de/record/1010425/files/Invoice_JNUMED_2023_265725.gif?subformat=icon$$xicon
001010425 8564_ $$uhttps://juser.fz-juelich.de/record/1010425/files/Invoice_JNUMED_2023_265725.jpg?subformat=icon-1440$$xicon-1440
001010425 8564_ $$uhttps://juser.fz-juelich.de/record/1010425/files/Invoice_JNUMED_2023_265725.jpg?subformat=icon-180$$xicon-180
001010425 8564_ $$uhttps://juser.fz-juelich.de/record/1010425/files/Invoice_JNUMED_2023_265725.jpg?subformat=icon-640$$xicon-640
001010425 8564_ $$uhttps://juser.fz-juelich.de/record/1010425/files/JNUMED-2023-265725v2-Lohmann.pdf$$yRestricted
001010425 8767_ $$8JNUMED/2023/265725$$92023-06-01$$a1200193584$$d2023-06-12$$ePublication charges$$jZahlung erfolgt$$zFZJ-2023-02225; USD 450,-
001010425 909CO $$ooai:juser.fz-juelich.de:1010425$$popenCost$$pVDB$$pOpenAPC
001010425 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)181076$$aForschungszentrum Jülich$$b0$$kFZJ
001010425 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)173675$$aForschungszentrum Jülich$$b3$$kFZJ
001010425 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-HGF)0$$aForschungszentrum Jülich$$b5$$kFZJ
001010425 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a INM-4$$b5
001010425 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131794$$aForschungszentrum Jülich$$b6$$kFZJ
001010425 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)131777$$aForschungszentrum Jülich$$b7$$kFZJ
001010425 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)143792$$aForschungszentrum Jülich$$b8$$kFZJ
001010425 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)145110$$aForschungszentrum Jülich$$b10$$kFZJ
001010425 9131_ $$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
001010425 9141_ $$y2023
001010425 915pc $$0PC:(DE-HGF)0000$$2APC$$aAPC keys set
001010425 915pc $$0PC:(DE-HGF)0001$$2APC$$aLocal Funding
001010425 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2022-11-19
001010425 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2022-11-19
001010425 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2022-11-19
001010425 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2023-08-31
001010425 915__ $$0StatID:(DE-HGF)1190$$2StatID$$aDBCoverage$$bBiological Abstracts$$d2023-08-31
001010425 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2023-08-31
001010425 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bJ NUCL MED : 2022$$d2023-10-24
001010425 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-10-24
001010425 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-10-24
001010425 915__ $$0StatID:(DE-HGF)0320$$2StatID$$aDBCoverage$$bPubMed Central$$d2023-10-24
001010425 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-10-24
001010425 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews$$d2023-10-24
001010425 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-10-24
001010425 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences$$d2023-10-24
001010425 915__ $$0StatID:(DE-HGF)1110$$2StatID$$aDBCoverage$$bCurrent Contents - Clinical Medicine$$d2023-10-24
001010425 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bJ NUCL MED : 2022$$d2023-10-24
001010425 9201_ $$0I:(DE-Juel1)INM-4-20090406$$kINM-4$$lPhysik der Medizinischen Bildgebung$$x0
001010425 9201_ $$0I:(DE-Juel1)INM-11-20170113$$kINM-11$$lJara-Institut Quantum Information$$x1
001010425 9201_ $$0I:(DE-Juel1)INM-3-20090406$$kINM-3$$lKognitive Neurowissenschaften$$x2
001010425 9201_ $$0I:(DE-82)080010_20140620$$kJARA-BRAIN$$lJARA-BRAIN$$x3
001010425 980__ $$ajournal
001010425 980__ $$aVDB
001010425 980__ $$aI:(DE-Juel1)INM-4-20090406
001010425 980__ $$aI:(DE-Juel1)INM-11-20170113
001010425 980__ $$aI:(DE-Juel1)INM-3-20090406
001010425 980__ $$aI:(DE-82)080010_20140620
001010425 980__ $$aAPC
001010425 980__ $$aUNRESTRICTED
001010425 9801_ $$aAPC