Home > Publications database > Artificial Intelligence, Radiomics, and Deep Learning in Neuro-Oncology > print

001     890309
005     20240507205533.0
024 7 _ |a 10.1093/noajnl/vdaa179
|2 doi
024 7 _ |a 2128/27085
|2 Handle
024 7 _ |a altmetric:99211382
|2 altmetric
024 7 _ |a pmid:33521635
|2 pmid
024 7 _ |a WOS:000897684800001
|2 WOS
037 _ _ |a FZJ-2021-00882
082 _ _ |a 610
100 1 _ |a Galldiks, Norbert
|0 P:(DE-Juel1)143792
|b 0
|e Corresponding author
|u fzj
245 _ _ |a Artificial Intelligence, Radiomics, and Deep Learning in Neuro-Oncology
260 _ _ |a Oxford
|c 2021
|b Oxford University 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 1715085652_30445
|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 Besides the histomolecular evaluation of tissue samples obtained from resection or biopsy, neuroimaging forms the basis for the diagnosis of brain cancer. Contrast-enhanced MRI is the method of choice for brain tumor diagnostics, treatment planning, and follow-up. Currently, advanced MRI techniques as well as amino acid PET are increasingly applied, generating a large variety of imaging parameters for brain tumor diagnostics. This is also driven by the increasing availability of hybrid PET/CT and PET/MRI scanners. Evaluation of the complex, multiparametric imaging data can be achieved by methods from the emerging field of artificial intelligence, potentially supporting physicians in clinical routine. For example, time-consuming steps such as manual detection and segmentation of lesions can be performed fully automatically. Since computer-aided image analysis is independent of the experience level of the evaluating physician, the results are more standardized and improve the inter-institutional comparability.
536 _ _ |a 5252 - Brain Dysfunction and Plasticity (POF4-525)
|0 G:(DE-HGF)POF4-5252
|c POF4-525
|f POF IV
|x 0
536 _ _ |a 5253 - Neuroimaging (POF4-525)
|0 G:(DE-HGF)POF4-5253
|c POF4-525
|f POF IV
|x 1
536 _ _ |a DFG project 428090865 - Radiomics basierend auf MRT und Aminosäure PET in der Neuroonkologie (428090865)
|0 G:(GEPRIS)428090865
|c 428090865
|x 2
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Zadeh, Gelareh
|0 P:(DE-HGF)0
|b 1
700 1 _ |a Lohmann, Philipp
|0 P:(DE-Juel1)145110
|b 2
|u fzj
773 _ _ |a 10.1093/noajnl/vdaa179
|g Vol. 2, no. Supplement_4, p. iv1 - iv2
|0 PERI:(DE-600)3009682-0
|n Supplement_4
|p iv1 - iv2
|t Neuro-oncology advances
|v 2
|y 2021
|x 2632-2498
856 4 _ |u https://juser.fz-juelich.de/record/890309/files/vdaa179.pdf
|y OpenAccess
909 C O |o oai:juser.fz-juelich.de:890309
|p openaire
|p open_access
|p VDB
|p driver
|p dnbdelivery
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 0
|6 P:(DE-Juel1)143792
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)145110
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 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)POF4-5253
|x 1
914 1 _ |y 2021
915 _ _ |a Creative Commons Attribution-NonCommercial CC BY-NC 4.0
|0 LIC:(DE-HGF)CCBYNC4
|2 HGFVOC
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2020-09-05
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2020-09-05
915 _ _ |a OpenAccess
|0 StatID:(DE-HGF)0510
|2 StatID
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Peer review
|d 2020-09-05
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2020-09-05
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2020-09-05
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2020-09-05
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2020-09-05
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b NEURO-ONCOL ADV : 2022
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0320
|2 StatID
|b PubMed Central
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0501
|2 StatID
|b DOAJ Seal
|d 2022-11-23T11:49:35Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0500
|2 StatID
|b DOAJ
|d 2022-11-23T11:49:35Z
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b DOAJ : Anonymous peer review
|d 2022-11-23T11:49:35Z
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2023-10-27
915 _ _ |a WoS
|0 StatID:(DE-HGF)0112
|2 StatID
|b Emerging Sources Citation Index
|d 2023-10-27
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2023-10-27
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2023-10-27
915 _ _ |a Article Processing Charges
|0 StatID:(DE-HGF)0561
|2 StatID
|d 2023-10-27
915 _ _ |a Fees
|0 StatID:(DE-HGF)0700
|2 StatID
|d 2023-10-27
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 I:(DE-Juel1)INM-4-20090406
980 _ _ |a I:(DE-Juel1)INM-3-20090406
980 _ _ |a UNRESTRICTED
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21