Home > Publications database > Radiomics in neuro-oncology: Basics, workflow, and applications > print

001     877539
005     20230522110531.0
024 7 _ |a 10.1016/j.ymeth.2020.06.003
|2 doi
024 7 _ |a 1046-2023
|2 ISSN
024 7 _ |a 1095-9130
|2 ISSN
024 7 _ |a 2128/27387
|2 Handle
024 7 _ |a altmetric:83953364
|2 altmetric
024 7 _ |a 32522530
|2 pmid
024 7 _ |a WOS:000631883800011
|2 WOS
037 _ _ |a FZJ-2020-02275
082 _ _ |a 540
100 1 _ |a Lohmann, Philipp
|0 P:(DE-Juel1)145110
|b 0
|e Corresponding author
|u fzj
245 _ _ |a Radiomics in neuro-oncology: Basics, workflow, and applications
260 _ _ |a Orlando, Fla.
|c 2021
|b Academic 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 1646035135_1933
|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 Over the last years, the amount, variety, and complexity of neuroimaging data acquired in patients with brain tumors for routine clinical purposes and the resulting number of imaging parameters have substantially increased. Consequently, a timely and cost-effective evaluation of imaging data is hardly feasible without the support of methods from the field of artificial intelligence (AI). AI can facilitate and shorten various time-consuming steps in the image processing workflow, e.g., tumor segmentation, thereby optimizing productivity. Besides, the automated and computer-based analysis of imaging data may help to increase data comparability as it is independent of the experience level of the evaluating clinician. Importantly, AI offers the potential to extract new features from the routinely acquired neuroimages of brain tumor patients. In combination with patient data such as survival, molecular markers, or genomics, mathematical models can be generated that allow, for example, the prediction of treatment response or prognosis, as well as the noninvasive assessment of molecular markers. The subdiscipline of AI dealing with the computation, identification, and extraction of image features, as well as the generation of prognostic or predictive mathematical models, is termed radiomics. This review article summarizes the basics, the current workflow, and methods used in radiomics with a focus on feature-based radiomics in neuro-oncology and provides selected examples of its clinical application.
536 _ _ |a 5253 - Neuroimaging (POF4-525)
|0 G:(DE-HGF)POF4-5253
|c POF4-525
|x 0
|f POF IV
536 _ _ |a 5252 - Brain Dysfunction and Plasticity (POF4-525)
|0 G:(DE-HGF)POF4-5252
|c POF4-525
|x 1
|f POF IV
536 _ _ |a DFG project 428090865 - Radiomics basierend auf MRT und Aminosäure PET in der Neuroonkologie
|0 G:(GEPRIS)428090865
|c 428090865
|x 2
588 _ _ |a Dataset connected to CrossRef
700 1 _ |a Galldiks, Norbert
|0 P:(DE-Juel1)143792
|b 1
|u fzj
700 1 _ |a Kocher, Martin
|0 P:(DE-Juel1)173675
|b 2
|u fzj
700 1 _ |a Heinzel, Alexander
|0 P:(DE-Juel1)132315
|b 3
|u fzj
700 1 _ |a Filss, Christian P.
|0 P:(DE-Juel1)141877
|b 4
|u fzj
700 1 _ |a Stegmayr, Carina
|0 P:(DE-Juel1)156479
|b 5
|u fzj
700 1 _ |a Mottaghy, Felix M.
|0 P:(DE-Juel1)132318
|b 6
|u fzj
700 1 _ |a Fink, Gereon R.
|0 P:(DE-Juel1)131720
|b 7
|u fzj
700 1 _ |a Shah, N. J.
|0 P:(DE-Juel1)131794
|b 8
|u fzj
700 1 _ |a Langen, Karl-Josef
|0 P:(DE-Juel1)131777
|b 9
|u fzj
773 _ _ |a 10.1016/j.ymeth.2020.06.003
|g p. S1046202319303172
|0 PERI:(DE-600)1471152-7
|p 112-121
|t Methods
|v 188
|y 2021
|x 1046-2023
856 4 _ |u https://juser.fz-juelich.de/record/877539/files/Lohmann_2020_Post%20Print_Methods_Radiomics%20in%20Neuro_Oncology_Basics%2C%20Workflow%2C%20and%20Applications.pdf
|y Published on 2020-06-06. Available in OpenAccess from 2021-06-06.
856 4 _ |u https://juser.fz-juelich.de/record/877539/files/Lohmann_2020_Post%20Print_Methods_Radiomics%20in%20Neuro_Oncology_Basics%2C%20Workflow%2C%20and%20Applications.pdf?subformat=pdfa
|x pdfa
|y Published on 2020-06-06. Available in OpenAccess from 2021-06-06.
909 C O |o oai:juser.fz-juelich.de:877539
|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)145110
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 1
|6 P:(DE-Juel1)143792
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 2
|6 P:(DE-Juel1)173675
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 3
|6 P:(DE-Juel1)132315
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 4
|6 P:(DE-Juel1)141877
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 5
|6 P:(DE-Juel1)156479
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 6
|6 P:(DE-Juel1)132318
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 7
|6 P:(DE-Juel1)131720
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 8
|6 P:(DE-Juel1)131794
910 1 _ |a Forschungszentrum Jülich
|0 I:(DE-588b)5008462-8
|k FZJ
|b 9
|6 P:(DE-Juel1)131777
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)POF4-5252
|x 1
913 0 _ |a DE-HGF
|b Key Technologies
|l Decoding the Human Brain
|1 G:(DE-HGF)POF3-570
|0 G:(DE-HGF)POF3-573
|3 G:(DE-HGF)POF3
|2 G:(DE-HGF)POF3-500
|4 G:(DE-HGF)POF
|v Neuroimaging
|x 0
914 1 _ |y 2021
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0200
|2 StatID
|b SCOPUS
|d 2020-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0160
|2 StatID
|b Essential Science Indicators
|d 2020-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1030
|2 StatID
|b Current Contents - Life Sciences
|d 2020-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1190
|2 StatID
|b Biological Abstracts
|d 2020-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0600
|2 StatID
|b Ebsco Academic Search
|d 2020-01-02
915 _ _ |a Creative Commons Attribution-NonCommercial-NoDerivs CC BY-NC-ND 4.0
|0 LIC:(DE-HGF)CCBYNCND4
|2 HGFVOC
915 _ _ |a Embargoed OpenAccess
|0 StatID:(DE-HGF)0530
|2 StatID
915 _ _ |a JCR
|0 StatID:(DE-HGF)0100
|2 StatID
|b METHODS : 2018
|d 2020-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0150
|2 StatID
|b Web of Science Core Collection
|d 2020-01-02
915 _ _ |a WoS
|0 StatID:(DE-HGF)0110
|2 StatID
|b Science Citation Index
|d 2020-01-02
915 _ _ |a WoS
|0 StatID:(DE-HGF)0111
|2 StatID
|b Science Citation Index Expanded
|d 2020-01-02
915 _ _ |a IF < 5
|0 StatID:(DE-HGF)9900
|2 StatID
|d 2020-01-02
915 _ _ |a Peer Review
|0 StatID:(DE-HGF)0030
|2 StatID
|b ASC
|d 2020-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0310
|2 StatID
|b NCBI Molecular Biology Database
|d 2020-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)1050
|2 StatID
|b BIOSIS Previews
|d 2020-01-02
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0300
|2 StatID
|b Medline
|d 2020-01-02
915 _ _ |a Nationallizenz
|0 StatID:(DE-HGF)0420
|2 StatID
|d 2020-01-02
|w ger
915 _ _ |a DBCoverage
|0 StatID:(DE-HGF)0199
|2 StatID
|b Clarivate Analytics Master Journal List
|d 2020-01-02
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-11-20170113
|k INM-11
|l Jara-Institut Quantum Information
|x 1
920 1 _ |0 I:(DE-82)080010_20140620
|k JARA-BRAIN
|l JARA-BRAIN
|x 2
920 1 _ |0 I:(DE-Juel1)INM-3-20090406
|k INM-3
|l Kognitive Neurowissenschaften
|x 3
980 _ _ |a journal
980 _ _ |a VDB
980 _ _ |a I:(DE-Juel1)INM-4-20090406
980 _ _ |a I:(DE-Juel1)INM-11-20170113
980 _ _ |a I:(DE-82)080010_20140620
980 _ _ |a I:(DE-Juel1)INM-3-20090406
980 _ _ |a UNRESTRICTED
980 1 _ |a FullTexts

LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21