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dc.contributor.authorPapanikolaou, Nen_US
dc.contributor.authorMatos, Cen_US
dc.contributor.authorKoh, DMen_US
dc.date.accessioned2020-06-22T10:08:43Z
dc.date.issued2020-05
dc.identifier.citationCancer imaging : the official publication of the International Cancer Imaging Society, 2020, 20 (1), pp. 33 - ?
dc.identifier.issn1740-5025
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/3761
dc.identifier.eissn1470-7330
dc.identifier.doi10.1186/s40644-020-00311-4
dc.description.abstractDuring the last decade, there is an increasing usage of quantitative methods in Radiology in an effort to reduce the diagnostic variability associated with a subjective manner of radiological interpretation. Combined approaches where visual assessment made by the radiologist is augmented by quantitative imaging biomarkers are gaining attention. Advances in machine learning resulted in the rise of radiomics that is a new methodology referring to the extraction of quantitative information from medical images. Radiomics are based on the development of computational models, referred to as "Radiomic Signatures", trying to address either unmet clinical needs, mostly in the field of oncologic imaging, or to compare radiomics performance with that of radiologists. However, to explore this new technology, initial publications did not consider best practices in the field of machine learning resulting in publications with questionable clinical value. In this paper, our effort was concentrated on how to avoid methodological mistakes and consider critical issues in the workflow of the development of clinically meaningful radiomic signatures.
dc.formatElectronic
dc.format.extent33 - ?
dc.languageeng
dc.language.isoeng
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.subjectHumans
dc.subjectNeoplasms
dc.subjectImage Interpretation, Computer-Assisted
dc.subjectImage Processing, Computer-Assisted
dc.subjectPractice Guidelines as Topic
dc.subjectMachine Learning
dc.titleHow to develop a meaningful radiomic signature for clinical use in oncologic patients.
dc.typeJournal Article
dcterms.dateAccepted2020-04-15
rioxxterms.versionofrecord10.1186/s40644-020-00311-4
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0
rioxxterms.licenseref.startdate2020-05
rioxxterms.typeJournal Article/Review
dc.relation.isPartOfCancer imaging : the official publication of the International Cancer Imaging Society
pubs.issue1
pubs.notesNo embargo
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/Royal Marsden Clinical Units
pubs.publication-statusPublished
pubs.volume20
pubs.embargo.termsNo embargo
dc.contributor.icrauthorKoh, Dow-Muen


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