dc.contributor.author
Penzkofer, Tobias
dc.contributor.author
Padhani, Anwar R.
dc.contributor.author
Turkbey, Baris
dc.contributor.author
Haider, Masoom A.
dc.contributor.author
Huisman, Henkjan
dc.contributor.author
Walz, Jochen
dc.contributor.author
Salomon, Georg
dc.contributor.author
Schoots, Ivo G.
dc.contributor.author
Richenberg, Jonathan
dc.contributor.author
Villeirs, Geert
dc.contributor.author
Panebianco, Valeria
dc.contributor.author
Rouviere, Olivier
dc.contributor.author
Logager, Vibeke Berg
dc.contributor.author
Barentsz, Jelle
dc.date.accessioned
2023-07-10T15:17:35Z
dc.date.available
2023-07-10T15:17:35Z
dc.identifier.uri
https://refubium.fu-berlin.de/handle/fub188/40041
dc.identifier.uri
http://dx.doi.org/10.17169/refubium-39763
dc.description.abstract
Artificial intelligence developments are essential to the successful deployment of community-wide, MRI-driven prostate cancer diagnosis. AI systems should ensure that the main benefits of biopsy avoidance are delivered while maintaining consistent high specificities, at a range of disease prevalences. Since all current artificial intelligence / computer-aided detection systems for prostate cancer detection are experimental, multiple developmental efforts are still needed to bring the vision to fruition. Initial work needs to focus on developing systems as diagnostic supporting aids so their results can be integrated into the radiologists' workflow including gland and target outlining tasks for fusion biopsies. Developing AI systems as clinical decision-making tools will require greater efforts. The latter encompass larger multicentric, multivendor datasets where the different needs of patients stratified by diagnostic settings, disease prevalence, patient preference, and clinical setting are considered. AI-based, robust, standard operating procedures will increase the confidence of patients and payers, thus enabling the wider adoption of the MRI-directed approach for prostate cancer diagnosis.
en
dc.rights.uri
https://creativecommons.org/licenses/by/4.0/
dc.subject
Artificial intelligence
en
dc.subject
Deep learning
en
dc.subject
Prostate cancer
en
dc.subject
Multiparametric magnetic resonance imaging
en
dc.subject
Image-guided biopsy
en
dc.subject.ddc
600 Technik, Medizin, angewandte Wissenschaften::610 Medizin und Gesundheit::610 Medizin und Gesundheit
dc.title
ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging
dc.type
Wissenschaftlicher Artikel
dcterms.bibliographicCitation.doi
10.1007/s00330-021-08021-6
dcterms.bibliographicCitation.journaltitle
European Radiology
dcterms.bibliographicCitation.number
12
dcterms.bibliographicCitation.originalpublishername
Springer Nature
dcterms.bibliographicCitation.pagestart
9567
dcterms.bibliographicCitation.pageend
9578
dcterms.bibliographicCitation.volume
31
refubium.affiliation
Charité - Universitätsmedizin Berlin
refubium.funding
Springer Nature DEAL
refubium.resourceType.isindependentpub
no
dcterms.accessRights.openaire
open access
dcterms.bibliographicCitation.pmid
33991226
dcterms.isPartOf.issn
0938-7994
dcterms.isPartOf.eissn
1432-1084