18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1619-7089
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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online resource.
- Bibliographic citation
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18F-FDG PET baseline radiomics features improve the prediction of treatment outcome in diffuse large B-cell lymphoma ; day:18 ; month:8 ; year:2021 ; pages:1-11
European journal of nuclear medicine and molecular imaging ; (18.8.2021), 1-11
- Creator
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Eertink, Jakoba J.
Brug, Tim van de
Wiegers, Sanne E.
Zwezerijnen, Gerben J. C.
Pfaehler, Elisabeth A. G.
Lugtenburg, Pieternella J.
Holt, Bronno van der
Vet, H. C. W. de
Hoekstra, Otto S.
Boellaard, Ronald
Zijlstra, Josée M.
- Contributor
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SpringerLink (Online service)
- DOI
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10.1007/s00259-021-05480-3
- URN
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urn:nbn:de:101:1-2021110605173192486481
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:24 AM CEST
Data provider
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Associated
- Eertink, Jakoba J.
- Brug, Tim van de
- Wiegers, Sanne E.
- Zwezerijnen, Gerben J. C.
- Pfaehler, Elisabeth A. G.
- Lugtenburg, Pieternella J.
- Holt, Bronno van der
- Vet, H. C. W. de
- Hoekstra, Otto S.
- Boellaard, Ronald
- Zijlstra, Josée M.
- SpringerLink (Online service)
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Use of population input functions for reduced scan duration whole-body Patlak 18F-FDG PET imaging
Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features
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Volume of interest delineation techniques for 18F-FDG PET-CT scans during neoadjuvant extremity soft tissue sarcoma treatment in adults: a feasibility study
EANM/EARL FDG-PET/CT accreditation - summary results from the first 200 accredited imaging systems
Comparing lesion and feature selections to predict progression in newly diagnosed DLBCL patients with FDG PET/CT radiomics features
Sensitivity of an AI method for [18F]FDG PET/CT outcome prediction of diffuse large B-cell lymphoma patients to image reconstruction protocols
Advanced analytics and artificial intelligence in gastrointestinal cancer: a systematic review of radiomics predicting response to treatment
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