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Future Oncology Volume 19, 2023 - Issue 32
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Short Communication

Response heterogeneity as a new biomarker of treatment response in patients with neuroendocrine tumors

Clarisse Dromain1 Lausanne University Hospital,Lausanne,SwitzerlandCorrespondence[email protected]
ORCID Iconhttps://orcid.org/0000-0002-2999-1958View further author information
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Marianne Pavel2 Department of Medicine 1,Friedrich-Alexander-University of Erlangen-Nürnberg,Erlangen,GermanyORCID Iconhttps://orcid.org/0000-0002-9669-5294View further author information
ORCID Icon,
Maxime Ronot3 Beaujon Hospital,Clichy,FranceView further author information
,
Niklaus Schaefer1 Lausanne University Hospital,Lausanne,SwitzerlandORCID Iconhttps://orcid.org/0000-0002-9732-7696View further author information
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Dalvinder Mandair4 Royal Free Hospital,London,UKORCID Iconhttps://orcid.org/0000-0002-5237-8641View further author information
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Delphine Gueguen5 Ipsen,Boulogne-Billancourt,FranceView further author information
,
Catherine Cheng5 Ipsen,Boulogne-Billancourt,FranceView further author information
,
Olivier Dehaene6 Owkin,Paris,FranceView further author information
,
Kathryn Schutte6 Owkin,Paris,FranceORCID Iconhttps://orcid.org/0000-0002-5161-6927View further author information
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David Cahané6 Owkin,Paris,FranceView further author information
,
Simon Jégou6 Owkin,Paris,FranceView further author information
&
Félix Balazard6 Owkin,Paris,FranceView further author information
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Pages 2171-2183 | Received 15 Nov 2022, Accepted 13 Jun 2023, Published online: 27 Jul 2023

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Marianne Pavel, Clarisse Dromain, Maxime Ronot, Niklaus Schaefer, Dalvinder Mandair, Delphine Gueguen, David Elvira, Simon Jégou, Félix Balazard, Olivier Dehaene & Kathryn Schutte. (2023) The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors. Future Oncology 19:32, pages 2185-2199.
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