The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors.

Pavel, Marianne; Dromain, Clarisse; Ronot, Maxime; Schaefer, Niklaus; Mandair, Dalvinder; Gueguen, Delphine; Elvira, David; Jégou, Simon; Balazard, Félix; Dehaene, Olivier; Schutte, Kathryn

Pavel, Marianne; Dromain, Clarisse; Ronot, Maxime; Schaefer, Niklaus; Mandair, Dalvinder; Gueguen, Delphine; Elvira, David; Jégou, Simon; Balazard, Félix; Dehaene, Olivier; Schutte, Kathryn.

Affiliation

Pavel M; Department of Medicine 1, Friedrich-Alexander-University of Erlangen-Nürnberg, Erlangen, Germany.
Dromain C; Lausanne University Hospital, Lausanne, Switzerland.
Ronot M; Beaujon Hospital, Clichy, France.
Schaefer N; Lausanne University Hospital, Lausanne, Switzerland.
Mandair D; Royal Free Hospital, London, UK.
Gueguen D; Ipsen, Boulogne-Billancourt, France.
Elvira D; Ipsen, Boulogne-Billancourt, France.
Jégou S; Owkin, Paris, France.
Balazard F; Owkin, Paris, France.
Dehaene O; Owkin, Paris, France.
Schutte K; Owkin, Paris, France.

Future Oncol ; 19(32): 2185-2199, 2023 Oct.

Article de En | MEDLINE | ID: mdl-37497644

Sujet(s)
Mots clés

RECIST; artificial intelligence; deep learning; neuroendocrine tumors; progression-free survival

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Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Sujet principal: Tumeurs neuroendocrines / Apprentissage profond Type d'étude: Prognostic_studies / Risk_factors_studies Limites: Humans Langue: En Journal: Future Oncol Année: 2023 Type de document: Article Pays d'affiliation: Allemagne Pays de publication: Royaume-Uni

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