The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors: Results from phase 3 of the RAISE project

Pavel ME, Dromain C, Ronot M, Schaefer N, Mandair D, Gueguen D, Elvira D, Balazard F, Dehaene O, Schutte K (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

Journal of Neuroendocrinology Wiley-Blackwell

Publisher: WILEY

City/Town: HOBOKEN

Pages Range: 126-126

Conference Proceedings Title: JOURNAL OF NEUROENDOCRINOLOGY

Authors with CRIS profile

Marianne Ellen Pavel Professur für Endokrinologie

Involved external institutions

Ipsen Pharma FR France (FR) Owkin FR France (FR) Lausanne University Hospital / Centre hospitalier universitaire vaudois (CHUV) CH Switzerland (CH) Royal Free Hospital GB United Kingdom (GB) Beaujon Hospital / Hôpital Beaujon FR France (FR)

How to cite

APA:

Pavel, M.E., Dromain, C., Ronot, M., Schaefer, N., Mandair, D., Gueguen, D.,... Schutte, K. (2021). The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors: Results from phase 3 of the RAISE project. In JOURNAL OF NEUROENDOCRINOLOGY (pp. 126-126). HOBOKEN: WILEY.

MLA:

Pavel, Marianne Ellen, et al. "The use of deep learning models to predict progression-free survival in patients with neuroendocrine tumors: Results from phase 3 of the RAISE project." Proceedings of the 18th Annual ENETS Conference for the Diagnosis and Treatment of Neuroendocrine Tumor Disease HOBOKEN: WILEY, 2021. 126-126.

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