Predicting benefit from PARP inhibitors using deep learning on H&E-stained ovarian cancer slides

Marme, Frederik; Krieghoff-Henning, Eva I.; Kiehl, Lennard; Wies, Christoph; Hauke, Jan; Hahnen, Eric; Harter, Philipp; Schouten, Philip C.; Brodkorb, Tobias; Kayali, Mohamad; Heitz, Florian; Zamagni, Claudio; Gonzalez-Martin, Antonio; Treilleux, Isabelle; Kommoss, Stefan; Prieske, Katharina; Gaiser, Timo; Froehling, Stefan; Ray-Coquard, Isabelle; Pujade-Lauraine, Eric; Brinker, Titus J.

Predicting benefit from PARP inhibitors using deep learning on H&E-stained ovarian cancer slides

Autor(en):

Marme, Frederik; Krieghoff-Henning, Eva I.; Kiehl, Lennard; Wies, Christoph; Hauke, Jan; Hahnen, Eric; Harter, Philipp; Schouten, Philip C.; Brodkorb, Tobias; Kayali, Mohamad; Heitz, Florian; Zamagni, Claudio; Gonzalez-Martin, Antonio; Treilleux, Isabelle; Kommoss, Stefan; Prieske, Katharina; Gaiser, Timo; Froehling, Stefan; Ray-Coquard, Isabelle; Pujade-Lauraine, Eric; Brinker, Titus J.

Tübinger Autor(en):

Kommoss, Stefan

Erschienen in:

European Journal of Cancer (2025), Bd. 216, Article 115199

Verlagsangabe:

London : Elsevier Sci Ltd

Sprache:

Englisch

Referenz zum Volltext:

http://dx.doi.org/10.1016/j.ejca.2024.115199

ISSN:

0959-8049