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

Together, our results indicate that it might be possible to generate a predictor of benefit from PARP inhibitors based on the DL-mediated analysis of WSIs. However, further studies with larger cohorts and further methodological improvements will be necessary to generate a predictor with clinically useful accuracy across independent patient cohorts.

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Publication Details

Journal
European Journal of Cancer
Published
2024-12-27
DOI
https://doi.org/10.1016/j.ejca.2024.115199
Citations
5
Primary Topic
PARP inhibition in cancer therapy
Type
article
Field-Weighted Citation Impact
1.41
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article

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

Timo Gaiser, Stefan Fröhling, Frederik Marmé, Tobias Brodkorb et al.
5 citations
European Journal of Cancer
PARP inhibition in cancer therapy
1.41
article

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

Timo Gaiser, Stefan Fröhling, Frederik Marmé, Tobias Brodkorb, Florian Heitz, C. Zamagni, Philip C. Schouten, Mohamad Kayali, Katharina Prieske, Antonio González‐Martín, Lennard Kiehl, Christoph Wies, Jan Hauke, Éric Pujade-Lauraine, Isabelle Laure Ray-Coquard, Isabelle Treilleux, Titus Josef Brinker, Eva Krieghoff‐Henning, Philipp Harter, Eric Hahnen, Stefan Kommoss
article en
5 citations

Abstract

Together, our results indicate that it might be possible to generate a predictor of benefit from PARP inhibitors based on the DL-mediated analysis of WSIs. However, further studies with larger cohorts and further methodological improvements will be necessary to generate a predictor with clinically useful accuracy across independent patient cohorts.

European Journal of CancerVol. 216
Universität Hamburg (DE), German Cancer Research Center (DE), Heidelberg University (DE), University Hospital Heidelberg (DE), Cambridge University Hospitals NHS Foundation Trust (GB), Integrated Oncology (United States) (US), Centre Léon Bérard (FR), University Medical Center Hamburg-Eppendorf (DE), National Center for Tumor Diseases (DE), Azienda USL di Bologna (IT), University Children's Hospital Tübingen (DE), Arcagy Gineco (FR), Clinica Universidad de Navarra (ES), Kliniken Essen-Mitte (DE), University Hospital Cologne (DE), University Medical Centre Mannheim (DE)
Openalex Percentile: Top 15%
PARP inhibition in cancer therapy
1.41
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