Treatment-aware deep learning enables counterfactual prediction of individual benefit from PARP inhibitors in ovarian cancer
- DOI
- 10.1038/s41746-026-02822-z
- Published
- 2026-06-20
- Container
- npj Digital Medicine
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41746-026-02822-z,
title = {Treatment-aware deep learning enables counterfactual prediction of individual benefit from PARP inhibitors in ovarian cancer},
author = {Jean-Sébastien Frenel and Pierre-Etienne Heudel and Emmanuelle Guinaudeau and Céline Bossard and Carmela Pisano and Giuseppe Caruso and Joseph Rynkiewicz and Sanae Salhi and Yahia Salhi and Jérôme Chetritt and Eric Pujade-Lauraine and Isabelle Ray-Coquard},
year = {2026},
journal = {npj Digital Medicine},
doi = {10.1038/s41746-026-02822-z},
url = {https://doi.org/10.1038/s41746-026-02822-z}
}RIS
TY - JOUR TI - Treatment-aware deep learning enables counterfactual prediction of individual benefit from PARP inhibitors in ovarian cancer AU - Jean-Sébastien Frenel AU - Pierre-Etienne Heudel AU - Emmanuelle Guinaudeau AU - Céline Bossard AU - Carmela Pisano AU - Giuseppe Caruso AU - Joseph Rynkiewicz AU - Sanae Salhi AU - Yahia Salhi AU - Jérôme Chetritt AU - Eric Pujade-Lauraine AU - Isabelle Ray-Coquard PY - 2026 JO - npj Digital Medicine DO - 10.1038/s41746-026-02822-z UR - https://doi.org/10.1038/s41746-026-02822-z ER -
APA
Frenel, J., Heudel, P., Guinaudeau, E., Bossard, C., Pisano, C., Caruso, G., Rynkiewicz, J., Salhi, S., Salhi, Y., Chetritt, J., Pujade-Lauraine, E., & Ray-Coquard, I. (2026). Treatment-aware deep learning enables counterfactual prediction of individual benefit from PARP inhibitors in ovarian cancer. npj Digital Medicine. https://doi.org/10.1038/s41746-026-02822-z
Source records
- crossref · retrieved 2026-09-26T00:19:46.827Z