Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study
Machine learning algorithms can enhance patient selection for gene expression profiling, though further external validation is needed.
Authors
- Johan Hartman (ORCID: https://orcid.org/0000-0002-6500-8527)
- Maria Ekholm (ORCID: https://orcid.org/0000-0002-5553-5323)
- Balázs Ács (ORCID: https://orcid.org/0000-0002-0972-4633)
- Nikos Tsiknakis (ORCID: https://orcid.org/0000-0002-7581-467X)
- Alexios Matikas (ORCID: https://orcid.org/0000-0002-4122-9624)
- Erik Olsson (ORCID: https://orcid.org/0000-0002-1591-7407)
- Una Kjällquist (ORCID: https://orcid.org/0000-0002-8123-9261)
- Theodoros Foukakis (ORCID: https://orcid.org/0000-0001-8952-9987)
- Henrik Lindman (ORCID: https://orcid.org/0000-0002-5871-268X)
- Antonios Valachis (ORCID: https://orcid.org/0000-0001-6059-0194)
- Christine Lundgren (ORCID: https://orcid.org/0000-0002-7880-2981)
- S Lévy
- Luisa Edman Kessler
- Sara Margolin (ORCID: https://orcid.org/0000-0002-5526-5570)
Institutions
- Uppsala University (SE)
- Karolinska University Hospital (SE)
- Örebro University (SE)
- Karolinska Institutet (SE)
- Stockholm South General Hospital (SE)
- Saint Göran Hospital (SE)
- Ryhov Hospital Jönköping (SE)
- Linköping University Hospital (SE)
Publication Details
- Journal
- The Breast
- Published
- 2025-05-07
- DOI
- https://doi.org/10.1016/j.breast.2025.104489
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Eli Lilly and Company
- Pfizer
- AstraZeneca
- Roche
- Gilead Sciences
- Veracyte
- Seagen
- Cancerfonden
- Svenska Sällskapet för Medicinsk Forskning
- Vetenskapsrådet
- Sveriges Läkarförbund
- Svenska Läkaresällskapet