Machine learning prediction of germline <i>BRCA1/2</i> pathogenic variants in patients with ovarian cancer
Results support the selection of relevant clinical features for predictive purposes, which could have significant implications for the clinical management of patients with OC.
Authors
- PierAndrea de Iaco
- Lea Godino (ORCID: https://orcid.org/0000-0002-3763-9372)
- Anna Myriam Perrone (ORCID: https://orcid.org/0000-0003-3140-4772)
- Sara Miccoli (ORCID: https://orcid.org/0000-0003-3930-6122)
- P. Rucci
- G Erini
- S. Ferrari
- Antonio De Leo
- Luca Caramanna
- Daniela Turchetti
- Claudio Zamagni
- Giovanni Innella
Institutions
- Azienda USL di Bologna (IT)
- University of Bologna (IT)
Publication Details
- Journal
- BMJ Health & Care Informatics
- Published
- 2025-12-01
- DOI
- https://doi.org/10.1136/bmjhci-2025-101751
- Primary Topic
- BRCA gene mutations in cancer
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Ministero della Salute