Predicting progression-free survival in hormone-receptor positive (HR+/HER2–) metastatic breast cancer (MBC) treated with CDK4/6 inhibitors: A machine learning approach
This study supports the clinical applicability ML models using baseline clinicopathological variables for individualized risk stratification in HR+/HER2- MBC.
Authors
- Noemi Maliziola
- Letizia Pontolillo (ORCID: https://orcid.org/0009-0006-3363-8512)
- Luca Mastrantoni (ORCID: https://orcid.org/0000-0003-1853-4927)
- Sergio Pannunzio
- Manuela Sgambato
- A. Palazzo
- Giovanna Garufi
- A. Orlandi
- Alessandra Emiliani (ORCID: https://orcid.org/0000-0001-8193-7599)
- Elena Di Monte
- Giampaolo Tortora
- Anna Cardillo
- Emilio Bria
Institutions
- Università Cattolica del Sacro Cuore (IT)
- Agostino Gemelli University Polyclinic (IT)
Publication Details
- Journal
- The Breast
- Published
- 2026-01-30
- DOI
- https://doi.org/10.1016/j.breast.2026.104715
- Primary Topic
- Advanced Breast Cancer Therapies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Amgen
- Eli Lilly and Company
- Pfizer
- Celgene
- Helsinn
- Ministero della Salute
- Associazione Italiana per la Ricerca sul Cancro
- Università Cattolica del Sacro Cuore
- Daiichi Sankyo Europe