Towards Precision Oncology: A New Predictive Machine Learning Model for Early Progression to Castration Resistant Prostate Cancer
This novel RINH model offers a robust tool for risk stratification in mHSPC patients, capable of personalizing therapeutic strategies. However, external validation in multi-center, prospective cohorts is an essential next step before its consideration as a clinical decision support tool.
Authors
- Rubén Campanario‐Pérez (ORCID: https://orcid.org/0000-0002-0025-564X)
- Miguel Ángel Gómez‐Luque (ORCID: https://orcid.org/0000-0001-6659-3353)
- P. Rodríguez-Marcos (ORCID: https://orcid.org/0000-0002-5304-0829)
- A. Medina-González (ORCID: https://orcid.org/0009-0001-4496-0594)
- Alejandro Cárdenas‐Fernández (ORCID: https://orcid.org/0009-0008-0269-6829)
- Walter Orlandi‐Oliveira (ORCID: https://orcid.org/0009-0000-4182-7918)
- Manuel Ruibal‐Moldes
Institutions
- Hospital San Juan de Dios (VE)
- Complejo Hospitalario de Pontevedra (ES)
- Andalusian Agency for Healthcare Quality (ES)
- Universidad de Sevilla (ES)
Publication Details
- Journal
- The Prostate
- Published
- 2025-11-04
- DOI
- https://doi.org/10.1002/pros.70088
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00