Radiomics-based machine learning models for predicting genomic alterations in metastatic prostate cancer using PSMA PET imaging: a pilot study
Clinical-radiomics ML models based on PSMA PET imaging show promising accuracy in predicting actionable genomic alterations in mPCa. These findings support further investigation into radiogenomics modelling as a complementary, non-invasive tool to inform molecular profiling and treatment stratification.
Authors
- Giovanni Pasini (ORCID: https://orcid.org/0000-0002-8750-0731)
- Alessandro Stefano (ORCID: https://orcid.org/0000-0002-7189-1731)
- Roberto Pedrero-Piedras (ORCID: https://orcid.org/0000-0003-2352-191X)
- Nora Sobrevilla Moreno
- Giorgio Russo
- Miguel A. Jimenez Rios
- Marina Arenas Hernández
- Osvaldo G. Perez
- Maria Delia Pérez Montiel
- Anna Scavuzzo
Institutions
- Istituto Nazionale di Fisica Nucleare (IT)
- Institute of Molecular Bioimaging and Physiology (IT)
- Instituto Nacional de Cancerología (MX)
- Sapienza University of Rome (IT)
Publication Details
- Journal
- EJNMMI Reports
- Published
- 2025-12-30
- DOI
- https://doi.org/10.1186/s41824-025-00280-6
- Citations
- 1
- Primary Topic
- Prostate Cancer Treatment and Research
- Type
- article
- Field-Weighted Citation Impact
- 1.52