Prediction of prostate biopsy outcomes at different cut-offs of prostate-specific antigen using machine learning: a multicenter study
ML models are proving to be effective tools in differentiating between prostate biopsy outcomes, enhancing diagnostic accuracy, and potentially transforming clinical practices in prostate cancer management.
Authors
- Mostafa A. Arafa (ORCID: https://orcid.org/0000-0003-3760-5380)
- Karim Farhat (ORCID: https://orcid.org/0000-0003-1871-2818)
- Danny Rabah (ORCID: https://orcid.org/0000-0002-0092-5789)
- Sherin Aly (ORCID: https://orcid.org/0000-0001-6136-0438)
- Waleed Altaweel (ORCID: https://orcid.org/0000-0003-1359-8809)
- Farrukh K. Khan
- Sultan S. Alkhateeb
- Sami Azhari
- Alaa Mokhtar
- Abdulaziz Mohammed Althunayan (ORCID: https://orcid.org/0000-0003-0468-9215)
Institutions
- Alfaisal University (SA)
- King Faisal Specialist Hospital & Research Centre (SA)
- King Saud University (SA)
- Alexandria University (EG)
Publication Details
- Journal
- Journal of the Egyptian National Cancer Institute
- Published
- 2025-03-17
- DOI
- https://doi.org/10.1186/s43046-025-00265-3
- Citations
- 1
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52