Multi-branch CNNFormer: a novel framework for predicting prostate cancer response to hormonal therapy
The findings emphasize the framework's potential in enhancing personalized PC treatment planning and monitoring. By combining the strengths of CNN and ViT, the proposed approach offers robust, accurate prediction of PC response to hormonal therapy, with implications for improving clinical decision-making.
Authors
- Fahmi Khalifa (ORCID: https://orcid.org/0000-0003-3318-2851)
- Eric Van Bogaert
- Mohammed Ghazal (ORCID: https://orcid.org/0000-0002-9045-6698)
- Ibrahim Abdelhalim (ORCID: https://orcid.org/0009-0000-1544-7276)
- Scott R. Silva (ORCID: https://orcid.org/0000-0002-6028-3685)
- Sohail G. Contractor (ORCID: https://orcid.org/0000-0002-4809-8890)
- Ayman El‐Baz (ORCID: https://orcid.org/0000-0001-7264-1323)
- Mohamed Abou El‐Ghar (ORCID: https://orcid.org/0000-0003-2713-4394)
- Mohamed Badawy (ORCID: https://orcid.org/0000-0002-0145-7868)
- Dibson Gondim (ORCID: https://orcid.org/0000-0003-0604-8403)
Institutions
- Abu Dhabi University (AE)
- University of Louisville (US)
- Mansoura University (EG)
- Morgan State University (US)
Publication Details
- Journal
- BioMedical Engineering OnLine
- Published
- 2024-12-23
- DOI
- https://doi.org/10.1186/s12938-024-01325-w
- Citations
- 5
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 2.09
Funders
- United States Agency for International Development
- Science and Technology Development Fund