Transforming [ <sup>177</sup> Lu]Lu‐PSMA‐617 treatment planning: Machine learning‐based radiodosiomics and swin UNETR using pretherapy PSMA positron emission tomography/computed tomography (PET/CT)
Integrating ML-based radiodosiomics and transformer-based DL enables accurate, efficient lesion AD and DRM prediction from pretherapy PET/CT, supporting personalized Lu-PSMA RLT planning.
Authors
- Elmira Yazdani (ORCID: https://orcid.org/0000-0001-8371-8832)
- Mahdi Sadeghi (ORCID: https://orcid.org/0000-0001-5055-4544)
- Najme Karamzade‐Ziarati (ORCID: https://orcid.org/0000-0002-0432-6193)
- Parham Geramifar (ORCID: https://orcid.org/0000-0002-7607-6859)
- Saeed Reza Kheradpisheh (ORCID: https://orcid.org/0000-0001-6168-4379)
- Atefeh Mahmoudi
- Habibeh Vosoughi
- Mahboobeh Asadi (ORCID: https://orcid.org/0000-0003-3451-6833)
- Farshad Emami (ORCID: https://orcid.org/0000-0001-7634-5759)
- Aryan Neizehbaz
Institutions
- Iran University of Medical Sciences (IR)
- Imam Reza International University (IR)
- Shahid Beheshti University (IR)
- Tehran University of Medical Sciences (IR)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-09-23
- DOI
- https://doi.org/10.1002/mp.70030
- Citations
- 5
- Primary Topic
- Prostate Cancer Treatment and Research
- Type
- article
- Field-Weighted Citation Impact
- 7.59