Predicting Response to [177Lu]Lu-PSMA Therapy in mCRPC Using Machine Learning
This study emphasizes the significance of an integrated approach that merges imaging and biological data, thereby augmenting the predictive accuracy of patient outcomes in RLT with [177Lu]Lu-PSMA. In particular, including Choline PET among the imaging parameters provides unique insights into the predictive factors affecting RLT efficacy. This approach not only deepens the understanding of predictive factors but also underscores the utility of machine learning in refining the patient selection process for optimized treatment planning.
Authors
- Adel Mamou (ORCID: https://orcid.org/0000-0002-1709-9233)
- Kaiyuan Gong
- Vincent Boudousq
- S. Lhostis
- Nadine Houédé (ORCID: https://orcid.org/0000-0002-8852-5754)
- Baptiste Magnier (ORCID: https://orcid.org/0000-0003-3458-0552)
- Laurent Maı̈moun (ORCID: https://orcid.org/0000-0002-1727-1048)
- Pierre Olivier Kotzki
- Fanny Borrely
- Sébastien Le Bon
Institutions
- Inserm (FR)
- Université de Montpellier (FR)
- Centre Hospitalier Universitaire de Nîmes (FR)
- IMT Mines Alès (FR)
- Hôpital Lapeyronie (FR)
- Institut de Recherche en Cancérologie de Montpellier (FR)
- Université de Nîmes (FR)
Publication Details
- Journal
- Journal of Personalized Medicine
- Published
- 2024-10-23
- DOI
- https://doi.org/10.3390/jpm14111068
- Citations
- 2
- Primary Topic
- Prostate Cancer Treatment and Research
- Type
- article
- Field-Weighted Citation Impact
- 0.84