The Central Composite Design and Artificial Neural Network Coupled with Genetic Algorithm in Optimization and Modeling of the Radiolabeling Process of <sup>177</sup>Lu-hydroxyapatite as a Potential Radiosynovectomy Agent
The ability to generate more data with fewer experiments for optimization and improved production is a pertinent advantage of multivariate optimization methods over traditional methods in radiation-related activities. The central composite design and artificial neural network- genetic algorithm optimization approaches are successfully utilized to create prediction models and investigate the impact of critical variables in the radiolabeling of hydroxyapatite with <sup>177</sup>Lu radionuclide.
Authors
- Ali Roozbahani
- S. Attar Nosrati (ORCID: https://orcid.org/0000-0002-7112-2633)
- Maryam Salahinejad (ORCID: https://orcid.org/0000-0002-7825-5981)
- Mojtaba Amiri (ORCID: https://orcid.org/0000-0001-5885-6483)
- Mohammad Reza Aboudzadeh
Institutions
- Nuclear Science and Technology Research Institute
Publication Details
- Journal
- Current Radiopharmaceuticals
- Published
- 2025-03-04
- DOI
- https://doi.org/10.2174/0118744710336283250227020659
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00