Abstract:

Despite the success of radiopharmaceutical therapy (RPT), there is a belief that personalized treatment can further enhance treatment outcomes while maintaining low side effects. Developing personalized RPT poses greater challenges compared to external beam radiotherapy (EBRT) due to the complex dynamics of radiation dose delivery after intravenous injection of radiopharmaceuticals. Furthermore, conventional radiobiological modeling used in EBRT is not applicable to RPT due to increased dose complexity and tumor heterogeneity.

In this talk, we will explore how Digital Twin technology can support the personalization of radiopharmaceutical therapy (RPT). Digital twins integrate multi-level computational models, including anatomical models, physiologically based pharmacokinetic models, convection-reaction diffusion models, and DNA damage and repair models, to simulate radiopharmaceutical delivery, dose deposition, and dose effects in virtual patients. This enables the exploration and optimization of treatment strategies. Moreover, advanced technologies such as spatial transcriptomics, total-body PET, organ-on-chips, and artificial intelligence—including large language models—are introduced to bridge the gap between digital twins and real patients. By combining clinical, experimental, and computational approaches, we aim to advance theranostic digital twins, bringing us closer to truly personalized treatments.