Mathematical and Computational Nuclear Oncology: Toward Optimized Radiopharmaceutical Therapy via Digital Twins

Area of Science:

• Nuclear Medicine
• Computational Biology
• Oncology

Background:

• Personalized radiopharmaceutical therapies (RPTs) offer targeted cancer treatment.
• Clinical decision-making in RPTs requires advanced predictive tools.
• Computational modeling is crucial for optimizing RPT efficacy.

Purpose of the Study:

• To present the framework for theranostic digital twins (TDTs) in nuclear medicine.
• To explore TDT applications for improving cancer patient prognosis.
• To propose a roadmap for TDT implementation in clinical settings.

Main Methods:

• Conceptual overview of TDTs in computational nuclear medicine.
• Review of mathematical and computational modeling for RPTs.
• Identification of challenges and strategies in TDT development.

Main Results:

• A general framework for TDTs is established.
• Potential clinical applications of TDTs are outlined.
• Key challenges and solutions for RPT modeling are discussed.

Conclusions:

• TDTs hold significant promise for personalized cancer therapy.
• Successful implementation requires a strategic roadmap.
• Advancements in computational modeling are essential for TDTs.