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

TL;DR

This paper introduces theranostic digital twins (TDTs) in computational nuclear medicine to enhance personalized radiopharmaceutical therapies and improve cancer treatment outcomes through advanced modeling and clinical decision support.

Contribution

It presents a comprehensive framework for TDTs, discusses clinical applications, and outlines a roadmap for integrating mathematical and computational models into personalized cancer therapy.

Findings

Proposes a general framework for TDTs in nuclear medicine

Highlights key challenges and strategies in modeling RPTs

Provides a roadmap for clinical implementation of digital twins

Abstract

This article presents the general framework of theranostic digital twins (TDTs) in computational nuclear medicine, designed to support clinical decision-making and improve cancer patient prognosis through personalized radiopharmaceutical therapies (RPTs). It outlines potential clinical applications of TDTs and proposes a roadmap for successful implementation. Additionally, the chapter provides a conceptual overview of the current state of the art in the mathematical and computational modeling of RPTs, highlighting key challenges and the strategies being pursued to address them.