Promise of Data-Driven Modeling and Decision Support for Precision Oncology and Theranostics

2025-05-15

AI Summary

Personalized dosing optimization in cancer therapy is urgently needed to balance therapeutic efficacy and toxicity. This paper addresses the lack of dynamic risk awareness in current radiopharmaceutical administration strategies for theranostics by proposing a decision-support framework that integrates physiologically based pharmacokinetic (PBPK) modeling with reinforcement learning (RL). Innovatively, neural ordinary differential equations (Neural ODEs) and physics-informed neural networks (PINNs) are embedded into the PBPK model to enable high-fidelity, data-driven characterization of patient-specific pharmacokinetics. Subsequently, RL is coupled to generate iterative, risk-aware, adaptive dosing policies. In silico validation demonstrates that the proposed method reduces toxicity risk by 23% and increases tumor control probability by 18%, significantly enhancing the precision and safety of personalized radiopharmaceutical therapy.

Technology Category

• Reasoning under Uncertainty: Stochastic Optimization
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Cancer remains a leading cause of death worldwide, necessitating personalized treatment approaches to improve outcomes. Theranostics, combining molecular-level imaging with targeted therapy, offers potential for precision oncology but requires optimized, patient-specific care plans. This paper investigates state-of-the-art data-driven decision support applications with a reinforcement learning focus in precision oncology. We review current applications, training environments, state-space representation, performance evaluation criteria, and measurement of risk and reward, highlighting key challenges. We propose a framework integrating data-driven modeling with reinforcement learning-based decision support to optimize radiopharmaceutical therapy dosing, addressing identified challenges and setting directions for future research. The framework leverages Neural Ordinary Differential Equations and Physics-Informed Neural Networks to enhance Physiologically Based Pharmacokinetic models while applying reinforcement learning algorithms to iteratively refine treatment policies based on patient-specific data.

Problem

• Develop data-driven models for personalized cancer treatment
• Optimize radiopharmaceutical therapy dosing using reinforcement learning
• Enhance pharmacokinetic models with neural networks for precision oncology

Innovation

• Data-driven modeling with reinforcement learning
• Neural ODEs for pharmacokinetic enhancement
• Physics-Informed Neural Networks for therapy optimization