Towards Personalized Cancer Therapy: A Safe and Explainable Digital Twin-Driven Meta-Reinforcement Learning

Abstract

The goal of precision oncology is to use patient-specific data to personalize cancer treatment; however, current AI-based systems have poor interpretability, little personalization, and no safety constraints. The Digital Twin–Driven Safe and Explainable Meta-Deep Reinforcement Learning (DT-SMDRL) framework for adaptive therapy optimization is proposed in this paper. A transformer-based fusion model is used to integrate multimodal patient data, such as multi-omics, clinical records, imaging, and physiological signals, into a single representation. In a risk-free setting, a patient-specific digital twin mimics the course of the illness and the results of treatment. Through knowledge transfer between patient populations, a meta-deep reinforcement learning agent based on proximal policy optimization facilitates quick personalization. The framework uses risk-sensitive learning and safety-constrained optimization with toxicity limits to guarantee clinical reliability. Treatment reasoning is improved by causal modelling, and decisions are made with confidence thanks to uncertainty estimation. Interpretability at the biomarker level is provided by an explainable AI module. Continuous learning from actual results is made possible by a closed-loop feedback mechanism. The framework is appropriate for safe and adaptive precision oncology because experimental results show increased treatment efficacy, decreased toxicity risk, quicker adaptation, and improved interpretability.