Personalized Bone Tumor Treatment Sequencing Using Counterfactual Causal Reinforcement Learning

The sequencing of treatment for patients with bone tumors is an acute issue because these tumors develop heterogeneously, their therapies interact in multiple ways, and the current AI-based clinical decision support systems do not understand the investigation of these tumors. Majority of the existing methods are based on correlation-based predictive models that do not reflect the actual effect of alternative treatment plans in the long-run. The proposed work develops a Counterfactual Causal Reinforcement Learning (CCRL) model framework to optimize bone tumor treatment in a personalized treatment. The model combines Structural Causal Models (SCM) to learn causal graphs, a counterfactual simulation engine to judge the hypothetical effects of treatment, and a deep reinforcement learning policy network to make an optimal decision. The training and validation are performed using multi-modal longitudinal datasets that include CT/MRI imaging, radiomic features, and clinical records. The experimental analysis shows that the proposed CCRL model can reach an accuracy of 98.94% in treatment response, a 21.3% enhance in decision optimality, and a 17.6% decrease in recurrence risk than traditional deep learning and RL-based baselines. The framework offers interpretable, evidence-based treatment suggestions that will enhance precision oncology by incorporating causal AI.